ArticleLifetime data analysis2013
Competing risks with missing covariates: effect of haplotypematch on hematopoietic cell transplant patients.
Article in Lifetime data analysis, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Risk factors of CVD mortality among the elderly in Beijing, 1992 - 2009: an 18-year cohort study.International journal of environmental research and public health · 2014Article
- Risk factors for cerebrovascular disease mortality among the elderly in Beijing: a competing risk analysis.PloS one · 2014Article
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4 authors.
Funding
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.
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