A Class of Semiparametric Mixture Cure Survival Models with Dependent Censoring.
A Class of Semiparametric Mixture Cure Survival Models with Dependent Censoring.
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DOI：
10.1198/jasa.2009.tm08033
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发表时间：
2009-09-01
影响因子：
3.7
通讯作者：
Tiwari RC
中科院分区：
文献类型：
--
作者：
Othus M;Li Y;Tiwari RC
现代癌症治疗大大提高了治愈率，并产生了极大的兴趣和需要适当的统计工具来分析生存数据与不可忽略的治愈分数。具有治愈分数的数据通常因相关删失而变得复杂，并且这类数据的分析通常涉及对删失机制和真实生存时间的依赖性的不可检验的参数假设。出于对前列腺癌生存趋势的分析，我们提出了一类半参数转换治愈模型，该模型允许依赖删失，而无需对依赖关系进行参数假设。所提出的模型类别包括潜伏期生存函数的一些常见模型，包括比例风险模型和比例优势模型，并且还允许时间依赖性协变量。采用逆截尾概率加权方案导出无偏估计方程。我们验证小样本特性与模拟和演示的数据应用程序的方法。
Modern cancer treatments have substantially improved cure rates and have generated a great interest in and need for proper statistical tools to analyze survival data with non-negligible cure fractions. Data with cure fractions are often complicated by dependent censoring, and the analysis of this type of data typically involves untestable parametric assumptions on the dependence of the censoring mechanism and the true survival times. Motivated by the analysis of prostate cancer survival trends, we propose a class of semiparametric transformation cure models that allows for dependent censoring without making parametric assumptions on the dependence relationship. The proposed class of models encompasses a number of common models for the latency survival function, including the proportional hazards model and the proportional odds model, and also allows for time-dependent covariates. An inverse censoring probability reweighting scheme is used to derive unbiased estimating equations. We validate small sample properties with simulations and demonstrate the method with a data application.
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影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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DOI：
10.1111/1467-9868.00410
发表时间：
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影响因子：
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