title: Competing risks with missing covariates: effect of haplotypematch on hematopoietic cell transplant patients.
abstract: 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.
authors.affiliation: Department of Biostatistics, University of Copenhagen, Copenhagen, Denmark. ts@biostat.ku.dk
authors.forename: Thomas H
authors.initials: TH
authors.lastname: Scheike
authors.forename: Martin J
authors.initials: MJ
authors.lastname: Maiers
authors.forename: Vanderson
authors.initials: V
authors.lastname: Rocha
authors.forename: Mei-Jie
authors.initials: MJ
authors.lastname: Zhang
mesh_terms: D006086:Graft vs Host Disease / Q000209:etiology; D006680:HLA Antigens / Q000235:genetics; D006239:Haplotypes; D018380:Hematopoietic Stem Cell Transplantation / Q000009:adverse effects* / Q000401:mortality; D006650:Histocompatibility Testing; D006801:Humans; D015993:Life Tables; D015233:Models, Statistical; D016016:Proportional Hazards Models; D012044:Regression Analysis; D012307:Risk Factors
chemical_list: D006680:HLA Antigens