Under the case-cohort design introduced by Prentice (1986), the covariate
histories are ascertained only for the subjects who experience the event
of interest (i.e., the cases) during the follow-up period and for a
relatively small random sample from the original cohort (i.e., the
subcohort). The case-cohort design has been widely used
in clinical and epidemiological studies to assess the effects of
covariates on failure times. Most statistical methods developed for the
case-cohort design use the proportional hazards model, and few methods allow for time-varying regression coefficients. In addition, most methods disregard data from subjects outside of the subcohort, which can result in inefficient
inference. Addressing these issues, this paper proposes an estimation
procedure for the semiparametric additive hazards model with
case-cohort/two-phase sampling data, which allows the effects of some
covariates to be time varying while specifying the effects of others to be
constant. An augmented inverse probability weighted estimation procedure
is proposed, which is more efficient than the widely adopted inverse
probability weighted complete-case estimation method. The asymptotic properties of the proposed estimators are established, and the finite-sample properties are examined through an extensive simulation study. The method is applied to analyze data from a preventive HIV vaccine efficacy trial.
This is a joint work with Xiyuan Qian, Qiong Shou, and Peter Gilbert.