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Abstract Details

Activity Number: 275
Type: Topic Contributed
Date/Time: Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #306454
Title: Statistical Methods for Comparing Prostate Cancer Treatment Options Using the Seer-Medicare Cohort
Author(s): Dirk Moore*+ and Yong Lin and Weichung Joe Shih
Companies: University of Medicine and Dentistry of New Jersey School of Public Health and University of Medicine and Dentistry of New Jersey and University of Medicine and Dentistry of New Jersey
Address: 32 Tasley Ct, Robbinsville, NJ, 08691, United States
Keywords: prostate cancer ; instrumental variable analysis ; SEER-Medicare ; causal inference

Knowing which of competing treatment options is best for prostate cancer is of critical interest to patients and physicians Population-based cohorts such as the SEER-Medicare linked database provide a wealth of information on treatments and outcomes for prostate cancer, as well as a range of potential confounder covariates, allowing one to find associations between treatment and outcome. But with the advantages come special challenges. In this talk I will discuss the types of outcomes that one may use in comparing treatment options, and which are especially subject to confounding bias. For these outcomes, some confounders are only partially observed, making it difficult to infer causative relationships from observed associations. Instrumental variable analysis (IVA) is a method for observational data and that captures many of the advantages that randomized trials offer for inferring causation. In this talk I show how to construct a geographically-based instrumental variable in the SEER-Medicare database to compare the effectiveness, with regard to survival and comorbidity, of hormone therapy versus active surveillance in elderly prostate cancer patients. I also discuss other st

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