|Title||Efficient Estimation of Semiparametric Transformation Models for Two-Phase Cohort Studies.|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||Zeng, Donglin, and D Y. Lin|
|Journal||J Am Stat Assoc|
|Date Published||2014 Jan 01|
Under two-phase cohort designs, such as case-cohort and nested case-control sampling, information on observed event times, event indicators, and inexpensive covariates is collected in the first phase, and the first-phase information is used to select subjects for measurements of expensive covariates in the second phase; inexpensive covariates are also used in the data analysis to control for confounding and to evaluate interactions. This paper provides efficient estimation of semiparametric transformation models for such designs, accommodating both discrete and continuous covariates and allowing inexpensive and expensive covariates to be correlated. The estimation is based on the maximization of a modified nonparametric likelihood function through a generalization of the expectation-maximization algorithm. The resulting estimators are shown to be consistent, asymptotically normal and asymptotically efficient with easily estimated variances. Simulation studies demonstrate that the asymptotic approximations are accurate in practical situations. Empirical data from Wilms' tumor studies and the Atherosclerosis Risk in Communities (ARIC) study are presented.
|Alternate Journal||J Am Stat Assoc|
|Original Publication||Efficient estimation of semiparametric transformation models for two-phase cohort studies.|
|PubMed Central ID||PMC3960088|
|Grant List||R01 CA082659 / CA / NCI NIH HHS / United States |
R37 GM047845 / GM / NIGMS NIH HHS / United States
R37 AI029168 / AI / NIAID NIH HHS / United States
R01 AI029168 / AI / NIAID NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
Efficient Estimation of Semiparametric Transformation Models for Two-Phase Cohort Studies.