Accounting for confounding bias and heterogeneity in comparative effectiveness

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DESCRIPTION (provided by applicant): There is increased interest in comparative effectiveness research as a way of informing clinicians, payers, and policymakers about the relative effectiveness of different treatments with the goal of maximizing benefit to patients and value to payers. Although, randomized controlled trials (RCTs) are currently relied upon to provide information on efficacy of new treatments they are often a poor source of data on comparative effectiveness. RCTs typically enroll carefully selected populations that are not representative of all individuals using the new treatments and often compare a new treatment only with placebo rather than with other commonly-used treatments for the same conditions. Other data sources, including clinical registries developed by professional societies and large administrative databases, provide an opportunity to investigate treatment efficacy and safety in a variety of clinical settings beyond those used for RCTs. However, with these observational data comes the problem of confounding bias from uncontrolled selection into treatment. Thus, improved methods are needed to address the limitations of currently available comparative effectiveness data, including non-representative populations enrolled in RCTs, lack of comparisons between commonly-used treatments in RCTs, and confounding bias in observational studies. Such problems are common across a variety of areas of medicine. This proposal seeks to develop novel and generalizable methods for addressing these problems. Specifically, we will develop novel approaches for combining data from randomized trials, registries and/or claims-based data (taking advantage of the strengths of both RCT and observational data); extend the latest techniques for instrumental variable analysis; and develop novel simultaneous equation models to account for confounding that are less sensitive to assumptions than currently-used methods. In so doing, we will apply these methods to three important clinical examples: treatments for bipolar disorder for patients with psychiatric comorbidity, reformulations of existing psychiatric drug treatments, and the surgical repair for abdominal aortic aneurysm (AAA). PUBLIC HEALTH RELEVANCE: New methods are developed to address the limitations of currently available comparative effectiveness analysis, including non-representative populations enrolled in randomized controlled trials, lack of comparisons between commonly-used treatments in RCTs, and confounding bias in observational studies. Such problems are common across a variety of areas of medicine including mental health, surgery, cancer, and medical devices. The proposed methods will help to better inform clinicians, payers, and policymakers about the relative effectiveness of different treatments with the goal of maximizing benefit to patients and value to payers.