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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >SAS macros for estimation of direct adjusted cumulative incidence curves under proportional subdistribution hazards models.
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SAS macros for estimation of direct adjusted cumulative incidence curves under proportional subdistribution hazards models.

机译：SAS宏，用于在比例子分布危害模型下估算直接调整的累积发生率曲线。

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摘要

The cumulative incidence function is commonly reported in studies with competing risks. The aim of this paper is to compute the treatment-specific cumulative incidence functions, adjusting for potentially imbalanced prognostic factors among treatment groups. The underlying regression model considered in this study is the proportional hazards model for a subdistribution function [1]. We propose estimating the direct adjusted cumulative incidences for each treatment using the pooled samples as the reference population. We develop two SAS macros for estimating the direct adjusted cumulative incidence function for each treatment based on two regression models. One model assumes the constant subdistribution hazard ratios between the treatments and the alternative model allows each treatment to have its own baseline subdistribution hazard function. The macros compute the standard errors for the direct adjusted cumulative incidence estimates, as well as the standard errors for the differences of adjusted cumulative incidence functions between any two treatments. Based on the macros' output, one can assess treatment effects at predetermined time points. A real bone marrow transplant data example illustrates the practical utility of the SAS macros.
机译：累积发病率函数通常在具有竞争风险的研究中报告。本文的目的是计算特定于治疗的累积发生率函数，并针对治疗组之间可能不平衡的预后因素进行调整。在这项研究中考虑的基本回归模型是子分布函数的比例风险模型[1]。我们建议使用合并的样本作为参考人群来估计每种治疗的直接调整累积发生率。我们开发了两个SAS宏，用于基于两个回归模型来估算每种疗法的直接调整的累积发生率函数。一个模型假定处理之间的子分布危害比率恒定，而替代模型则允许每种处理具有自己的基准子分布危害函数。宏计算直接调整的累积发生率估算值的标准误差，以及任意两种治疗之间调整的累积发生率函数之差的标准误差。根据宏的输出，可以在预定时间点评估治疗效果。一个真实的骨髓移植数据示例说明了SAS宏的实用性。

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