Case-cohort sampling is a commonly used and efficient method for studying large cohorts. In many situations, some covariates are easily measured on all cohort subjects, and surrogate measurements of the expensive covariates also may be observed. In this paper, to make full use of the covariate data collected outside the case-cohort sample, we propose＇a class of weighted estimators with general time-varying weights for the additive hazards model, and the estimators are shown to be consistent and asymptotically normal. We also identify the estimator within this class that maximizes efficiency, and simulation studies show that the efficiency gains of the proposed estimator over the existing ones can be substantial in practical situations. A real example is provided.
Epidemiologic studies use outcome-dependent sampling （ODS） schemes where, in addition to a simple random sample, there are also a number of supplement samples that are collected based on outcome variable. ODS scheme is a cost-effective way to improve study efficiency. We develop a maximum semiparametric empirical likelihood estimation （MSELE） for data from a two-stage ODS scheme under the assumption that given covariate, the outcome follows a general linear model. The information of both validation samples and nonvalidation samples are used. What is more, we prove the asymptotic properties of the proposed MSELE.
A cost-effective sampling design is desirable in large cohort studies with a limited budget due to the high cost of measurements of primary exposure variables.The outcome-dependent sampling(ODS) designs enrich the observed sample by oversampling the regions of the underlying population that convey the most information about the exposure-response relationship.The generalized linear models(GLMs) are widely used in many fields,however,much less developments have been done with the GLMs for data from the ODS designs.We study how to fit the GLMs to data obtained by the original ODS design and the two-phase ODS design,respectively.The asymptotic properties of the proposed estimators are derived.A series of simulations are conducted to assess the finite-sample performance of the proposed estimators.Applications to a Wilms tumor study and an air quality study demonstrate the practicability of the proposed methods.
