Developing new methods for comparing treatments in case-control studies / Goodarz Danaei.

Format:

Book

Author/Creator:

Danaei, Goodarz, author.

Language:

English

Subjects (All):

Clinical Trials as Topic.

Physical Description:

1 online resource (1 PDF file (84 pages)) : illustrations

Place of Publication:

Washington (DC) : Patient-Centered Outcomes Research Institute (PCORI), 2021.

Summary:

BACKGROUND: Comparative effectiveness research (CER) and observational analyses of safety and off-target effects of drugs lie at the heart of patient-centered outcomes research. Emulating the design and analysis of a target trial using a cohort design (causal CER methods) reduces potential for several major biases in observational CER. Large cohort studies often use electronic medical record (EMR) data without validating key variables due to feasibility constraints. Using a case-control design provides the opportunity to reduce measurement bias by validating measures of eligibility, exposure, confounders, and outcome using resource-intensive data collection methods such as medical record review within a much smaller population compared with the entire cohort. However, causal CER methods for case-control studies do not yet exist, leaving researchers to choose between using either advanced analytical methods with potentially lower-quality data from a cohort design or a conventional case-control design with higher-quality data. OBJECTIVES: To reduce bias in CER by developing methods to emulate the design and analysis of a target trial using high-quality measurements and a case-control design. METHODS: We developed a guideline and an analytical program to emulate the design and analysis of a target trial using a case-control design. The guideline provides details on how the data should be structured and how to use the accompanying SAS macro to implement the observational analogues of intention-to-treat (ITT) and per-protocol analyses in a case-control design. We conducted 2 sets of interviews with 20 investigators and data analysts and incorporated their comments into the guideline. For our clinical example, we used cohort data from Kaiser Permanente Washington and a case-control study (nested within the same system) that had previously validated case and control status using medical record reviews. We emulated the design and analysis of a target trial of statin therapy among healthy participants. The primary outcome was fatal or nonfatal myocardial infarction (MI). RESULTS: Using a cohort design to emulate a primary prevention statin trial, we selected 76 020 eligible participants into an analytic cohort. During an average follow-up of 73 months, we observed 1318 events. Compared with a pooled hazard ratio (HR) of 0.69 (95% CI, 0.60-0.79) reported in a meta-analysis of randomized controlled trials, the observational analogue of the ITT HR using a cohort design and outcomes based on ICD codes was 1.01 (0.91-1.12), and the ITT odds ratio (OR) using a case-control design and validated case and control status was 0.80 (0.69-0.92). A case-control analysis that measured eligibility, exposures, and confounders at the index date yielded an adjusted OR of 1.12 (0.96-1.31), which suggests a harmful effect of statins on MI. Adherence to assigned treatment in our study population was low, with 70% of initiators discontinuing treatment within 5 years. After adjusting for imperfect adherence by censoring nonadherent person-times and adjusting for time-varying determinants of adherence via inverse probability weighting, the per-protocol HR was 0.80 (0.67-0.94) in the cohort design using ICD codes and the corresponding OR was 0.71 (0.58-0.87) in the case-control design using validated cases and controls. CONCLUSIONS: We were able to replicate the results of statin trials on MI prevention using a causal case-control design and outcome data that were validated using medical record reviews. In contrast, a cohort design that did not use validated outcomes produced results due to measurement error and lower adherence, and a conventional case-control design with validated events yielded substantially biased results due to a different definition of eligibility and exposure and inappropriate adjustment for confounders. LIMITATIONS: The causal analysis of case-control data requires that the case-control study be nested in a health care system with longitudinal EMR data, which does not apply to many existing studies. Our analyses used only 1 example; further research is needed to examine whether these findings generalize to other drug exposures and health outcomes.Includes bibliographical references.

Notes:

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