Developing an Interactive Online Guide to Support the Use of Causal Inference Methods in Comparative Effectiveness Research

Abstract

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. What were the results?The research team created an online guide called CERBOT, which has five sections. Researchers enter information about the study they would like to do on the CERBOT website. Then CERBOT creates a report with information on how to design studies using information from health records and registries. It suggests what statistical methods to use and how to use them. What did the research team do?To design CERBOT, the research team asked for input from researchers, statisticians, and patients. The group suggested what the guide should do, how it should work, and how to make it easy to use. The research team also looked at other research studies designed using methods like those considered by CERBOT. With this information, the team created CERBOT and then tested how well it worked. What were the limits of the study?Researchers can use CERBOT for some study designs, but not for others.Future research could improve CERBOT. For example, researchers could add more features to it. The creators of CERBOT could improve the guide so that it works for additional study designs.

Conducting Real-world Evidence Studies on the Clinical Outcomes of Diabetes Treatments

“…Software platforms that are validated procedures to intake raw healthcare data, line them up longitudinally, allow transparent implementations of measurements, study designs, and statistical analyses have proven to reduces such ambiguity and providing the highest transparency possible in the study conduct ( 29 , 158 ). Software tools can support causal study designs by incorporating target trial thinking and provide guard rails to investigators so to avoid known design biases ( 159 ). Such commercially and publicly available software platforms are increasingly used and will contribute to high-quality RWE.…”

“…5 ) that should provide needed clarity in planning and evaluating studies ( 65 ). Tools were developed to help investigators design studies in transparent and logical ways ( 159 ), and there are comprehensive software platforms available that provide guardrails in the RWE study design process and provide complete transparency, including audit trails and reporting functions ( 29 , 162 ).…”

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“…The CERBOT tool has been developed to help researchers in this process and provides recommendations for the statistical analysis. 16 In the analysis phase, the use of censoring weights allows the assessment of balance over time between the study arms, thus ensuring internal validity. Reporting can also be more transparent with the use of the CONSORT checklist for randomized trials, including the CONSORT flow chart for patient selection.…”

“…The CERBOT tool (Comparative Effectiveness Research Based on Observational data to emulate a Target trial) is a useful tool to guide researchers in this process. 16 We also recommend the use of the CONSORT style flow diagram to report on patient selection. 17 Most items are standard to clinical trials but, when there is a delay between the start of follow-up and treatment initiation, the definitions of the treatment implementation and causal contrast are more complex.…”

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“…Zhang et al 46 describe an online tool called CERBOT that was developed to help implement trial emulation (http://cerbot.org/about). Further technical guidance is covered in a variety of papers 47,48 .…”