Professional Abstract

Objective

To develop a web-based interactive guide for (1) formulating a well-defined comparative effectiveness research (CER) question and study design using observational data and (2) selecting an appropriate causal inference analytical method

Study Design

When randomized controlled trials (RCTs) are not feasible for a specific research question, researchers can use observational data to emulate a hypothetical randomized trial, known as the target trial. Despite advances in causal inference methods for observational studies that emulate RCTs, these complex methods have been largely inaccessible to applied researchers or clinicians who work with observational data. Common challenges include how to implement causal inference study design principles and how to select suitable analytic methods based on specific questions and data. Options for analytical methods include g-methods, doubly robust methods, instrumental variables, propensity scoring, and standard conditioning methods.

To address this gap, the research team developed a web-based guide called Comparative Effectiveness Research Based on Observational Data to Emulate a Target Trial (CERBOT). The guide aids researchers using observational data for CER by explicitly specifying and emulating a target trial. CERBOT also helps researchers create a complete study design and select appropriate causal inference methods for analysis.

The research team convened an eight-member advisory committee of researchers, statisticians, patient representatives, and dissemination experts to provide input on CERBOT’s aim, scope, and functionality as well as approaches for making it accessible to researchers. The research team also conducted a targeted literature review on comparative effectiveness studies that applied the target trial framework. The team then qualitatively analyzed the committee’s input and results from the literature review to refine and finalize the conceptual framework for CERBOT.

Results

The completed conceptual framework defined two key steps for causal inference in CER using observational data. First, formulate a causal question by designing the ideal hypothetical RCT, the target trial. Then, emulate the design and analysis of the target trial using observational data.

The CERBOT guide takes users through five modules where they input information about their intended research. They provide eligibility criteria, outcomes, follow-up period, treatment strategies, and confounding variables. Upon completion of the modules, users receive a report with recommendations for an appropriate study design and analytical methods for causal inference.

Limitations

CERBOT requires additional formal evaluation and testing with researchers from various fields using different observational data to ensure content clarity, usefulness, and ease of use. Moreover, CERBOT may not be applicable under certain complex study design scenarios (e.g., multiple or complex treatment strategies, eligibility criteria met at multiple time points).

Conclusions and Relevance

CERBOT may provide researchers and clinicians with a user-friendly application to guide them in designing and analyzing CER using observational data to emulate RCTs. It may expand researchers’ abilities to perform causal inference using observational data and enable non-statisticians to use causal inference methods to explore the effects of treatments.

Future Research Needs

Researchers need to conduct further evaluation and testing and apply CERBOT in different and complex designs to assess its performance and adapt it to different settings.