Mixture cure models have been developed as an effective tool to analyze failure time data with a cure fraction. Used in conjunction with the logistic regression model, this model allows covariate-adjusted inference of an exposure effect on the cured probability and the hazard of failure for the uncured subjects. However, the covariate-adjusted inference for the overall exposure effect is not directly provided. In this paper, we describe a Cox proportional hazards cure model to analyze interval-censored survival data in the presence of a cured fraction and then apply a post-estimation approach by using model-predicted estimates difference to assess the overall exposure effect on the restricted mean survival time scale. For baseline hazard/survival function estimation, simple parametric models as fractional polynomials or restricted cubic splines are utilized to approximate the baseline logarithm cumulative hazard function, or, alternatively, the full likelihood is specified through a piecewise linear approximation for the cumulative baseline hazard function. Simulation studies were conducted to demonstrate the unbiasedness of both estimation methods for the overall exposure effect estimates over various baseline hazard distribution shapes. The methods are applied to analyze the interval-censored relapse time data from a smoking cessation study.

中文翻译：

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具有区间截尾生存数据的 Cox 比例风险治愈模型的暴露评估

混合物固化模型已被开发为一种有效的工具，用于分析具有固化分数的失效时间数据。该模型与逻辑回归模型结合使用，允许对未治愈受试者的治愈概率和失败风险的暴露效应进行协变量调整推断。然而，没有直接提供整体暴露效应的协变量调整推断。在本文中，我们描述了一个 Cox 比例风险治愈模型，用于在存在治愈分数的情况下分析区间截尾生存数据，然后通过使用模型预测的估计差异来应用后估计方法来评估总体暴露对受限的影响平均生存时间尺度。对于基线危险/生存函数估计，使用分数多项式或受限三次样条等简单参数模型来近似基线对数累积风险函数，或者，通过累积基线风险函数的分段线性近似来指定完全似然。进行了模拟研究，以证明两种估计方法对各种基线危害分布形状的总体暴露效应估计的无偏性。这些方法用于分析来自戒烟研究的间隔删失复发时间数据。进行了模拟研究，以证明两种估计方法对各种基线危害分布形状的总体暴露效应估计的无偏性。这些方法用于分析来自戒烟研究的间隔删失复发时间数据。进行了模拟研究，以证明两种估计方法对各种基线危害分布形状的总体暴露效应估计的无偏性。这些方法用于分析来自戒烟研究的间隔删失复发时间数据。