Showing papers by "Ross L. Prentice published in 1995"

Estimating equations for hazard ratio parameters based on correlated failure time data

Abstract: SUMMARY Weighted partial likelihood estimating equations are proposed for the estimation of marginal hazard ratio parameters based on correlated failure time data. Asymptotic distribution theory is derived for the solution to such equations using martingale convergence results and inverse function theory. Simulation studies and theoretical efficiency calculations indicate that the inclusion of weights in the estimating equation produces important efficiency gains only if the dependencies among the failure times are strong.

Aggregate data studies of disease risk factors

Abstract: SUMMARY Statistical methods are proposed for estimating relative rate parameters, based on estimated disease rates and covariate data from random samples of individuals from each of several cohorts. A random effects model is used to derive mean and variance models for estimated disease rates. Estimating equations for relative rate parameters are then developed by replacing cohort covariate averages by corresponding sample averages. The asymptotic distribution of regression parameter estimates is derived, and the asymptotic bias is shown to be small, even if covariates are contaminated by classical random measurement errors, provided the covariate sample size in each cohort is not small. Simulation studies, motivated by international data on diet and breast cancer, provide insights into the properties of the proposed estimators.

On the reliability and precision of within- and between- population estimates of relative rate parameters.

Abstract: Comparisons of individual- and aggregate-level analyses of data from a multigroup observational study are made using an exponential form relative rate model. Stratified, analytic random effects, and aggregate random effects analyses are studied. Estimating equations are developed to give a consistent estimation procedure across analyses and corresponding information matrices are compared. Simulation studies provide insight into the efficiency and bias of relative rate parameter estimates with respect to covariate dispersion, confounding, and covariate measurement error.

Design issues in cohort studies.

Abstract: Some basic features of cohort studies are reviewed along with a brief discussion of relative risk estimation procedures. This background, and a discussion of factors influencing cohort study power, provides a context to examine various cohort study design choices, including the choice of study population, the selection of cohort size and follow-up duration, cohort ascertainment and subsampling choices, and decisions concerning validation and reliability substudies. It is noted that confounding issues, and especially issues related to measurement error in the assessment of primary exposure and confounding variables, may have a major influence on the precision and reliability of cohort study analyses. A brief discussion is provided of the role of cohort studies in relation to other observational and experimental research strategies.