Competing risks with missing covariates: effect of haplotypematch on hematopoietic cell transplant patients
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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.
KeywordsBinomial modeling Bone marrow transplant Competing risks Haplotype effects Haplotype match Missing covariates Inverse-censoring probability weighting Nonparametric effects Non-proportionality Regression effects
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- Eapen M, Rubinstein P, Zhang M-J, Stevens C, Kurtzberg J, Scaradavaou A, Loberiza FRECR, Klein JP, Horowitz MM, Wagner JE (2007) Outcomes of transplantation of unrelated donor umbilical cord blood and bone marrow in children with acute leukaemia: a comparison study. Lancet 369: 1947–1954CrossRefGoogle Scholar
- Excoffier L, Slatkin M (1995) Maximum-likelihood estimation of polecular haplotype frequenceis in a deiploid population. Mol Biol Evol 12: 921–927Google Scholar
- Hawley M, Kidd K (1995) Haplo: a program using the EM algorithm to estimate the frequencies of multi-site haplotypes. J Hered 86: 409–411Google Scholar
- Long J, Williams R, Urbanek M (1995) An EM algorithm and testing strategy for multi-locus haplotypes. Am J Hum Genet 56: 799–810Google Scholar