Moreno-Betancur Margarita, Rey Grégoire, Latouche Aurélien
Inserm Centre for research in Epidemiology and Population Health, Biostatistics Team, Villejuif, France.
Univ Paris-Sud, UMRS 1018, F-94807 Villejuif, France.
Biometrics. 2015 Jun;71(2):498-507. doi: 10.1111/biom.12295. Epub 2015 Mar 11.
Competing risks arise in the analysis of failure times when there is a distinction between different causes of failure. In many studies, it is difficult to obtain complete cause of failure information for all individuals. Thus, several authors have proposed strategies for semi-parametric modeling of competing risks when some causes of failure are missing under the missing at random (MAR) assumption. As many authors have stressed, while semi-parametric models are convenient, fully-parametric regression modeling of the cause-specific hazards (CSH) and cumulative incidence functions (CIF) may be of interest for prediction and is likely to contribute towards a fuller understanding of the time-dynamics of the competing risks mechanism. We propose a so-called "direct likelihood" approach for fitting fully-parametric regression models for these two functionals under MAR. The MAR assumption not being verifiable from the observed data, we propose an approach for performing sensitivity analyses to assess the robustness of inferences to departures from this assumption. The method relies on so-called "pattern-mixture models" from the missing data literature and was evaluated in a simulation study. This sensitivity analysis approach is applicable to various competing risks regression models (fully-parametric or semi-parametric, for the CSH or the CIF). We illustrate the proposed methods with the analysis of a breast cancer clinical trial, including suggestions for ad hoc graphical goodness-of-fit assessments under MAR.
当在失败原因之间存在区别时，竞争风险会出现在失败时间的分析中。在许多研究中，很难获取所有个体完整的失败原因信息。因此，一些作者提出了在随机缺失（MAR）假设下，当某些失败原因缺失时对竞争风险进行半参数建模的策略。正如许多作者所强调的，虽然半参数模型很方便，但特定原因风险（CSH）和累积发病率函数（CIF）的全参数回归建模对于预测可能很有意义，并且可能有助于更全面地理解竞争风险机制的时间动态。我们提出了一种所谓的“直接似然”方法，用于在MAR下为这两个函数拟合全参数回归模型。由于无法从观测数据中验证MAR假设，我们提出了一种进行敏感性分析的方法，以评估推断对于偏离该假设的稳健性。该方法依赖于缺失数据文献中所谓的“模式混合模型”，并在一项模拟研究中进行了评估。这种敏感性分析方法适用于各种竞争风险回归模型（全参数或半参数，用于CSH或CIF）。我们通过对一项乳腺癌临床试验的分析来说明所提出的方法，包括在MAR下进行特设图形拟合优度评估的建议。
