A general semiparametric Z-estimation approach for case-cohort studies.
Case-cohort design, an outcome-dependent sampling design for censored survival data, is increasingly used in biomedical research. The development of asymptotic theory for a case-cohort design in the current literature primarily relies on counting process stochastic integrals. Such an approach, however, is rather limited and lacks theoretical justification for outcome-dependent weighted methods due to non-predictability. Instead of stochastic integrals, we derive asymptotic properties for case-cohort studies based on a general Z-estimation theory for semi-parametric models with bundled parameters using empirical process theory. Both the Cox model and the additive hazards model with time-dependent covariates are considered.
Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029.
Bin
B
Nan
Department of Statistics, University of Washington, Seattle, WA 98195-4322.
Jon A
JA
Wellner
Additive hazards model; Cox model; Donsker class; Glivenko-Cantelli class; Z-estimation; bundled parameters; case-cohort study; empirical process; missing covariates; semiparametric estimation function