formulating a research question in healthcare

University of Tasmania, Australia

Systematic reviews for health: 1. formulate the research question.

• Handbooks / Guidelines for Systematic Reviews
• Standards for Reporting
• Registering a Protocol
• Tools for Systematic Review
• Online Tutorials & Courses
• Books and Articles about Systematic Reviews
• Finding Systematic Reviews
• Critical Appraisal
• Library Help
• Bibliographic Databases
• Grey Literature
• Handsearching
• Citation Searching
• 1. Formulate the Research Question
• 2. Identify the Key Concepts
• 3. Develop Search Terms - Free-Text
• 4. Develop Search Terms - Controlled Vocabulary
• 5. Search Fields
• 6. Phrase Searching, Wildcards and Proximity Operators
• 7. Boolean Operators
• 8. Search Limits
• 9. Pilot Search Strategy & Monitor Its Development
• 10. Final Search Strategy
• 11. Adapt Search Syntax
• Documenting Search Strategies
• Handling Results & Storing Papers

Step 1. Formulate the Research Question

A systematic review is based on a pre-defined specific research question ( Cochrane Handbook, 1.1 ). The first step in a systematic review is to determine its focus - you should clearly frame the question(s) the review seeks to answer  ( Cochrane Handbook, 2.1 ). It may take you a while to develop a good review question - it is an important step in your review.  Well-formulated questions will guide many aspects of the review process, including determining eligibility criteria, searching for studies, collecting data from included studies, and presenting findings ( Cochrane Handbook, 2.1 ).

The research question should be clear and focused - not too vague, too specific or too broad.

You may like to consider some of the techniques mentioned below to help you with this process. They can be useful but are not necessary for a good search strategy.

PICO - to search for quantitative review questions

Richardson, WS, Wilson, MC, Nishikawa, J & Hayward, RS 1995, 'The well-built clinical question: A key to evidence-based decisions', ACP Journal Club , vol. 123, no. 3, pp. A12-A12 .

We do not have access to this article at UTAS.

A variant of PICO is PICOS . S stands for Study designs . It establishes which study designs are appropriate for answering the question, e.g. randomised controlled trial (RCT). There is also PICO C (C for context) and PICO T (T for timeframe).

You may find this document on PICO / PIO / PEO useful:

• Framing a PICO / PIO / PEO question Developed by Teesside University

SPIDER - to search for qualitative and mixed methods research studies

Cooke, A, Smith, D & Booth, A 2012, 'Beyond pico the spider tool for qualitative evidence synthesis', Qualitative Health Research , vol. 22, no. 10, pp. 1435-1443.

This article is only accessible for UTAS staff and students.

SPICE - to search for qualitative evidence

Cleyle, S & Booth, A 2006, 'Clear and present questions: Formulating questions for evidence based practice', Library hi tech , vol. 24, no. 3, pp. 355-368.

ECLIPSE - to search for health policy/management information

Wildridge, V & Bell, L 2002, 'How clip became eclipse: A mnemonic to assist in searching for health policy/management information', Health Information & Libraries Journal , vol. 19, no. 2, pp. 113-115.

There are many more techniques available. See the below guide from the CQUniversity Library for an extensive list:

• Question frameworks overview from Framing your research question guide, developed by CQUniversity Library

This is the specific research question used in the example:

"Is animal-assisted therapy more effective than music therapy in managing aggressive behaviour in elderly people with dementia?"

Within this question are the four PICO concepts :

S - Study design

This is a therapy question. The best study design to answer a therapy question is a randomised controlled trial (RCT). You may decide to only include studies in the systematic review that were using a RCT, see  Step 8 .

See source of example

Need More Help? Book a consultation with a  Learning and Research Librarian  or contact  [email protected] .

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Formulating the Research Question

• Open Access
• First Online: 10 September 2016

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• Anuj Mehta 2 ,
• Brian Malley 2 &
• Allan Walkey 2  

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In this chapter, the reader will learn how to convert a clinical question into a pertinent research question, which includes defining an appropriate study design, select a population sample, the exposure and outcome of interest.

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Introduction to Clinical Research Concepts, Essential Characteristics of Clinical Research, Overview of Clinical Research Study Designs

The beginning – historical aspects of clinical research, clinical research: definitions, “anatomy and physiology,” and the quest for “universal truth”.

What Is the Question?

• Clinical question
• Research question

Understand how to turn a clinical question into a research question.

Principles of choosing a sample.

Approaches and potential pitfalls.

Principles of defining the exposure of interest.

Principles of defining the outcome.

Selecting an appropriate study design.

1 Introduction

The clinical question arising at the time of most health-care decisions is: “will this help my patient?” Before embarking on an investigation to provide data that may be used to inform the clinical question, the question must be modified into a research query. The process of developing a research question involves defining several components of the study and also what type of study is most suited to utilize these components to yield valid and reliable results. These components include: in whom is this research question relevant? The population of subjects defined by the researcher is referred to as the sample. The drug, maneuver, event or characteristic that we are basing our alternative hypothesis on is called the exposure of interest. Finally, the outcome of interest must be defined. With these components in mind the researcher must decide which study design is best or most feasible for answering the question. If an observational study design is chosen, then the choice of a database is also crucial.

In this chapter, we will explore how researchers might work through converting a clinical question into a research question using the clinical scenario of indwelling arterial catheters (IAC) use during mechanical ventilation (MV). Furthermore, we will discuss the strengths and weaknesses of common study designs including randomized controlled trials as well as observational studies.

2 The Clinical Scenario: Impact of Indwelling Arterial Catheters

Patients who require MV because they are unable to maintain adequate breathing on their own (e.g. from severe pneumonia or asthma attack) are often the sickest patients in the hospital, with mortality rates exceeding 30 % [ 1 – 3 ]. Multiple options are available to monitor the adequacy of respiratory support for critically ill patients requiring MV, ranging from non-invasive trans-cutaneous measures to invasive, indwelling monitoring systems. IACs are invasive monitoring devices that allow continuous real time blood pressure monitoring and facilitate access to arterial blood sampling to assess arterial blood pH, oxygen and carbon dioxide levels, among others [ 4 – 6 ]. While closer monitoring of patients requiring MV with IACs may appear at face value to be beneficial, IACs may result in severe adverse events, including loss of blood flow to the hand and infection [ 7 , 8 ]. Currently, data is lacking whether benefits may outweigh risks of more intensive monitoring using IACs. Examining factors associated with the decision to use IACs, and outcomes in patients provided IACs as compared to non-invasive monitors alone, may provide information useful to clinicians facing the decision as to whether to place an IAC.

3 Turning Clinical Questions into Research Questions

The first step in the process of transforming a clinical question into research is to carefully define the study sample (or patient cohort) , the exposure of interest, and the outcome of interest. These 3 components—sample, exposure, and outcome—are essential parts of every research question. Slight variations in each component can dramatically affect the conclusions that can be drawn from any research study, and whether the research will appropriately address the overarching clinical question.

3.1 Study Sample

In the case of IAC use, one might imagine many potential study samples of interest: for example, one might include all ICU patients, all patients receiving MV, all patients receiving intravenous medications that strongly affect blood pressure, adults only, children only, etc. Alternatively, one could define samples based on specific diseases or syndrome, such as shock (where IACs may be used to closely monitor blood pressure) or severe asthma (where IAC may be used to monitor oxygen or carbon dioxide levels).

The choice of study sample will affect both the internal and the external validity (generalizability) of the study. A study focusing only on a pediatric population may not apply to the adult population. Similarly, a study focused on patients receiving MV may not be applicable to non-ventilated patients. Furthermore, a study including patients with different reasons for using an IAC, with different outcomes related to the reason for IAC use, may lack internal validity due to bias called ‘confounding’. Confounding is a type of study bias in which an exposure variable is associated with both the exposure and the outcome.

For instance, if the benefits of IACs on mortality are studied in all patients receiving MV, researchers must take into account the fact that IAC placement may actually be indicative of greater severity of illness. For example, imagine a study with a sample of MV patients in which those with septic shock received an IAC to facilitate vasoactive medications and provide close blood pressuring monitoring while patients with asthma did not receive an IAC as other methods were used to monitor their ventilation (such as end-tidal CO 2 monitoring). Patients with septic shock tend to have a much higher severity of illness compared to patients with asthma regardless of whether an IAC is placed. In such a study, researchers may conclude that IACs are associated with higher mortality only because IACs were used in sicker patients with a higher risk of dying. The variable “diagnosis” is therefore a confounding factor, associated with both the exposure (decision to insert an IAC) and the outcome (death). Careful sample selection is one method of attempting to address issues of confounding related to severity of illness. Restricting study samples to exclude groups that may strongly confound results (i.e. no patients on vasoactive medications) is one strategy to reduce bias. However, the selection of homogeneous study samples to increase internal validity should be balanced with the desire to generalize study findings to broader patient populations. These principles are discussed more extensively in the Chap.  10 —“Cohort Selection”.

3.2 Exposure

The exposure in our research question appears to be fairly clear: placement of an IAC. However, careful attention should be paid as to how each exposure or variable of interest is defined. Misclassifying exposures may bias results. How should IAC be measured? For example, investigators may use methods ranging from direct review of the medical chart to use of administrative claims data (i.e. International Classification of Diseases—ICD-codes) to identify IAC use. Each method of ascertaining the exposure of interest may have pros (improved accuracy of medical chart review) and cons (many person-hours to perform manual chart review).

Defining the time window during which an exposure of interest is measured may also have substantial implications that must be considered when interpreting the research results. For the purposes of our IAC study, the presence of an IAC was defined as having an IAC placed after the initiation of MV. The time-dependent nature of the exposure is critical for answering the clinical question; some IACs placed prior to MV are for monitoring of low-risk surgical patients in the operating room. Including all patients with IACs regardless of timing may bias the results towards a benefit for IACs by including many otherwise healthy patients who had an IAC placed for surgical monitoring. Alternatively, if the exposure group is defined as patients who had an IAC at least 48 h after initiation of MV, the study is at risk for a type of confounding called “immortal time bias”: only patients who were alive could have had an IAC placed, whereas patients dying prior to 48 h (supposedly sicker) could not have had an IAC.

Equally important to defining the group of patients who received or experienced an exposure is to define the “unexposed” or control group. While not all research requires a control group (e.g. epidemiologic studies), a control group is needed to assess the effectiveness of healthcare interventions. In the case of the IAC study, the control group is fairly straightforward: patients receiving MV who did not have an IAC placed. However, there are important nuances when defining control groups. In our study example, an alternate control group could be all ICU patients who did not receive an IAC. However, the inclusion of patients not receiving MV results in a control group with a lower severity of illness and expected mortality than patients receiving MV, which would bias in favor of not using IACs. Careful definition of the control group is needed to properly interpret any conclusions from research; defining an appropriate control group is as important as defining the exposure.

3.3 Outcome

Finally, the investigator needs to determine the outcome of interest. Several different types of outcomes can be considered, including intermediate or mechanistic outcomes (informs etiological pathways, but may not immediately impact patients), patient-centered outcomes (informs outcomes important to patients, but may lack mechanistic insights: e.g. comfort scales, quality of life indices, or mortality), or healthcare-system centered outcomes (e.g. resource utilization, or costs). In our example of IAC use, several outcomes could be considered including intermediate outcomes (e.g. number of arterial blood draws, ventilator setting changes, or vasoactive medication changes), patient-centered outcomes (e.g. 28-day or 90-day mortality, adverse event rates), or healthcare utilization (e.g. hospitalization costs, added clinician workload). As shown in our example, outcome(s) may build upon each other to yield a constellation of findings that provides a more complete picture to address the clinical question of interest.

After clearly defining the study sample, exposure of interest, and outcome of interest, a research question can be formulated. A research question using our example may be formulated as follows:

“ In the population of interest ( study cohort ), is the exposure to the variable of interest associated with a different outcome than in the control group ? ”, which becomes, in our example:

“ Among mechanically ventilated, adult ICU patients who are not receiving vasoactive medications (i.e., the study sample) is placement of an IAC after initiation of MV (as compared with not receiving an IAC) (i.e. the exposure and control patients) associated with improved 28 - day mortality rates (primary outcome, patient - centered) and the number of blood gas measurements per day (supporting secondary outcome, intermediate/mechanistic)? ”

4 Matching Study Design to the Research Question

Once the research question has been defined, the next step is to choose the optimal study design given the question and resources available. In biomedical research, the gold-standard for study design remains the double-blinded, randomized, placebo-controlled trial (RCT) [ 9 , 10 ]. In a RCT, patients with a given condition (e.g. all adults receiving MV) would be randomized to receive a drug or intervention of interest (e.g. IAC) or randomized to receive the control (e.g. no IAC), with careful measurement of pre-determined outcomes (e.g. 28-day mortality). In ideal conditions, the randomization process eliminates all measured and unmeasured confounding and allows for causal inferences to be drawn, which cannot generally be achieved without randomization. As shown above, confounding is a threat to valid inferences from study results. Alternatively, in our example of septic shock verses asthma, severity of illness associated with the underlying condition may represent another confounder. Randomization solely based on the exposure of interest attempts to suppress issues of confounding. In our examples, proper randomization in a large sample would theoretically create equal age distributions and equal numbers of patients with septic shock and asthma in both the exposure and the control group.

However, RCTs have several limitations. Although the theoretical underpinnings of RCTs are fairly simple, the complex logistics of patient enrollment and retention, informed consent, randomization, follow up, and blinding may result in RCTs deviating from the ‘ideal conditions’ necessary for unbiased, causal inference. Additionally, RCTs carry the highest potential for patient harm and require intensive monitoring because the study dictates what type of treatment a patient receives (rather than the doctor) and may deviate from routine care. Given the logistic complexity, RCTs are often time- and cost-intensive, frequently taking many years and millions of dollars to complete. Even when logistically feasible, RCTs often ‘weed out’ multiple groups of patients in order to minimize potential harms and maximize detection of associations between interventions and outcomes of interest. As a result, RCTs can consist of homogeneous patients meeting narrow criteria, which may reduce the external validity of the studies’ findings. Despite much effort and cost, an RCT may miss relevance to the clinical question as to whether the intervention of interest is helpful for your particular patient or not. Finally, some clinical questions may not ethically be answered with RCTs. For instance, the link between smoking and lung cancer has never been shown in a RCT, as it is unethical to randomize patients to start smoking in a smoking intervention group, or randomize patients to a control group in a trial to investigate the efficacy of parachutes [ 11 ]!

Observational research differs from RCTs. Observational studies are non-experimental; researchers record routine medical practice patterns and derive conclusions based on correlations and associations without active interventions [ 9 , 12 ]. Observational studies can be retrospective (based on data that has already been collected), prospective (data is actively collected over time), or ambi-directional (a mix). Unlike RCTs, researchers in observational studies have no role in deciding what types of treatments or interventions patients receive. Observational studies tend to be logistically less complicated than RCTs as there is no active intervention, no randomization, no data monitoring boards, and data is often collected retrospectively. As such, observational studies carry less risk of harm to patients (other than loss of confidentiality of data that has been collected) than RCTs, and tend to be less time- and cost-intensive. Retrospective databases like MIMIC-II [ 13 ] or the National Inpatient Sample [ 14 ] can also provide much larger study samples (tens of thousands in some instances) than could be enrolled in an RCT, thus providing larger statistical power. Additionally, broader study samples are often included in observational studies, leading to greater generalizability of the results to a wider range of patients (external validity). Finally, certain clinical questions that would be unethical to study in an RCT can be investigated with observational studies. For example, the link between lung cancer and tobacco use has been demonstrated with multiple large prospective epidemiological studies [ 15 , 16 ] and the life-saving effects of parachutes have been demonstrated mostly through the powers of observation.

Although logistically simpler than RCTs, the theoretical underpinnings of observational studies are generally more complex than RCTs. Obtaining causal estimates of the effect of a specific exposure on a specific outcome depends on the philosophical concept of the ‘counterfactual’ [ 17 ]. The counterfactual is the situation in which, all being equal, the same research subject at the same time would receive the exposure of interest and (the counterfactual) not receive the exposure of interest, with the same outcome measured in the exposed and unexposed research subject. Because we cannot create cloned research subjects in the real-world, we rely on creating groups of patients similar to the group that receives an intervention of interest. In the case of an ideal RCT with a large enough number of subjects, the randomization process used to select the intervention and control groups creates two alternate ‘universes’ of patients that will be similar except as related to the exposure of interest. Because observational studies cannot intervene on study subjects, observational studies create natural experiments in which the counterfactual group is defined by the investigator and by clinical processes occurring in the real-world. Importantly, real-world clinical processes often occur for a reason, and these reasons can cause deviation from counterfactual ideals in which exposed and unexposed study subjects differ in important ways. In short, observational studies may be more prone to bias (problems with internal validity) than RCTs due to difficulty obtaining the counterfactual control group.

Several types of biases have been identified in observational studies. Selection bias occurs when the process of selecting exposed and unexposed patients introduces a bias into the study. For example, the time between starting MV and receiving IAC may introduce a type of “survivor treatment selection bias” since patients who received IAC could not have died prior to receiving IACs. Information bias stems from mismeasurement or misclassification of certain variables. For retrospective studies, the data has already been collected and sometimes it is difficult to evaluate for errors in the data. Another major bias in observational studies is confounding. As stated, confounding occurs when a third variable is correlated with both the exposure and outcome. If the third variable is not taken into consideration, a spurious relationship between the exposure and outcome may be inferred. For example, smoking is an important confounder in several observational studies as it is associated with several other behaviors such as coffee and alcohol consumption. A study investigating the relationship between coffee consumption and incidence of lung cancer may conclude that individuals who drink more coffee have higher rates of lung cancer. However, as smoking is associated with both coffee consumption and lung cancer, it is confounder in the relationship between coffee consumption and lung cancer if unmeasured and unaccounted for in analysis. Several methods have been developed to attempt to address confounding in observational research such as adjusting for the confounder in regression equations if it is known and measured, matching cohorts by known confounders, and using instrumental variables—methods that will be explained in-depth in future chapters. Alternatively, one can restrict the study sample (e.g. excluding patients with shock from a study evaluating the utility of IACs). For these reasons, while powerful, an individual observational study can, at best, demonstrate associations and correlations and cannot prove causation. Over time, a cumulative sum of multiple high quality observational studies coupled with other mechanistic evidence can lead to causal conclusions, such as in the causal link currently accepted between smoking and lung cancer established by observational human studies and experimental trials in animals.

5 Types of Observational Research

There are multiple different types of questions that can be answered with observational research (Table  9.1 ). Epidemiological studies are one major type of observational research that focuses on the burden of disease in predefined populations. These types of studies often attempt to define incidence, prevalence, and risk factors for disease. Additionally, epidemiological studies also can investigate changes to healthcare or diseases over time. Epidemiological studies are the cornerstone of public health and can heavily influence policy decisions, resource allocation, and patient care. In the case of lung cancer, predefined groups of patients without lung cancer were monitored for years until some patients developed lung cancer. Researchers then compared numerous risk factors, like smoking, between those who did and did not develop lung cancer which led to the conclusion that smoking increased the risk of lung cancer [ 15 , 16 ].

There are other types of epidemiological studies that are based on similar principles of observational research but differ in the types of questions posed. Predictive modeling studies develop models that are able to accurately predict future outcomes in specific groups of patients. In predictive studies, researchers define an outcome of interest (e.g. hospital mortality) and use data collected on patients such as labs, vital signs, and disease states to determine which factors contributed to the outcome. Researchers then validate the models developed from one group of patients in a separate group of patients. Predictive modeling studies developed many common prediction scores used in clinical practice such as the Framingham Cardiovascular Risk Score [ 18 ], APACHE IV [ 19 ], SAPS II [ 20 ], and SOFA [ 21 ].

Comparative effectiveness research is another form of observational research which involves the comparison of existing healthcare interventions in order to determine effective methods to deliver healthcare. Unlike descriptive epidemiologic studies, comparative effectiveness research compares outcomes between similar patients who received different treatments in order to assess which intervention may be associated with superior outcomes in real-world conditions. This could involve comparing drug A to drug B or could involve comparing one intervention to a control group who did not receive that intervention. Given that there are often underlying reasons why one patient received treatment A versus B or an intervention versus no intervention, comparative effectiveness studies must meticulously account for potential confounding factors. In the case of IACs, the research question comparing patients who had an IAC placed to those who did not have an IAC placed would represent a comparative effectiveness study.

Pharmacovigilance studies are yet another form of observational research. As many drug and device trials end after 1 or 2 years, observational methods are used to evaluate if there are patterns of rarer adverse events occurring in the long-term. Phase IV clinical studies are one form of pharmacovigilance studies in which long-term information related to efficacy and harm are gathered after the drug has been approved.

6 Choosing the Right Database

A critical part of the research process is deciding what types of data are needed to answer the research question. Administrative/claims data, secondary use of clinical trial data, prospective epidemiologic studies, and electronic health record (EHR) systems (both from individual institutions and those pooled from multiple institutions) are several sources from which databases can be built. Administrative or claims databases, such as the National Inpatient Sample and State Inpatient Databases complied by the Healthcare Cost and Utilization Project or the Medicare database, contain information on patient and hospital demographics as well as billing and procedure codes. Several techniques have been developed to translate these billing and procedure codes to more clinically useful disease descriptions. Administrative databases tend to provide very large sample sizes and, in some cases, can be representative of an entire population. However, they lack granular patient-level data from the hospitalization such as vital signs, laboratory and microbiology data, timing data (such as duration of MV or days with an IAC) or pharmacology data, which are often important in dealing with possible confounders.

Another common source of data for observational research is large epidemiologic studies like the Framingham Heart Study as well as large multicenter RCTs such as the NIH ARDS Network. Data that has already been can be analyzed retrospectively with new research questions in mind. As the original data was collected for research purposes, these types of databases often have detailed, granular information not available in other clinical databases. However, researchers are often bound by the scope of data collection from the original research study which limits the questions that may be posed. Importantly, generalizability may be limited in data from trials.

The advent of Electronic Health Records (EHR) has resulted in the digitization of medical records from their prior paper format. The resulting digitized medical records present opportunities to overcome some of the shortcomings of administrative data, yielding granular data with laboratory results, medications, and timing of clinical events [ 13 ]. These “big databases” take advantage of the fact many EHRs collect data from a variety of sources such as patient monitors, laboratory systems, and pharmacy systems and coalesce them into one system for clinicians. This information can then be translated into de-identified databases for research purposes that contain detailed patient demographics, billing and procedure information, timing data, hospital outcomes data, as well as patient-level granular data and provider notes which can searched using natural language processing tools. “Big data” approaches may attenuate confounding by providing detailed information needed to assess severity of illness (such as lab results and vital signs). Furthermore, the granular nature of the data can provide insight as to the reason why one patient received an intervention and another did not which can partly address confounding by indication. Thus, the promise of “big data” is that it contains small, very detailed data. “Big data” databases, such as MIMIC-III, have the potential to expand the scope of what had previously been possible with observational research.

7 Putting It Together

Fewer than 10 % of clinical decisions are supported by high level evidence [ 22 ]. Clinical questions arise approximately in every other patient [ 23 ] and provide a large cache of research questions. When formulating a research question, investigators must carefully select the appropriate sample of subjects, exposure variable, outcome variable, and confounding variables. Once the research question is clear, study design becomes the next pivotal step. While RCTs are the gold standard for establishing causal inference under ideal conditions, they are not always practical, cost-effective, ethical or even possible for some types of questions. Observational research presents an alternative to performing RCTs, but is often limited in causal inference by unmeasured confounding.

Our clinical scenario gave rise to the question of whether IACs improved the outcomes of patients receiving MV. This translated into the research question: “Among mechanically ventilated ICU patients not receiving vasoactive medications (study sample) is use of an IAC after initiation of MV (exposure) associated with improved 28-day mortality (outcome)?” While an RCT could answer this question, it would be logistically complex, costly, and difficult. Using comparative effectiveness techniques, one can pose the question using a granular retrospective database comparing patients who received an IAC to measurably similar patients who did not have an IAC placed. However, careful attention must be paid to unmeasured confounding by indication as to why some patients received IAC and others did not. Factors such as severity of illness, etiology of respiratory failure, and presence of certain diseases that make IAC placement difficult (such as peripheral arterial disease) may be considered as possible confounders of the association between IAC and mortality. While an administrative database could be used, it could lack important information related to possible confounders. As such, EHR databases like MIMIC-III, with detailed granular patient-level data, may allow for measurement of a greater number of previously unmeasured confounding variables and allow for greater attenuation of bias in observational research.

Take Home Messages

Most research questions arise from clinical scenarios in which the proper course of treatment is unclear or unknown.

Defining a research question requires careful consideration of the optimal study sample, exposure, and outcome in order to answer a clinical question of interest.

While observational research studies can overcome many of the limitations of randomized controlled trials, careful consideration of study design and database selection is needed to address bias and confounding.

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Mehta, A., Malley, B., Walkey, A. (2016). Formulating the Research Question. In: Secondary Analysis of Electronic Health Records. Springer, Cham. https://doi.org/10.1007/978-3-319-43742-2_9

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Formulating a research question

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Clarifying the review question leads to specifying what type of studies can best address that question and setting out criteria for including such studies in the review. This is often called inclusion criteria or eligibility criteria. The criteria could relate to the review topic, the research methods of the studies, specific populations, settings, date limits, geographical areas, types of interventions, or something else.

Systematic reviews address clear and answerable research questions, rather than a general topic or problem of interest. They also have clear criteria about the studies that are being used to address the research questions. This is often called inclusion criteria or eligibility criteria.

Six examples of types of question are listed below, and the examples show different questions that a review might address based on the topic of influenza vaccination. Structuring questions in this way aids thinking about the different types of research that could address each type of question. Mneumonics can help in thinking about criteria that research must fulfil to address the question. The criteria could relate to the context, research methods of the studies, specific populations, settings, date limits, geographical areas, types of interventions, or something else.

Examples of review questions

• Needs - What do people want? Example: What are the information needs of healthcare workers regarding vaccination for seasonal influenza?
• Impact or effectiveness - What is the balance of benefit and harm of a given intervention? Example: What is the effectiveness of strategies to increase vaccination coverage among healthcare workers. What is the cost effectiveness of interventions that increase immunisation coverage?
• Process or explanation - Why does it work (or not work)? How does it work (or not work)?  Example: What factors are associated with uptake of vaccinations by healthcare workers?  What factors are associated with inequities in vaccination among healthcare workers?
• Correlation - What relationships are seen between phenomena? Example: How does influenza vaccination of healthcare workers vary with morbidity and mortality among patients? (Note: correlation does not in itself indicate causation).
• Views / perspectives - What are people's experiences? Example: What are the views and experiences of healthcare workers regarding vaccination for seasonal influenza?
• Service implementation - What is happening? Example: What is known about the implementation and context of interventions to promote vaccination for seasonal influenza among healthcare workers?

Examples in practice :  Seasonal influenza vaccination of health care workers: evidence synthesis / Loreno et al. 2017

Example of eligibility criteria

Research question: What are the views and experiences of UK healthcare workers regarding vaccination for seasonal influenza?

• Population: healthcare workers, any type, including those without direct contact with patients.
• Context: seasonal influenza vaccination for healthcare workers.
• Study design: qualitative data including interviews, focus groups, ethnographic data.
• Date of publication: all.
• Country: all UK regions.
• Studies focused on influenza vaccination for general population and pandemic influenza vaccination.
• Studies using survey data with only closed questions, studies that only report quantitative data.

Consider the research boundaries

It is important to consider the reasons that the research question is being asked. Any research question has ideological and theoretical assumptions around the meanings and processes it is focused on. A systematic review should either specify definitions and boundaries around these elements at the outset, or be clear about which elements are undefined. 

For example if we are interested in the topic of homework, there are likely to be pre-conceived ideas about what is meant by 'homework'. If we want to know the impact of homework on educational attainment, we need to set boundaries on the age range of children, or how educational attainment is measured. There may also be a particular setting or contexts: type of school, country, gender, the timeframe of the literature, or the study designs of the research.

Research question: What is the impact of homework on children's educational attainment?

• Scope : Homework - Tasks set by school teachers for students to complete out of school time, in any format or setting.
• Population: children aged 5-11 years.
• Outcomes: measures of literacy or numeracy from tests administered by researchers, school or other authorities.
• Study design: Studies with a comparison control group.
• Context: OECD countries, all settings within mainstream education.
• Date Limit: 2007 onwards.
• Any context not in mainstream primary schools.
• Non-English language studies.

Mnemonics for structuring questions

Some mnemonics that sometimes help to formulate research questions, set the boundaries of question and inform a search strategy.

Intervention effects

PICO  Population – Intervention– Outcome– Comparison

Variations: add T on for time, or ‘C’ for context, or S’ for study type,

Policy and management issues

ECLIPSE : Expectation – Client group – Location – Impact ‐ Professionals involved – Service

Expectation encourages  reflection on what the information is needed for i.e. improvement, innovation or information.  Impact looks at what  you would like to achieve e.g. improve team communication .

• How CLIP became ECLIPSE: a mnemonic to assist in searching for health policy/management information / Wildridge & Bell, 2002

Analysis tool for management and organisational strategy

PESTLE:  Political – Economic – Social – Technological – Environmental ‐ Legal

An analysis tool that can be used by organizations for identifying external factors which may influence their strategic development, marketing strategies, new technologies or organisational change.

• PESTLE analysis / CIPD, 2010

Service evaluations with qualitative study designs

SPICE:  Setting (context) – Perspective– Intervention – Comparison – Evaluation

Perspective relates to users or potential users. Evaluation is how you plan to measure the success of the intervention.

• Clear and present questions: formulating questions for evidence based practice / Booth, 2006

Read more about some of the frameworks for constructing review questions:

• Formulating the Evidence Based Practice Question: A Review of the Frameworks / Davis, 2011
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• Next: Identifying studies >>
• Last Updated: Aug 2, 2024 9:22 AM
• URL: https://library-guides.ucl.ac.uk/systematic-reviews

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• Purpose of this Guide

Formulate a Research Question

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The research question or statement is crucial. A well-formulated question will focus your information needs, help to identify key search concepts, and guide you in the direction of potential resources. 

• Quantitative Questions  aim to discover cause-and-effect relationships by comparing two or more individuals or groups based on differing outcomes associated with exposures or interventions
• Qualitative Questions  aim to discover meaning or gain an understanding of a phenomenon

There are a variety of frameworks that can be used to formulate your research question and identify possible search concepts for your literature search.  Here are some selected frameworks to help you:  

PICO(T):  Q uantitative Research

P opulation/ P roblem,  I ntervention/ E xposure,  C omparison,  O utcome, and  T ime Period/ T ype of Study.

In  ____[P]_____,  do/does  ____[I]____  result in  ____[O]___  over  ___[T]______?

E.g. In   emergency room visitors,  do  hand sanitizing stations  result in  fewer in-hospital infections  when compared  with no hand sanitizing stations  over a  year-long pilot period?

PS:  Q ualitative Research

P opulation/ P roblem,  S ituation

How do/does  ___  [P]  ___ experience ____ [S] _____?

E.g. How do  caregiver-spouses of Alzheimer patients  experience  placing their spouse in a nursing home ?

Additional Frameworks

PIE  ( P opulation,  I ntervention,  E ffect / Outcome)

PEO  ( P opulation/Problem,  E xposure,  O utcomes/Themes)

FINER  ( F easibility,  I nteresting,  N ovel,  E thical,  R elevant)

SPICE  ( S etting,  P erspective,  I ntervention,  C omparison,  E valuation)

SPIDER  ( S ample,  P henomena of  I nterest,  D esign,  E valuation,  R esearch type)

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• What is a Literature Review?

The Research Questions

• Selection Criteria
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Background vs. Foreground Questions

You may need to find answers to background questions (i.e. about general knowledge) before seeking answers to foreground questions (i.e. about specific knowledge, such as information that might inform a clinical decision).

The research questions on this page are for foreground questions.

A well-formulated research question:

• starts your entire search process
• provides focus for your searches
• guides the selection of literature sources

Question formats are helpful tools researchers can use to structure a question that will facilitate a focused search. Such formats include: PICO , PEO , SPIDER , and  COSMIN . Other formats can be found here .  

The  PICO  format is commonly used in evidence-based clinical practice.  This format creates a "well-built" question that identifies four concepts: (1) the P atient problem or P opulation, (2) the I ntervention, (3) the C omparison (if there is one), and (4) the O utcome(s) .

Example : In adults with recurrent furunculosis (skin boils), do prophylactic antibiotics, compared to no treatment, reduce the recurrence rate?  ( Cochrane Library Tutorial, 2005 )

The  PEO  question format is useful for qualitative research questions. Questions based on this format identify three concepts: (1) P opulation, (2) E xposure, and (3) O utcome(s) .

Example:  In infants, is there an association between exposure to soy milk and the subsequent development of peanut allergy ( Levine, Ioannidis, Haines, & Guyatt, 2014 )?

The  SPIDER  question format was adapted from the PICO tool to search for qualitative and mixed-methods research.  Questions based on this format identify the following concepts: (1) S ample, (2) P henomenon of I nterest, (3) D esign, (4) E valuation, and (5) R esearch type .

Example:  What are young parents’ experiences of attending antenatal education? 

Search for ( S  AND  P of I   AND ( D  OR  E ) AND  R ) ( Cooke, Smith, & Booth, 2012 ).

The COSMIN  ( CO nsensus-based  S tandards for the selection of health status M easurement IN struments ) format is used for systematic review of measurement properties.  Questions based on this format identify (1) the construct or the name(s) of the outcome measurement instrument(s) of interest,  (2) the target population, (3) the type of measurement instrument of interest, and (4) the measurement properties on which the review focuses.

Visit the COSMIN website to view the COSMIN manual and checklist.

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Capstone and PICO Project Toolkit

• Starting a Project: Overview
• Developing a Research Question
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Defining the Question: Foreground & Background Questions

In order to most appropriately choose an information resource and craft a search strategy, it is necessary to consider what  kind  of question you are asking: a specific, narrow "foreground" question, or a broader background question that will help give context to your research?

Foreground Questions

A "foreground" question in health research is one that is relatively specific, and is usually best addressed by locating primary research evidence. 

Using a structured question framework can help you clearly define the concepts or variables that make up the specific research question. 

 Across most frameworks, you’ll often be considering:

• a who (who was studied - a population or sample)
• a what (what was done or examined - an intervention, an exposure, a policy, a program, a phenomenon)
• a how ([how] did the [what] affect the [who] - an outcome, an effect). 

PICO is the most common framework for developing a clinical research question, but multiple question frameworks exist.

PICO (Problem/Population, Intervention, Comparison, Outcome)

Appropriate for : clinical questions, often addressing the effect of an intervention/therapy/treatment

Example : For adolescents with type II diabetes (P) does the use of telehealth consultations (I) compared to in-person consultations  (C) improve blood sugar control  (O)?

Framing Different Types of Clinical Questions with PICO

Different types of clinical questions are suited to different syntaxes and phrasings, but all will clearly define the PICO elements.  The definitions and frames below may be helpful for organizing your question:

Intervention/Therapy

Questions addressing how a clinical issue, illness, or disability is treated.

"In__________________(P), how does__________________(I) compared to_________________(C) affect______________(O)?"

Questions that address the causes or origin of disease, the factors which produce or predispose toward a certain disease or disorder.

"Are_________________(P), who have_________________(I) compared with those without_________________(C) at_________________risk for/of_________________(O) over_________________(T)?" 

Questions addressing the act or process of identifying or determining the nature and cause of a disease or injury through evaluation.

In_________________(P) are/is_________________(I) compared with_________________(C) more accurate in diagnosing_________________(O)?

Prognosis/Prediction:

Questions addressing the prediction of the course of a disease.

In_________________(P), how does_________________(I) compared to_________________ (C) influence_________________(O)?

Questions addressing how one experiences a phenomenon or why we need to approach practice differently.

"How do_________________(P) with_________________(I) perceive_________________(O)?" 

Adapted from: Melnyk, B. M., & Fineout-Overholt, E. (2011). Evidence-based practice in nursing & healthcare: A guide to best practice. Philadelphia: Wolters Kluwer/Lippincott Williams & Wilkins.

Beyond PICO: Other Types of Question Frameworks

PICO is a useful framework for clinical research questions, but may not be appropriate for all kinds of reviews.  Also consider:

PEO (Population, Exposure, Outcome)

Appropriate for : describing association between particular exposures/risk factors and outcomes

Example : How do  preparation programs (E) influence the development of teaching competence  (O) among novice nurse educators  (P)?

SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, Research Type)

Appropriate for : questions of experience or perspectives (questions that may be addressed by qualitative or mixed methods research)

Example : What are the experiences and perspectives (E) of  undergraduate nursing students  (S)  in clinical placements within prison healthcare settings (PI)?

SPICE (Setting, Perspective, Intervention/phenomenon of Interest, Comparison, Evaluation)

Appropriate for : evaluating the outcomes of a service, project, or intervention

Example : What are the impacts and best practices for workplace (S) transition support programs (I) for the retention (E) of newly-hired, new graduate nurses (P)?

PCC (Problem/population, Concept, Context)

Appropriate for : broader (scoping) questions

Example : How do nursing schools  (Context) teach, measure, and maintain nursing students ' (P)  technological literacy  (Concept))throughout their educational programs?

Background Questions

To craft a strong and reasonable foreground research question, it is important to have a firm understanding of the concepts of interest.  As such, it is often necessary to ask background questions, which ask for more general, foundational knowledge about a disorder, disease, patient population, policy issue, etc. 

For example, consider the PICO question outlined above:

"For adolescents with type II diabetes does the use of telehealth consultations compared to in-person consultations  improve blood sugar control ?

To best make sense of the literature that might address this PICO question, you would also need a deep understanding of background questions like:

• What are the unique barriers or challenges related to blood sugar management in adolescents with TII diabetes?
• What are the measures of effective blood sugar control?
• What kinds of interventions would fall under the umbrella of 'telehealth'?
• What are the qualitative differences in patient experience in telehealth versus in-person interactions with healthcare providers?
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Systematic Reviews for Non-Health Sciences

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• Types of reviews
• 0. Planning the systematic review
• 1. Formulating the research question

Formulating a research question

Purpose of a framework, selecting a framework.

• 2. Developing the protocol
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Formulating a question.

Formulating a strong research question for a systematic review can be a lengthy process. While you may have an idea about the topic you want to explore, your specific research question is what will drive your review and requires some consideration. 

You will want to conduct preliminary  or  exploratory searches  of the literature as you refine your question. In these searches you will want to:

• Determine if a systematic review has already been conducted on your topic and if so, how yours might be different, or how you might shift or narrow your anticipated focus
• Scope the literature to determine if there is enough literature on your topic to conduct a systematic review
• Identify key concepts and terminology
• Identify seminal or landmark studies
• Identify key studies that you can test your research strategy against (more on that later)
• Begin to identify databases that might be useful to your search question

Systematic review vs. other reviews

Systematic reviews required a  narrow and specific research question. The goal of a systematic review is to provide an evidence synthesis of ALL research performed on one particular topic. So, your research question should be clearly answerable from the data you gather from the studies included in your review.

Ask yourself if your question even warrants a systematic review (has it been answered before?). If your question is more broad in scope or you aren't sure if it's been answered, you might look into performing a systematic map or scoping review instead.

Learn more about systematic reviews versus scoping reviews:

• CEE. (2022). Section 2:Identifying the need for evidence, determining the evidence synthesis type, and establishing a Review Team. Collaboration for Environmental Evidence.  https://environmentalevidence.org/information-for-authors/2-need-for-evidence-synthesis-type-and-review-team-2/
• DistillerSR. (2022). The difference between systematic reviews and scoping reviews. DistillerSR.  https://www.distillersr.com/resources/systematic-literature-reviews/the-difference-between-systematic-reviews-and-scoping-reviews
• Nalen, CZ. (2022). What is a scoping review? AJE.  https://www.aje.com/arc/what-is-a-scoping-review/

• Frame your entire research process
• Determine the scope of your review
• Provide a focus for your searches
• Help you identify key concepts
• Guide the selection of your papers

There are different frameworks you can use to help structure a question.

• PICO / PECO
• What if my topic doesn't fit a framework?

The PICO or PECO framework is typically used in clinical and health sciences-related research, but it can also be adapted for other quantitative research.

P — Patient / Problem / Population

I / E — Intervention / Indicator / phenomenon of Interest / Exposure / Event 

C  — Comparison / Context / Control

O — Outcome

Example topic : Health impact of hazardous waste exposure

Fazzo, L., Minichilli, F., Santoro, M., Ceccarini, A., Della Seta, M., Bianchi, F., Comba, P., & Martuzzi, M. (2017). Hazardous waste and health impact: A systematic review of the scientific literature.  Environmental Health ,  16 (1), 107.  https://doi.org/10.1186/s12940-017-0311-8

The SPICE framework is useful for both qualitative and mixed-method research. It is often used in the social sciences.

S — Setting (where?)

P — Perspective (for whom?)

I — Intervention / Exposure (what?)

C — Comparison (compared with what?)

E — Evaluation (with what result?)

Learn more : Booth, A. (2006). Clear and present questions: Formulating questions for evidence based practice.  Library Hi Tech ,  24 (3), 355-368.  https://doi.org/10.1108/07378830610692127

The SPIDER framework is useful for both qualitative and mixed-method research. It is most often used in health sciences research.

S — Sample

PI — Phenomenon of Interest

D — Design

E — Evaluation

R — Study Type

Learn more : Cooke, A., Smith, D., & Booth, A. (2012). Beyond PICO: The SPIDER tool for qualitative evidence synthesis.  Qualitative Health Research, 22 (10), 1435-1443.  https://doi.org/10.1177/1049732312452938

The CIMO framework is used to understand complex social and organizational phenomena, most useful for management and business research.

C — Context (the social and organizational setting of the phenomenon)

I  — Intervention (the actions taken to address/influence the phenomenon)

M — Mechanisms (the underlying processes or mechanisms that drive change within the phenomenon)

O — Outcomes (the resulting changes that occur due to intervention/mechanisms)

Learn more : Denyer, D., Tranfield, D., & van Aken, J. E. (2008). Developing design propositions through research synthesis. Organization Studies, 29 (3), 393-413. https://doi.org/10.1177/0170840607088020

Click  here   for an exhaustive list of research question frameworks from the University of Maryland Libraries.

You might find that your topic does not always fall into one of the models listed on this page. You can always modify a model to make it work for your topic, and either remove or incorporate additional elements. Be sure to document in your review the established framework that yours is based off and how it has been modified.

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Secondary Analysis of Electronic Health Records [Internet]. Cham (CH): Springer; 2016. doi: 10.1007/978-3-319-43742-2_9

Secondary Analysis of Electronic Health Records [Internet].

Chapter 9 formulating the research question.

Anuj Mehta , Brian Malley , and Allan Walkey .

Affiliations

Published online: September 10, 2016.

In this chapter, the reader will learn how to convert a clinical question into a pertinent research question, which includes defining an appropriate study design, select a population sample, the exposure and outcome of interest.

Learning Objectives

• Understand how to turn a clinical question into a research question.
• Principles of choosing a sample.
• Approaches and potential pitfalls.
• Principles of defining the exposure of interest.
• Principles of defining the outcome.
• Selecting an appropriate study design.

9.1. Introduction

The clinical question arising at the time of most health-care decisions is: “will this help my patient?” Before embarking on an investigation to provide data that may be used to inform the clinical question, the question must be modified into a research query. The process of developing a research question involves defining several components of the study and also what type of study is most suited to utilize these components to yield valid and reliable results. These components include: in whom is this research question relevant? The population of subjects defined by the researcher is referred to as the sample. The drug, maneuver, event or characteristic that we are basing our alternative hypothesis on is called the exposure of interest. Finally, the outcome of interest must be defined. With these components in mind the researcher must decide which study design is best or most feasible for answering the question. If an observational study design is chosen, then the choice of a database is also crucial.

In this chapter, we will explore how researchers might work through converting a clinical question into a research question using the clinical scenario of indwelling arterial catheters (IAC) use during mechanical ventilation (MV). Furthermore, we will discuss the strengths and weaknesses of common study designs including randomized controlled trials as well as observational studies.

9.2. The Clinical Scenario: Impact of Indwelling Arterial Catheters

Patients who require MV because they are unable to maintain adequate breathing on their own (e.g. from severe pneumonia or asthma attack) are often the sickest patients in the hospital, with mortality rates exceeding 30 % [ 1 – 3 ]. Multiple options are available to monitor the adequacy of respiratory support for critically ill patients requiring MV, ranging from non-invasive trans-cutaneous measures to invasive, indwelling monitoring systems. IACs are invasive monitoring devices that allow continuous real time blood pressure monitoring and facilitate access to arterial blood sampling to assess arterial blood pH, oxygen and carbon dioxide levels, among others [ 4 – 6 ]. While closer monitoring of patients requiring MV with IACs may appear at face value to be beneficial, IACs may result in severe adverse events, including loss of blood flow to the hand and infection [ 7 , 8 ]. Currently, data is lacking whether benefits may outweigh risks of more intensive monitoring using IACs. Examining factors associated with the decision to use IACs, and outcomes in patients provided IACs as compared to non-invasive monitors alone, may provide information useful to clinicians facing the decision as to whether to place an IAC.

9.3. Turning Clinical Questions into Research Questions

The first step in the process of transforming a clinical question into research is to carefully define the study sample (or patient cohort) , the exposure of interest, and the outcome of interest. These 3 components—sample, exposure, and outcome—are essential parts of every research question. Slight variations in each component can dramatically affect the conclusions that can be drawn from any research study, and whether the research will appropriately address the overarching clinical question.

9.3.1. Study Sample

In the case of IAC use, one might imagine many potential study samples of interest: for example, one might include all ICU patients, all patients receiving MV, all patients receiving intravenous medications that strongly affect blood pressure, adults only, children only, etc. Alternatively, one could define samples based on specific diseases or syndrome, such as shock (where IACs may be used to closely monitor blood pressure) or severe asthma (where IAC may be used to monitor oxygen or carbon dioxide levels).

The choice of study sample will affect both the internal and the external validity (generalizability) of the study. A study focusing only on a pediatric population may not apply to the adult population. Similarly, a study focused on patients receiving MV may not be applicable to non-ventilated patients. Furthermore, a study including patients with different reasons for using an IAC, with different outcomes related to the reason for IAC use, may lack internal validity due to bias called ‘confounding’. Confounding is a type of study bias in which an exposure variable is associated with both the exposure and the outcome.

For instance, if the benefits of IACs on mortality are studied in all patients receiving MV, researchers must take into account the fact that IAC placement may actually be indicative of greater severity of illness. For example, imagine a study with a sample of MV patients in which those with septic shock received an IAC to facilitate vasoactive medications and provide close blood pressuring monitoring while patients with asthma did not receive an IAC as other methods were used to monitor their ventilation (such as end-tidal CO 2 monitoring). Patients with septic shock tend to have a much higher severity of illness compared to patients with asthma regardless of whether an IAC is placed. In such a study, researchers may conclude that IACs are associated with higher mortality only because IACs were used in sicker patients with a higher risk of dying. The variable “diagnosis” is therefore a confounding factor, associated with both the exposure (decision to insert an IAC) and the outcome (death). Careful sample selection is one method of attempting to address issues of confounding related to severity of illness. Restricting study samples to exclude groups that may strongly confound results (i.e. no patients on vasoactive medications) is one strategy to reduce bias. However, the selection of homogeneous study samples to increase internal validity should be balanced with the desire to generalize study findings to broader patient populations. These principles are discussed more extensively in the Chap.  10 —“Cohort Selection”.

9.3.2. Exposure

The exposure in our research question appears to be fairly clear: placement of an IAC. However, careful attention should be paid as to how each exposure or variable of interest is defined. Misclassifying exposures may bias results. How should IAC be measured? For example, investigators may use methods ranging from direct review of the medical chart to use of administrative claims data (i.e. International Classification of Diseases—ICD-codes) to identify IAC use. Each method of ascertaining the exposure of interest may have pros (improved accuracy of medical chart review) and cons (many person-hours to perform manual chart review).

Defining the time window during which an exposure of interest is measured may also have substantial implications that must be considered when interpreting the research results. For the purposes of our IAC study, the presence of an IAC was defined as having an IAC placed after the initiation of MV. The time-dependent nature of the exposure is critical for answering the clinical question; some IACs placed prior to MV are for monitoring of low-risk surgical patients in the operating room. Including all patients with IACs regardless of timing may bias the results towards a benefit for IACs by including many otherwise healthy patients who had an IAC placed for surgical monitoring. Alternatively, if the exposure group is defined as patients who had an IAC at least 48 h after initiation of MV, the study is at risk for a type of confounding called “immortal time bias”: only patients who were alive could have had an IAC placed, whereas patients dying prior to 48 h (supposedly sicker) could not have had an IAC.

Equally important to defining the group of patients who received or experienced an exposure is to define the “unexposed” or control group. While not all research requires a control group (e.g. epidemiologic studies), a control group is needed to assess the effectiveness of healthcare interventions. In the case of the IAC study, the control group is fairly straightforward: patients receiving MV who did not have an IAC placed. However, there are important nuances when defining control groups. In our study example, an alternate control group could be all ICU patients who did not receive an IAC. However, the inclusion of patients not receiving MV results in a control group with a lower severity of illness and expected mortality than patients receiving MV, which would bias in favor of not using IACs. Careful definition of the control group is needed to properly interpret any conclusions from research; defining an appropriate control group is as important as defining the exposure.

9.3.3. Outcome

Finally, the investigator needs to determine the outcome of interest. Several different types of outcomes can be considered, including intermediate or mechanistic outcomes (informs etiological pathways, but may not immediately impact patients), patient-centered outcomes (informs outcomes important to patients, but may lack mechanistic insights: e.g. comfort scales, quality of life indices, or mortality), or healthcare-system centered outcomes (e.g. resource utilization, or costs). In our example of IAC use, several outcomes could be considered including intermediate outcomes (e.g. number of arterial blood draws, ventilator setting changes, or vasoactive medication changes), patient-centered outcomes (e.g. 28-day or 90-day mortality, adverse event rates), or healthcare utilization (e.g. hospitalization costs, added clinician workload). As shown in our example, outcome(s) may build upon each other to yield a constellation of findings that provides a more complete picture to address the clinical question of interest.

• “ In the population of interest ( study cohort ), is the exposure to the variable of interest associated with a different outcome than in the control group ? ”, which becomes, in our example:
• “ Among mechanically ventilated, adult ICU patients who are not receiving vasoactive medications (i.e., the study sample) is placement of an IAC after initiation of MV (as compared with not receiving an IAC) (i.e. the exposure and control patients) associated with improved 28 - day mortality rates (primary outcome, patient - centered) and the number of blood gas measurements per day (supporting secondary outcome, intermediate/mechanistic)? ”

9.4. Matching Study Design to the Research Question

Once the research question has been defined, the next step is to choose the optimal study design given the question and resources available. In biomedical research, the gold-standard for study design remains the double-blinded, randomized, placebo-controlled trial (RCT) [ 9 , 10 ]. In a RCT, patients with a given condition (e.g. all adults receiving MV) would be randomized to receive a drug or intervention of interest (e.g. IAC) or randomized to receive the control (e.g. no IAC), with careful measurement of pre-determined outcomes (e.g. 28-day mortality). In ideal conditions, the randomization process eliminates all measured and unmeasured confounding and allows for causal inferences to be drawn, which cannot generally be achieved without randomization. As shown above, confounding is a threat to valid inferences from study results. Alternatively, in our example of septic shock verses asthma, severity of illness associated with the underlying condition may represent another confounder. Randomization solely based on the exposure of interest attempts to suppress issues of confounding. In our examples, proper randomization in a large sample would theoretically create equal age distributions and equal numbers of patients with septic shock and asthma in both the exposure and the control group.

However, RCTs have several limitations. Although the theoretical underpinnings of RCTs are fairly simple, the complex logistics of patient enrollment and retention, informed consent, randomization, follow up, and blinding may result in RCTs deviating from the ‘ideal conditions’ necessary for unbiased, causal inference. Additionally, RCTs carry the highest potential for patient harm and require intensive monitoring because the study dictates what type of treatment a patient receives (rather than the doctor) and may deviate from routine care. Given the logistic complexity, RCTs are often time- and cost-intensive, frequently taking many years and millions of dollars to complete. Even when logistically feasible, RCTs often ‘weed out’ multiple groups of patients in order to minimize potential harms and maximize detection of associations between interventions and outcomes of interest. As a result, RCTs can consist of homogeneous patients meeting narrow criteria, which may reduce the external validity of the studies’ findings. Despite much effort and cost, an RCT may miss relevance to the clinical question as to whether the intervention of interest is helpful for your particular patient or not. Finally, some clinical questions may not ethically be answered with RCTs. For instance, the link between smoking and lung cancer has never been shown in a RCT, as it is unethical to randomize patients to start smoking in a smoking intervention group, or randomize patients to a control group in a trial to investigate the efficacy of parachutes [ 11 ]!

Observational research differs from RCTs. Observational studies are non-experimental; researchers record routine medical practice patterns and derive conclusions based on correlations and associations without active interventions [ 9 , 12 ]. Observational studies can be retrospective (based on data that has already been collected), prospective (data is actively collected over time), or ambi-directional (a mix). Unlike RCTs, researchers in observational studies have no role in deciding what types of treatments or interventions patients receive. Observational studies tend to be logistically less complicated than RCTs as there is no active intervention, no randomization, no data monitoring boards, and data is often collected retrospectively. As such, observational studies carry less risk of harm to patients (other than loss of confidentiality of data that has been collected) than RCTs, and tend to be less time- and cost-intensive. Retrospective databases like MIMIC-II [ 13 ] or the National Inpatient Sample [ 14 ] can also provide much larger study samples (tens of thousands in some instances) than could be enrolled in an RCT, thus providing larger statistical power. Additionally, broader study samples are often included in observational studies, leading to greater generalizability of the results to a wider range of patients (external validity). Finally, certain clinical questions that would be unethical to study in an RCT can be investigated with observational studies. For example, the link between lung cancer and tobacco use has been demonstrated with multiple large prospective epidemiological studies [ 15 , 16 ] and the life-saving effects of parachutes have been demonstrated mostly through the powers of observation.

Although logistically simpler than RCTs, the theoretical underpinnings of observational studies are generally more complex than RCTs. Obtaining causal estimates of the effect of a specific exposure on a specific outcome depends on the philosophical concept of the ‘counterfactual’ [ 17 ]. The counterfactual is the situation in which, all being equal, the same research subject at the same time would receive the exposure of interest and (the counterfactual) not receive the exposure of interest, with the same outcome measured in the exposed and unexposed research subject. Because we cannot create cloned research subjects in the real-world, we rely on creating groups of patients similar to the group that receives an intervention of interest. In the case of an ideal RCT with a large enough number of subjects, the randomization process used to select the intervention and control groups creates two alternate ‘universes’ of patients that will be similar except as related to the exposure of interest. Because observational studies cannot intervene on study subjects, observational studies create natural experiments in which the counterfactual group is defined by the investigator and by clinical processes occurring in the real-world. Importantly, real-world clinical processes often occur for a reason, and these reasons can cause deviation from counterfactual ideals in which exposed and unexposed study subjects differ in important ways. In short, observational studies may be more prone to bias (problems with internal validity) than RCTs due to difficulty obtaining the counterfactual control group.

Several types of biases have been identified in observational studies. Selection bias occurs when the process of selecting exposed and unexposed patients introduces a bias into the study. For example, the time between starting MV and receiving IAC may introduce a type of “survivor treatment selection bias” since patients who received IAC could not have died prior to receiving IACs. Information bias stems from mismeasurement or misclassification of certain variables. For retrospective studies, the data has already been collected and sometimes it is difficult to evaluate for errors in the data. Another major bias in observational studies is confounding. As stated, confounding occurs when a third variable is correlated with both the exposure and outcome. If the third variable is not taken into consideration, a spurious relationship between the exposure and outcome may be inferred. For example, smoking is an important confounder in several observational studies as it is associated with several other behaviors such as coffee and alcohol consumption. A study investigating the relationship between coffee consumption and incidence of lung cancer may conclude that individuals who drink more coffee have higher rates of lung cancer. However, as smoking is associated with both coffee consumption and lung cancer, it is confounder in the relationship between coffee consumption and lung cancer if unmeasured and unaccounted for in analysis. Several methods have been developed to attempt to address confounding in observational research such as adjusting for the confounder in regression equations if it is known and measured, matching cohorts by known confounders, and using instrumental variables—methods that will be explained in-depth in future chapters. Alternatively, one can restrict the study sample (e.g. excluding patients with shock from a study evaluating the utility of IACs). For these reasons, while powerful, an individual observational study can, at best, demonstrate associations and correlations and cannot prove causation. Over time, a cumulative sum of multiple high quality observational studies coupled with other mechanistic evidence can lead to causal conclusions, such as in the causal link currently accepted between smoking and lung cancer established by observational human studies and experimental trials in animals.

9.5. Types of Observational Research

There are multiple different types of questions that can be answered with observational research (Table  9.1 ). Epidemiological studies are one major type of observational research that focuses on the burden of disease in predefined populations. These types of studies often attempt to define incidence, prevalence, and risk factors for disease. Additionally, epidemiological studies also can investigate changes to healthcare or diseases over time. Epidemiological studies are the cornerstone of public health and can heavily influence policy decisions, resource allocation, and patient care. In the case of lung cancer, predefined groups of patients without lung cancer were monitored for years until some patients developed lung cancer. Researchers then compared numerous risk factors, like smoking, between those who did and did not develop lung cancer which led to the conclusion that smoking increased the risk of lung cancer [ 15 , 16 ].

Table 9.1

Major types of observational research, and their purpose

There are other types of epidemiological studies that are based on similar principles of observational research but differ in the types of questions posed. Predictive modeling studies develop models that are able to accurately predict future outcomes in specific groups of patients. In predictive studies, researchers define an outcome of interest (e.g. hospital mortality) and use data collected on patients such as labs, vital signs, and disease states to determine which factors contributed to the outcome. Researchers then validate the models developed from one group of patients in a separate group of patients. Predictive modeling studies developed many common prediction scores used in clinical practice such as the Framingham Cardiovascular Risk Score [ 18 ], APACHE IV [ 19 ], SAPS II [ 20 ], and SOFA [ 21 ].

Comparative effectiveness research is another form of observational research which involves the comparison of existing healthcare interventions in order to determine effective methods to deliver healthcare. Unlike descriptive epidemiologic studies, comparative effectiveness research compares outcomes between similar patients who received different treatments in order to assess which intervention may be associated with superior outcomes in real-world conditions. This could involve comparing drug A to drug B or could involve comparing one intervention to a control group who did not receive that intervention. Given that there are often underlying reasons why one patient received treatment A versus B or an intervention versus no intervention, comparative effectiveness studies must meticulously account for potential confounding factors. In the case of IACs, the research question comparing patients who had an IAC placed to those who did not have an IAC placed would represent a comparative effectiveness study.

Pharmacovigilance studies are yet another form of observational research. As many drug and device trials end after 1 or 2 years, observational methods are used to evaluate if there are patterns of rarer adverse events occurring in the long-term. Phase IV clinical studies are one form of pharmacovigilance studies in which long-term information related to efficacy and harm are gathered after the drug has been approved.

9.6. Choosing the Right Database

A critical part of the research process is deciding what types of data are needed to answer the research question. Administrative/claims data, secondary use of clinical trial data, prospective epidemiologic studies, and electronic health record (EHR) systems (both from individual institutions and those pooled from multiple institutions) are several sources from which databases can be built. Administrative or claims databases, such as the National Inpatient Sample and State Inpatient Databases complied by the Healthcare Cost and Utilization Project or the Medicare database, contain information on patient and hospital demographics as well as billing and procedure codes. Several techniques have been developed to translate these billing and procedure codes to more clinically useful disease descriptions. Administrative databases tend to provide very large sample sizes and, in some cases, can be representative of an entire population. However, they lack granular patient-level data from the hospitalization such as vital signs, laboratory and microbiology data, timing data (such as duration of MV or days with an IAC) or pharmacology data, which are often important in dealing with possible confounders.

Another common source of data for observational research is large epidemiologic studies like the Framingham Heart Study as well as large multicenter RCTs such as the NIH ARDS Network. Data that has already been can be analyzed retrospectively with new research questions in mind. As the original data was collected for research purposes, these types of databases often have detailed, granular information not available in other clinical databases. However, researchers are often bound by the scope of data collection from the original research study which limits the questions that may be posed. Importantly, generalizability may be limited in data from trials.

The advent of Electronic Health Records (EHR) has resulted in the digitization of medical records from their prior paper format. The resulting digitized medical records present opportunities to overcome some of the shortcomings of administrative data, yielding granular data with laboratory results, medications, and timing of clinical events [ 13 ]. These “big databases” take advantage of the fact many EHRs collect data from a variety of sources such as patient monitors, laboratory systems, and pharmacy systems and coalesce them into one system for clinicians. This information can then be translated into de-identified databases for research purposes that contain detailed patient demographics, billing and procedure information, timing data, hospital outcomes data, as well as patient-level granular data and provider notes which can searched using natural language processing tools. “Big data” approaches may attenuate confounding by providing detailed information needed to assess severity of illness (such as lab results and vital signs). Furthermore, the granular nature of the data can provide insight as to the reason why one patient received an intervention and another did not which can partly address confounding by indication. Thus, the promise of “big data” is that it contains small, very detailed data. “Big data” databases, such as MIMIC-III, have the potential to expand the scope of what had previously been possible with observational research.

9.7. Putting It Together

Fewer than 10 % of clinical decisions are supported by high level evidence [ 22 ]. Clinical questions arise approximately in every other patient [ 23 ] and provide a large cache of research questions. When formulating a research question, investigators must carefully select the appropriate sample of subjects, exposure variable, outcome variable, and confounding variables. Once the research question is clear, study design becomes the next pivotal step. While RCTs are the gold standard for establishing causal inference under ideal conditions, they are not always practical, cost-effective, ethical or even possible for some types of questions. Observational research presents an alternative to performing RCTs, but is often limited in causal inference by unmeasured confounding.

Our clinical scenario gave rise to the question of whether IACs improved the outcomes of patients receiving MV. This translated into the research question: “Among mechanically ventilated ICU patients not receiving vasoactive medications (study sample) is use of an IAC after initiation of MV (exposure) associated with improved 28-day mortality (outcome)?” While an RCT could answer this question, it would be logistically complex, costly, and difficult. Using comparative effectiveness techniques, one can pose the question using a granular retrospective database comparing patients who received an IAC to measurably similar patients who did not have an IAC placed. However, careful attention must be paid to unmeasured confounding by indication as to why some patients received IAC and others did not. Factors such as severity of illness, etiology of respiratory failure, and presence of certain diseases that make IAC placement difficult (such as peripheral arterial disease) may be considered as possible confounders of the association between IAC and mortality. While an administrative database could be used, it could lack important information related to possible confounders. As such, EHR databases like MIMIC-III, with detailed granular patient-level data, may allow for measurement of a greater number of previously unmeasured confounding variables and allow for greater attenuation of bias in observational research.

Take Home Messages

• Most research questions arise from clinical scenarios in which the proper course of treatment is unclear or unknown.
• Defining a research question requires careful consideration of the optimal study sample, exposure, and outcome in order to answer a clinical question of interest.
• While observational research studies can overcome many of the limitations of randomized controlled trials, careful consideration of study design and database selection is needed to address bias and confounding.

Open Access    This chapter is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License ( http://creativecommons.org/licenses/by-nc/4.0/ ), which permits any noncommercial use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, a link is provided to the Creative Commons license and any changes made are indicated.

The images or other third party material in this chapter are included in the work’s Creative Commons license, unless indicated otherwise in the credit line; if such material is not included in the work’s Creative Commons license and the respective action is not permitted by statutory regulation, users will need to obtain permission from the license holder to duplicate, adapt or reproduce the material.

• Cite this Page Mehta A, Malley B, Walkey A. Formulating the Research Question. 2016 Sep 10. In: Secondary Analysis of Electronic Health Records [Internet]. Cham (CH): Springer; 2016. Chapter 9. doi: 10.1007/978-3-319-43742-2_9
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In this Page

• Introduction
• The Clinical Scenario: Impact of Indwelling Arterial Catheters
• Turning Clinical Questions into Research Questions
• Matching Study Design to the Research Question
• Types of Observational Research
• Choosing the Right Database
• Putting It Together

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Try one of these tried and true clinical or quantitative research question frameworks. Not sure where to start? PICO is the most common clinical question framework. and PEO works well for public health and epidemiology.

• Condition, Context, Population
• Aromataris, E., & Munn, Z. (2017). Joanna Briggs Institute reviewer's manual. The Joanna Briggs Institute. Available from JBI Manual for Evidence Synthesis .
• Population, Exposure of Interest, Outcome or Response
• Population or Problem, Intervention or Exposure, Comparison or Control, Outcome
• Heneghan, C., & Badenoch, D. (2002). Evidence-based medicine toolkit. London: BMJ Books. https://www.worldcat.org/title/evidence-based-medicine-toolkit/oclc/62307845
• Population or Problem, Intervention or Exposure, Comparison or Control, Outcome, Study Type
• Methley, A. M., Campbell, S., Chew-Graham, C., McNally, R., & Cheraghi-Sohi, S. (2014). PICO, PICOS and SPIDER: a comparison study of specificity and sensitivity in three search tools for qualitative systematic reviews. BMC health services research, 14, 579. https://doi.org/10.1186/s12913-014-0579-0 .
• Population or Problem, Intervention or Exposure, Comparison or Control, Outcome, Time
• Richardson, W. S., Wilson, M. C., Nishikawa, J., & Hayward, R. S. (1995). The well-built clinical question: A key to evidence-based decisions. ACP Journal Club, 123(3), A12-A12. https://pubmed.ncbi.nlm.nih.gov/7582737/
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