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