Survival analysis using auxiliary variables via multiple imputation, with application to AIDS clinical trial data.

We develop an approach, based on multiple imputation, to using auxiliary variables to recover information from censored observations in survival analysis. We apply the approach to data from an AIDS clinical trial comparing ZDV and placebo, in which CD4 count is the time-dependent auxiliary variable. To facilitate imputation, a joint model is developed for the data, which includes a hierarchical change-point model for CD4 counts and a time-dependent proportional hazards model for the time to AIDS.

Ascertainment bias in rate ratio estimation from case-sibling control studies of variable age-at-onset diseases.

Motivated by a Finnish case-control study of early onset diabetes in which diabetic children are matched to sibling controls, we investigate ascertainment bias of the usual rate ratio estimator from case-control data under simplex complete ascertainment of families during a fixed interval of time. Analytic results indicate that the assumptions necessary for valid estimation are that the disease is rare and the factors under study are exchangeable--essentially that the covariate distribution does not depend on calendar time or birth order. Further, we found that the rare disease assumption could be dropped by restricting to cases that were diagnosed during the enrollment period of the study or including all cases but eliminating the proband as a control for non-enrollment-period cases.

Predictive value of prior Rose angina for myocardial infarction confirmation after emergency admissions.

The Rose Questionnaire, developed to facilitate screening for the presence of coronary artery disease, has shown good utility for white men and more variable utility among Latino, African-American, and female subjects. This study investigated its utility for prediction of outcome in patients with suspected myocardial infarction. A total of 1428 white, Latino, and African-American subjects completed questionnaires after emergency admission, which were correlated with diagnoses at the time of discharge from a public hospital and private hospital.

Detection of gene-environment interactions in joint segregation and linkage analysis.

We compare approaches for analysis of gene-environment (G x E) interaction, using segregation and joint segregation and linkage analyses of a quantitative trait. Analyses of triglyceride levels in a single large pedigree demonstrate the two methods and show evidence for a significant interaction (P=.015 when segregation analysis is used; P=.

Joint segregation and linkage analysis of a quantitative trait compared to separate analyses.

Our goal was to determine the degree to which joint segregation and linkage analysis leads to increased efficiency for estimating the recombination fraction and to greater power for detecting linkage, compared to separate analyses. We concentrated on the quantitative phenotype Q2 and analyzed linkage with a tightly linked marker, a loosely linked marker, and eight unlinked markers, the latter chosen to evaluate false positive rates. We considered both nuclear-family and extended-pedigree data, using the 200 replicates of each provided to GAW participants.