title: Survival analysis with incomplete genetic data.
abstract: Genetic data are now collected frequently in clinical studies and epidemiological cohort studies. For a large study, it may be prohibitively expensive to genotype all study subjects, especially with the next-generation sequencing technology. Two-phase sampling, such as case-cohort and nested case-control sampling, is cost-effective in such settings but entails considerable analysis challenges, especially if efficient estimators are desired. Another type of missing data arises when the investigators are interested in the haplotypes or the genetic markers that are not on the genotyping platform used for the current study. Valid and efficient analysis of such missing data is also interesting and challenging. This article provides an overview of these issues and outlines some directions for future research.
authors.affiliation: Department of Biostatistics, University of North Carolina, CB#7420, Chapel Hill, NC, 27599-7420, USA, lin@bios.unc.edu.
authors.forename: D Y
authors.initials: DY
authors.lastname: Lin
mesh_terms: D016022:Case-Control Studies; D015331:Cohort Studies; D003627:Data Interpretation, Statistical; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study / Q000379:methods; D006239:Haplotypes / Q000235:genetics*; D006801:Humans; D016013:Likelihood Functions; D016016:Proportional Hazards Models