期刊
BIOMETRICAL JOURNAL
卷 58, 期 6, 页码 1445-1464出版社
WILEY
DOI: 10.1002/bimj.201500035
关键词
Constrained optimization; Cox's proportional hazards model; Nonidentifiability; Nonlinear effects; Time-varying effects
资金
- Canadian Institutes for Health Research (CIHR) [81275]
- Natural Sciences and Engineering Research Council of Canada (NSERC) [228203]
- Fonds quebecois de la recherche sur la nature et les technologies (FQRNT)
Standard optimization algorithms for maximizing likelihood may not be applicable to the estimation of those flexible multivariable models that are nonlinear in their parameters. For applications where the model's structure permits separating estimation of mutually exclusive subsets of parameters into distinct steps, we propose the alternating conditional estimation (ACE) algorithm. We validate the algorithm, in simulations, for estimation of two flexible extensions of Cox's proportional hazards model where the standard maximum partial likelihood estimation does not apply, with simultaneous modeling of (1) nonlinear and time-dependent effects of continuous covariates on the hazard, and (2) nonlinear interaction and main effects of the same variable. We also apply the algorithm in real-life analyses to estimate nonlinear and time-dependent effects of prognostic factors for mortality in colon cancer. Analyses of both simulated and real-life data illustrate good statistical properties of the ACE algorithm and its ability to yield new potentially useful insights about the data structure.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据