Semi-Parametric Methods for Competing Risks Data with Applications in Organ Transplantation.
- Resource Type
- Theses
- Authors
- Fan, Ludi
- Source
- Dissertation Abstracts International; Dissertation Abstract International; 75-01B(E).
- Subject
- Biology, Biostatistics
- Language
- English
Summary: The fourth chapter develops a multiple imputation method for competing risks data. For individuals who experienced a competing risk not-of-interest, we impute censoring times in order to create censoring-complete data. The subdistribution hazard regression model developed by Fine and Gray (1999) can then be applied to the censoring-complete data, without the need to use inverse weighting. For each of the proposed methods, large sample properties are derived and the finite-sample properties are evaluated using simulations. We apply each method to national kidney transplantation data from the Scientific Registry of Transplant Recipients.