1. Multiple imputation analysis for propensity score matching with missing causes of failure: An application to hepatocellular carcinoma data
- Author
-
Hui Zhang, Young-Suk Lim, Adin Cristian Andrei, Kam-Wah Tsui, Gi Ae Kim, and Seungbong Han
- Subjects
Statistics and Probability ,Oncology ,medicine.medical_specialty ,Matching (statistics) ,Carcinoma, Hepatocellular ,Epidemiology ,business.industry ,Liver Neoplasms ,Competing risks ,medicine.disease ,Causality ,Survival data ,Health Information Management ,Internal medicine ,Hepatocellular carcinoma ,Propensity score matching ,Humans ,Medicine ,Computer Simulation ,Observational study ,Propensity Score ,business - 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.
- Published
- 2021