DOI: 10.3886/icpsr39533.v1
title: Development of a Causal Inference Toolkit for Patient-Centered Outcomes Research [Methods Study], 2013-2018
title: Version 1

description: Comparative effectiveness research compares two or more treatments to see which one works better for which patients. One type of research study is a randomized controlled trial, or an RCT. In an RCT, the research team assigns patients to a treatment by chance.
Other types of studies use information from health records and registries. Registries store data about patients with a specific health problem. They often include information on how each patient responds to a treatment. Because researchers don't assign treatments by chance in such studies, differences in how patients respond to a treatment may be from the treatment or something else, such as a patient's age or the severity of their illness. In studies using registries and health records, researchers apply statistical approaches, called causal inference methods, to estimate how treatments work. At the same time, they look at other things that could affect results, like a patient's age.
Researchers can choose among many different causal inference methods. But they may have a hard time knowing which methods to use or how to use complex methods correctly. In this study, the research team made an interactive online guide for researchers. The guide, called CERBOT, helps researchers design studies and select these methods.
description: 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.
description: 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.
description: Datasets: 
DS1: Development of a Causal Inference Toolkit for Patient-Centered Outcomes Research [Methods Study], 2013-2018