Observational designs for real-world evidence studies.

In the era of evidence-based medicine, real-world evidence (RWE) studies have opened avenues to utilize real-world data (RWD) effectively for improving clinical decision-making. However, the transformation of RWD into a meaningful RWE can only be achieved when the researcher asks the right clinical question, selects the right RWD source for variables of interest, uses the right study design, and applies the right statistical analysis. The generated RWE needs to have internal as well as external validity to be actionable. The "fit-for-purpose" observational study designs include descriptive, case-control, cross-sectional, and cohort. This article focuses on the advantages and disadvantages including the inherent bias of each study design. The RWE study decision guide has also been provided to aid the selection of appropriate study designs. Copyright:

INTRODUCTION

The evolution of real-world evidence (RWE) over the past years has improved our understanding of disease characteristics as well as the safety and effectiveness of treatment in clinical practice. Well-designed RWE may complement the randomized controlled trials (RCTs) in clinical decision-making. Digitalization of health-care systems advances the availability and utilization of real-world data (RWD), such as electronic medical records, registries, and wearable devices, by researchers. The generated RWE needs to be internally valid and generalizable to an identifiable target population to be actionable.[1] Turning RWD into RWE involves the interplay of a design layer – study design appropriate for the research question; a measurement layer – transforming the patient-level data into the variables of exposure and outcome; and an analysis layer – deriving the estimate of effect.[1] We will discuss RWE studies with focus on observational designs and the selection of appropriate designs that guide investigators and reviewers of RWE.

REAL-WORLD EVIDENCE STUDY DESIGNS

RWE studies can be classified basis the assignment of intervention and requirement of the comparison group [Figure 1 and Table 1].[2]

Figure 1

Classification of real-world evidence study designs; R – retrospective, P – prospective

Table 1

The design and analysis time frame (relative to the study start or index date) of different types of real-world evidence studies

Retrospective	Present	Prospective
Noninterventional case-control study	Cross-sectional study	Noninterventional cohort study with primary data
Noninterventional cohort study with secondary data		Registry
Administrative or claims database study
Electronic health record study

In the observational study, the participants are not preassigned to exposure or an intervention. The choice of treatments is up to patients and their physicians.

In a retrospective observational study, both exposure and outcomes have already occurred. In subsequent sections, we shall discuss in detail about 3 retrospective observational study designs, namely cross-sectional, case-control, and cohort.

Cross-sectional studies

Cross-sectional studies involve the simultaneous assessment of exposure and outcome in a single group of patients at a specific point in time. They are typically used to assess the prevalence and infer the cause of conditions/outcomes.[2] The general design involves defining the target population, deriving a sample of that population, and defining the characteristics being studied. The definition of the condition and health characteristics under study should be standardized, reproducible, and feasible to apply on a large scale.[3]

Datta et al.[4] conducted a community-based descriptive cross-sectional study among individuals aged 30 years and above in urban field practice area of a tertiary care hospital. Pretested questionnaire was used to measure the prevalence of self-reported hypertension over the past 2 years.

Advantages

Relatively quick and inexpensive compared with RCTs[2]

Simultaneous assessment of a wide variety of exposures and outcomes[3]

The most efficient way to determine the prevalence of a condition.[2]

Disadvantages

As the data are collected at a single time point, a temporal relationship between the exposure and the outcome cannot be ascertained,[2] for example, if the cross-sectional study demonstrates an association between obesity and arthritis, for example, one cannot prove causal relationship whether arthritis led to obesity or the obesity caused arthritis or, whether some third factor caused them both.

Susceptible to selection bias, for example, patients who develop an outcome but die before the end of the study are not captured[2]

Unsuitable for studying rare diseases or for diseases of short duration due to low prevalence at a single point in time[3]

Often completed using questionnaires, which have inherent problems, including low response rates and susceptibility to various sources of bias.[2]

Case–control studies

Case–control studies are typically retrospective studies (”backward looking”) because the approach is to identify persons with the disease of interest and then look backward in time to identify factors that may have caused it [Figure 2].[3] Cases are the patients with the outcome of interest, and controls are matched groups of patients without this outcome derived from the same population. The exposure to potential causal variables such as risk factors and treatment are evaluated basis of the medical history to determine causality. Case–control studies have a longitudinal or temporal aspect to the data that cross-sectional studies do not.[3] To have the consistency of the associations and risk estimates between a case-control study and other types of studies, the three key assumptions need to be met:[3]

Figure 2

Design of case-control study

cases are representative of all the cases,

controls are representative of persons without the disease, and

data are collected similarly in cases and controls.

The case–control studies help answer the research question leading to hypothesis generation.[2]

Swain et al.[5] conducted a population-based case–control study. Cases (disease) were confirmed dengue fever patients tested through IgM method and hospitalized to any public or private health facilities of the state, and controls (no disease) were from same living area of cases matched with sex and age (±2 years) with no history of dengue, chikungunya or acute febrile illness in the last 1 year. A structured validated questionnaire was used to collect information regarding exposures such as environmental practices and travel history for each consenting participant.

Suitable for rare outcomes or those with a long latency between exposure and disease[2]

Simultaneous assessment of multiple etiologic factors or potential predictors of the specific outcome[2]

The sample size requirement for the same effect size would be higher for a cohort study than that for a case–control study. Further, the lower prevalence of disease may result in much higher sample size of a cohort study but may not have significant impact on that for a case–control study.[3]

The study typically involves a single outcome[2]

No information about the base population from which the cases are drawn; so incidence and prevalence cannot be estimated[3]

Bias [Table 2] may skew the estimate of association.[3]

Table 2

Bias in case–control studies

Type of Bias	Description of Bias
Volunteer bias or “healthy volunteer” effect	Significant differences in characteristics and behavior of study volunteers from those of nonvolunteers
Prevalence or incidence bias	Missing the subjects who experienced the outcome/exposure for a short duration or a fatal episode remotely in the past
Membership bias or “healthy worker” or “healthy migrant” effect	A specific group of people, for example, employed or migrant population, may systematically differ in quality of health from that of the general population; this bias can be controlled by taking controls from the same worker or migrant population
Diagnostic/exposure suspicion bias	Information about a subject’s disease status, such as the thromboembolic episode in a woman, influences both the intensity and the outcome of a search for exposure to a putative cause, such as the use of contraceptive pills
Recall bias	The cases may have better recall/memory of any possible exposure that could have caused their illness than the controls
Family information bias	A new case triggers the flow of information about exposures and illnesses within a family, for example, a rare familial condition that is never mentioned until a family member begins to demonstrate some of the same symptoms

Cohort studies

Cohort studies evaluate the association between a particular exposure or a risk factor and subsequent development of disease [Figure 3]. They are “prospective” (”forward looking”) in that exposure or risk factor information is collected about 2 cohorts (exposed and nonexposed), and then both cohorts are followed up to ascertain the development of the outcome. In concurrent cohort studies, people with or without exposures are identified at the initiation of the study and information is collected looking forward in time to identify disease outcomes. In nonconcurrent cohort studies (retrospective), exposure information has already been collected at some point in the past and participants are surveyed in the present to determine the presence or absence of disease. It has the advantage of providing long follow-ups without waiting for time to pass to obtain disease outcomes.

Figure 3

Concurrent and nonconcurrent cohort study design. Adapted from Johnson 2018[3]

Badyal et al.[6] conducted a prospective, observational, multicenter cohort study among health-care workers [HCW] (HCWs; likely to be exposed to COVID-19 cases) who were either taking or not taking prophylactic treatment (Exposure) for COVID-19 and assessed their COVID-19 positivity by reverse transcription-polymerase chain reaction (Disease).

Nested case–control study is a special study design that reduces most biases related to selection and data collection, which is typically seen in classic case–control studies, by selecting cases and controls from within a large-scale prospective cohort study. The biologic samples in the ongoing cohort study can be collected and stored until enough cases have accumulated to provide adequate study power. At that time, these baseline samples from the newly occurring cases can be thawed and measured, along with a comparison group of matched (or unmatched) controls, allowing a much more efficient approach to examining expensive or difficult-to-measure risk factors.[3]

Immortal time bias. Adapted from Franklin et al., 2017[7]

Nested case-cohort designs sample a priori so regardless of case status or time. This design is used for the analysis of “time to event/failure” in a large cohort where that event/failure is rare and enormous resources may be needed to ascertain covariate values. Nested case–cohort designs are more flexible than nested case–control designs when there are different choices for the time axis or several failure time outcomes.[3]

Find incident (new-onset) cases, rather than prevalent (existing) cases

Provide more information about the natural history of disease as well as direct estimates of incidence and relative risk

Firmly establish the temporal relationships between exposure and disease

Multiple disease outcomes can be studied in relationship to the exposure

Suitable to study a rare exposure, with exposed persons gathered at the beginning of the study.

Concurrent cohort design: Study duration may be exceedingly long, making difficult the maintenance of consistent study methods and enthusiasm of staff and participants

Follow-up of free-living populations may be expensive because people move or change their contact information and can be difficult to track

Requirement of large samples size and more so for rare diseases

Bias [Table 3] may skew the estimate of association[178]

Table 3

Bias in cohort studies

Type of Bias	Description of Bias
Selection bias	A systematic error in creating intervention groups, causing them to differ with respect to measured or unmeasured baseline characteristics, and ultimately prognosis
Adjustment for causal intermediates	Adjusting for variables on the causal pathway between treatment and outcome can result in biased estimation of both the total effect of treatment and the direct effect that is not mediated through the adjustment variables
Immortal person-time bias	Occur whenever information assessed during follow-up is used to determine a patient’s inclusion or exclusion in the study or treatment group assignmentFor example, when assessing a new drug vs. an old comparator drug, some cohort studies first identify all patients receiving the new drug to maximize the size of this group, and then identify patients receiving the old comparator drug who never receive the new drug, beginning follow-up at the initiation of the relevant treatment for each group [Figure 4]. Patients who survived ‘immortal time’ on an old drug were switched to the new drug, and selectively excluded from the comparator group, making the old drug appear worse
Depletion of susceptibles or “survivorship bias”	In the Nurses’ Health study, prevalent users of HRT were followed for outcomes and compared with nonusers. Because the HRT group included many patients who had been on treatment for several years, it effectively excluded cardiovascular events occurring shortly after therapy initiation, leaving a cohort of hormone users that were less susceptible to the outcome
Reverse causation	When an apparent association between treatment and outcome is because outcome status influences treatment choice, rather than treatment impacting the outcome.

HRT=Hormone therapy

Selection of right real-world evidence study design

A well-conducted RWE has both successfully replicated and predicted findings of RCTs. Transparency in the conduct and presentation of RWE studies is critical to allow reviewers to evaluate the study's validity and have confidence in their decision-making.[1]

The decision guide for selection of RWE study design (modified from the RWE Framework flow diagram developed by Xia et al. 2019[9]) has been provided in Table 4.

Table 4

Real-world evidence study decision guide

What is your research question?

What is the research area of interest?

Disease

Drug/device

Other

What is the setting of study conduct?

Routine practice

Altering of routine practice

What are the outcomes of interest?

Are the data of interest recorded in routine practice?

Primary data collection and need for randomization

Secondary data analysis

Hybrid

What is the directionality of data review and analysis?

Retrospective

Prospective

Hybrid

What is the appropriate RWE study design?

Case–control

Cohort

Cross-sectional

Pragmatic trial

RWE study question – PECO or PICO

Population

Exposure/intervention

Comparison

Outcome

Modified from the RWE Framework flow diagram developed by Xia et al. 2019. RWE=Real-world evidence, PECO=Population, Exposure, Comparator, Outcome, PICO=Population, Intervention, Comparator, Outcome

CONCLUSION

The digitization of medical informatics has opened new avenues for the conduct of retrospective RWE studies. Observational RWE studies are less expensive and can be conducted quickly compared to the RCTs. However, observational RWE studies can be used only to find associations between risk factors and outcomes, but alone they cannot establish causation. In addition, there are inherent biases and issues of confounding associated with the observational studies impacting the “internal validity” of these studies. Well-designed and conducted observational RWE studies with careful analysis and interpretation will overcome most of these challenges and may help in hypothesis generation or complement the RCTs.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.