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Statistical inference for the additive hazards model under outcome‐dependent sampling
Author(s) -
Yu Jichang,
Liu Yanyan,
Sandler Dale P.,
Zhou Haibo
Publication year - 2015
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.11257
Subject(s) - outcome (game theory) , inference , statistical inference , statistical model , statistics , computer science , proportional hazards model , econometrics , mathematics , artificial intelligence , mathematical economics
Cost‐effective study designs and proper inference procedures for data from such designs are always of particular interest to study investigators. In this article, we propose a biased sampling scheme: an outcome‐dependent sampling (ODS) design for survival data with right censoring under the additive hazards model. We develop a weighted pseudo‐score estimator for the regression parameters for the proposed design and derive the asymptotic properties of the proposed estimator. We also provide some suggestions for using the proposed method by evaluating the relative efficiency of the proposed method against simple random sampling design and derive the optimal allocation of the subsamples for the proposed design. Simulation studies show that the proposed ODS design is more powerful than other existing designs and the proposed estimator is more efficient than other estimators. We apply our method to analyze a cancer study conducted at NIEHS, the Cancer Incidence and Mortality of Uranium Miners Study, to study the risk of radon exposure to cancer. The Canadian Journal of Statistics 43: 436–453; 2015 © 2015 Statistical Society of Canada