AI Article Synopsis
- Propensity score matching is a popular technique in observational studies to analyze treatment effects, especially in medical research involving competing risks for patient outcomes.
- This study addresses the lack of existing research that combines propensity score matching with competing risk survival data, particularly when there are missing causes of failure.
- The authors provide guidelines for handling this data issue, test various methods for imputing missing information through simulations, and apply their findings to analyze the risk of a specific liver cancer in patients with chronic hepatitis B and C.
Article Abstract
Propensity score matching is widely used to determine the effects of treatments in observational studies. Competing risk survival data are common to medical research. However, there is a paucity of propensity score matching studies related to competing risk survival data with missing causes of failure. In this study, we provide guidelines for estimating the treatment effect on the cumulative incidence function when using propensity score matching on competing risk survival data with missing causes of failure. We examined the performances of different methods for imputing the data with missing causes. We then evaluated the gain from the missing cause imputation in an extensive simulation study and applied the proposed data imputation method to the data from a study on the risk of hepatocellular carcinoma in patients with chronic hepatitis B and chronic hepatitis C.
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| http://dx.doi.org/10.1177/09622802211037075 | DOI Listing |
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