Assessment of Treatment Effects in High-Dimensional, Routine Care Claims Data

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DESCRIPTION (provided by applicant): Data on the comparative effectiveness of medications are limited because few head-to-head trials are available and most of them do not represent the general population or real-world practice. Comparative effectiveness research using non-randomized healthcare data can provide critical evidence on the effectiveness and safety of medications and procedures in routine care. While such studies in large electronic healthcare databases can provide expedited and less costly evidence on drug effects in routine care, the conventional confounding adjustment methods that rely on a small number of investigator-specified confounders often fail to produce unbiased results. In recent years three novel methodologies have shown promise to overcome these limitations. Their performance, however, has never been compared with each other in real world comparative effectiveness studies: 1) Combine claims data studies with detailed clinical surveys in subpopulations and use this information for improved confounding control through Propensity Score Calibration (PSC). 2) Empirically identify and prioritize very large numbers of potential confounders and adjusting for them using propensity score methods, called high-dimensional propensity score adjustment (hd-PS). 3) Identify a quasi-random process in the healthcare system that influences treatment choice beyond patient characteristics, e.g. prescriber preference, and apply instrumental variable analysis (IVA). Although long know to economists, IVA is fairly new to comparative effectiveness research. One in three American adults has a cardiovascular condition and the total inpatient cost for such conditions approximates one fourth of the total cost of hospital care in the US. The lack of good comparative effectiveness information is a significant limitation for improving care. The performance of the 3 novel approaches will be tested in three cardiovascular example studies, including (a) Vytorin vs. statin use alone, (b) high vs. low intensity statin therapy after MI, and (c) short and medium-term effectiveness of anticoagulation therapies during percutaneous coronary interventions. Specifically, we will: Aim 1: Implement three novel approaches to improved confounding control in comparative effectiveness research using relevant cardiovascular example studies, Aim 2: Compare performance of the three approaches and improve their implementation, Aim 3: Disseminate methods and provide internet support for free analysis software and result libraries. This project will meaningfully improve methodologies for comparative effectiveness research in cardiovascular medicine using a wide array of healthcare databases. After completion of this project a library of validated algorithms will be available on an interactive web-portal that supports applications and is a depository of supplemental results. PUBLIC HEALTH RELEVANCE: The use of longitudinal healthcare databases is a potentially powerful tool to evaluate the comparative effectiveness of cardiovascular medications as used in routine care. However, conventional confounder adjustment methods that rely on a limited number of investigator-specific covariates often fail to produce unbiased results. We will implement and rigorously evaluate three novel analytic methods to enhance causal interpretation of the effectiveness and safety of commonly used medications.