Efficient estimation for the proportional hazards model with bivariate current status data.
We consider efficient estimation of regression and association parameters jointly for bivariate current status data with the marginal proportional hazards model. Current status data occur in many fields including demographical studies and tumorigenicity experiments and several approaches have been proposed for regression analysis of univariate current status data. We discuss bivariate current status data and propose an efficient score estimation approach for the problem. In the approach, the copula model is used for joint survival function with the survival times assumed to follow the proportional hazards model marginally. Simulation studies are performed to evaluate the proposed estimates and suggest that the approach works well in practical situations. A real life data application is provided for illustration.
Wang
Lianming
L
Biostatistics Branch, National Institute of Environmental Health Sciences, MD A3-03, P.O. Box 12233, Research Triangle Park, NC 27709, USA. wangl3@niehs.nih.gov
Sun
Jianguo
J
Tong
Xingwei
X
D000818:Animals; D002737:Chloroprene / Q000633:toxicity; D005260:Female; D008175:Lung Neoplasms / Q000139:chemically induced; D008297:Male; D051379:Mice; D008810:Mice, Inbred C57BL; D015233:Models, Statistical; D016016:Proportional Hazards Models; D051381:Rats; D011916:Rats, Inbred F344
D002737:Chloroprene