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Activity Number: 244 - New Advances in the Analysis of Competing Risks Data and Interval Censored Data and Related Topics
Type: Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Lifetime Data Science Section
Abstract #304659
Title: Propensity Score Matching with Missing Causes of Failure: a Monte Carlo Study
Author(s): Seungbong Han*
Companies: Gachon University
Keywords: propensity score; propensity score matching; competing risk; Monte Carlo simulation study; missing cause

Propensity score matching is widely used to estimate the treatment effects in observational studies. Competing risks survival data are common in medical research. There is a paucity of propensity score matching study when competing risks survival data with missing causes are present. We provide guidance for estimating the treatment effect on cumulative incidence function when using propensity score matching on the competing risks survival data with missing causes. We examine the performance of different methods for imputing data of missing causes. We evaluate the gain of the missing cause imputation in an extensive simulation study. We analyze the data from a study on atrial fibrillation in middle-aged East Asian men.

Authors who are presenting talks have a * after their name.

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