Selection of Comparator Group in Observational Drug Safety Studies: Alternatives to the Active Comparator New User Design. 2022

Viktor Wintzell, and Henrik Svanström, and Björn Pasternak
From the Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden.

A valid study design is essential when assessing the safety of drugs based on observational data. The comparator group is a key element of the design and can greatly influence the results. The active comparator new user design is a go-to design in observational drug safety research where a target trial of initiation of a study drug versus usual care is emulated. A comparison with another treatment that targets similar patients as the study drug and has no effect on the outcome has great potential to reduce bias. However, the active comparator new user design can be difficult to implement because no suitable comparator drug is available or because it requires extensive exclusion of study drug initiators. In this analysis, we evaluated alternative study designs that can be used in drug safety assessments when the active comparator new user design is not optimal. Using target trial emulation as a common framework, we defined and evaluated the following designs: traditional no use, no-use episodes, active comparator new user, prevalent new user, generalized prevalent new user, and hierarchical prevalent new user. We showed that all designs can be implemented by using sequential cohorts and simply altering the patient selection criteria, i.e., identifying increasingly restrictive cohorts. In this way, all designs are nested in each other and the differences between them can be demonstrated clearly. We concluded that many study-specific factors need to be considered when choosing a design, including indication, available comparator drugs, treatment patterns, potential effect modification, and sample size.

UI MeSH Term Description Entries
D012107 Research Design A plan for collecting and utilizing data so that desired information can be obtained with sufficient precision or so that an hypothesis can be tested properly. Experimental Design,Data Adjustment,Data Reporting,Design, Experimental,Designs, Experimental,Error Sources,Experimental Designs,Matched Groups,Methodology, Research,Problem Formulation,Research Methodology,Research Proposal,Research Strategy,Research Technics,Research Techniques,Scoring Methods,Adjustment, Data,Adjustments, Data,Data Adjustments,Design, Research,Designs, Research,Error Source,Formulation, Problem,Formulations, Problem,Group, Matched,Groups, Matched,Matched Group,Method, Scoring,Methods, Scoring,Problem Formulations,Proposal, Research,Proposals, Research,Reporting, Data,Research Designs,Research Proposals,Research Strategies,Research Technic,Research Technique,Scoring Method,Source, Error,Sources, Error,Strategies, Research,Strategy, Research,Technic, Research,Technics, Research,Technique, Research,Techniques, Research
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D015982 Bias Any deviation of results or inferences from the truth, or processes leading to such deviation. Bias can result from several sources: one-sided or systematic variations in measurement from the true value (systematic error); flaws in study design; deviation of inferences, interpretations, or analyses based on flawed data or data collection; etc. There is no sense of prejudice or subjectivity implied in the assessment of bias under these conditions. Aggregation Bias,Bias, Aggregation,Bias, Ecological,Bias, Statistical,Bias, Systematic,Ecological Bias,Outcome Measurement Errors,Statistical Bias,Systematic Bias,Bias, Epidemiologic,Biases,Biases, Ecological,Biases, Statistical,Ecological Biases,Ecological Fallacies,Ecological Fallacy,Epidemiologic Biases,Experimental Bias,Fallacies, Ecological,Fallacy, Ecological,Scientific Bias,Statistical Biases,Truncation Bias,Truncation Biases,Bias, Experimental,Bias, Scientific,Bias, Truncation,Biase, Epidemiologic,Biases, Epidemiologic,Biases, Truncation,Epidemiologic Biase,Error, Outcome Measurement,Errors, Outcome Measurement,Outcome Measurement Error
D018401 Sample Size The number of units (persons, animals, patients, specified circumstances, etc.) in a population to be studied. The sample size should be big enough to have a high likelihood of detecting a true difference between two groups. (From Wassertheil-Smoller, Biostatistics and Epidemiology, 1990, p95) Sample Sizes,Size, Sample,Sizes, Sample

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