title: Comparison of case-cohort estimators based on data on premature death of adult adoptees.
abstract: A case-cohort sample of adoptees was collected to investigate genetic and environmental influences on premature death, which motivated us to supplement existing simulation results to explore the performance of various estimators proposed for case-cohort samples of survival data. We studied six regression coefficients estimators, which differ with regard to the weighting scheme used in a pseudo-likelihood function, and two different estimators of their variances. Compared to earlier simulation studies, we changed the following conditions: type of explanatory variable, the distribution of lifetimes, and the percentage of deaths in the full cohort. The latter condition affected the performance of the estimated variances of the regression coefficients, where we found a systematic bias of the estimator, proposed by Self and Prentice, dependent on the percentages of deaths. This dependence of percentages of death was different for different sizes of case-cohort studies. A robust variance estimator showed a better overall performance. The estimators of regression coefficients compared did not differ much, the estimators proposed by Kalbfleisch and Lawless and by Prentice performing very well. Results of the case-cohort data of adoptees were not in conflict with earlier findings of a moderate genetic influence on premature death in adulthood.
authors.affiliation: Danish Epidemiology Science Centre at the Institute of Preventive Medicine, Copenhagen University Hospital, DK-1399 Copenhagen K, Denmark. lp@ipm.hosp.dk
authors.forename: Liselotte
authors.initials: L
authors.lastname: Petersen
authors.forename: Thorkild I A
authors.initials: TI
authors.lastname: Sørensen
authors.forename: Per Kragh
authors.initials: PK
authors.lastname: Andersen
mesh_terms: D000300:Adoption; D000328:Adult; D015331:Cohort Studies; D003718:Denmark; D004777:Environment; D005823:Genetics; D006801:Humans; D009026:Mortality; D016016:Proportional Hazards Models; D016019:Survival Analysis