Competing risks with missing covariates: effect of haplotypematch on hematopoietic cell transplant patients. Lifetime Data Anal 2013 Jan;19(1):19-32
Date
09/13/2012Pubmed ID
22968448Pubmed Central ID
PMC3817559DOI
10.1007/s10985-012-9229-1Scopus ID
2-s2.0-84871355897 (requires institutional sign-in at Scopus site) 3 CitationsAbstract
In this paper we consider a problem from hematopoietic cell transplant (HCT) studies where there is interest on assessing the effect of haplotype match for donor and patient on the cumulative incidence function for a right censored competing risks data. For the HCT study, donor's and patient's genotype are fully observed and matched but their haplotypes are missing. In this paper we describe how to deal with missing covariates of each individual for competing risks data. We suggest a procedure for estimating the cumulative incidence functions for a flexible class of regression models when there are missing data, and establish the large sample properties. Small sample properties are investigated using simulations in a setting that mimics the motivating haplotype matching problem. The proposed approach is then applied to the HCT study.
Author List
Scheike TH, Maiers MJ, Rocha V, Zhang MJAuthor
Mei-Jie Zhang PhD Professor in the Institute for Health and Equity department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
Graft vs Host DiseaseHLA Antigens
Haplotypes
Hematopoietic Stem Cell Transplantation
Histocompatibility Testing
Humans
Life Tables
Models, Statistical
Proportional Hazards Models
Regression Analysis
Risk Factors
