With heightened interest in the identification of genetic loci that play a role in the causation of disease and interactions between genetic and environmental factors, a great deal of attention has been devoted to the estimation of relative risks for haplotypes in both case-control and cohort studies. Censored survival data stemming from cohort designs are commonly analyzed in genetic epidemiology studies. In such cases, the Cox proportional hazards regression model is typically employed to estimate the association between genetic factors and the time to some event of interest. Although semi-parametric maximum likelihood estimators have been developed for modeling of unobserved haplotypes, such methods are computational complex and are not currently available in standard statistical software packages. As such it would be of great interest to genetic epidemiologists if standard software could be used to estimate and draw valid inference for haplotype associations. In addition, because most retrospective genetic cohort studies sample cases first and controls from the relevant family, methods would ideally allow for valid estimation and inference when particular genotypes are over-sampled and hierarchical clustering exists. The goal of the research proposed here is to provide simple estimates of haplotype relative risks in the setting of censored survival data and to validate such methods via extensive simulation studies. In addition, we will consider the estimation of standard errors under hierarchical clustering and the impact of incorporating inverse probability of sampling weights for cohort studies where particular subgroups have been oversampled.
NIH ExPorter
NIH ExPorter