|Title||Statistical inference for the additive hazards model under outcome-dependent sampling.|
|Publication Type||Journal Article|
|Year of Publication||2015|
|Authors||Yu, Jichang, Yanyan Liu, Dale P. Sandler, and Haibo Zhou|
|Journal||Can J Stat|
|Date Published||2015 Sep|
Cost-effective study design and proper inference procedures for data from such designs are always of particular interests 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.
|Alternate Journal||Can J Stat|
|Original Publication||Statistical inference for the additive hazards model under outcome-dependent sampling.|
|PubMed Central ID||PMC4569173|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 ES021900 / ES / NIEHS NIH HHS / United States
Statistical inference for the additive hazards model under outcome-dependent sampling.