Regression modeling with missing outcomes : competing risks and longitudinal data
Regression modeling with missing outcomes : competing risks and longitudinal data
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发表时间：
2013-12
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通讯作者：
M. Betancur
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作者：
M. Betancur
缺失的数据是在回归模型中的常见，缺少结果限制了我们对医学兴趣的协变量的推论，这些效果是描述整个计划结果的分布的人。从观察到的数据中得出推论的任何方法的有效性都将要求一些导致丢失结果的机制的假设（1976，Biometrika，63：581-592）称为MARS（随机失踪） ”）如果丢失结果的概率不取决于在观察到的数据上进行调节时的结果，而MNAR（对于“不随机丢失”），则此区别对建模要求的重要含义是从可用的数据，但通常不可能从这些数据中评估MAR或MNAR的丢失机制。多变量数据，其中的结果是在可能缺少某些组件的向量中，MAR方法广泛可用并越来越多地使用，另一方面也提出了几种MNAR建模策略。开发，这仍然是研究的活跃领域。每个人都可以在某个时间点上掉落，因此所有后续结果都缺失了。敏感性参数是通过随机临床试验来提示该方法的数量。基于现有的理论，以开发出竞争失败原因的竞争风险数据的方法。功能，例如特异性危害（CSH）和累积事件功能（CIF），通常认为失败的原因是所有患者的知名度，但并非总是如此。已经提出了MAR的假设，尤其是对于CSH的半参数建模，但其他有用的模型很少受到关注，并且在这种情况下从未考虑过MNAR建模和灵敏度分析方法。在MAR下，CIF使用反向概率加权和多个插补的想法。敏感性分析。拟议方法的实际价值。
Missing data are a common occurrence in medical studies. In regression modeling, missing outcomes limit our capability to draw inferences about the covariate effects of medical interest, which are those describing the distribution of the entire set of planned outcomes. In addition to losing precision, the validity of any method used to draw inferences from the observed data will require that some assumption about the mechanism leading to missing outcomes holds. Rubin (1976, Biometrika, 63:581-592) called the missingness mechanism MAR (for “missing at random”) if the probability of an outcome being missing does not depend on missing outcomes when conditioning on the observed data, and MNAR (for “missing not at random”) otherwise. This distinction has important implications regarding the modeling requirements to draw valid inferences from the available data, but generally it is not possible to assess from these data whether the missingness mechanism is MAR or MNAR. Hence, sensitivity analyses should be routinely performed to assess the robustness of inferences to assumptions about the missingness mechanism. In the field of incomplete multivariate data, in which the outcomes are gathered in a vector for which some components may be missing, MAR methods are widely available and increasingly used, and several MNAR modeling strategies have also been proposed. On the other hand, although some sensitivity analysis methodology has been developed, this is still an active area of research. The first aim of this dissertation was to develop a sensitivity analysis approach for continuous longitudinal data with drop-outs, that is, continuous outcomes that are ordered in time and completely observed for each individual up to a certain time-point, at which the individual drops-out so that all the subsequent outcomes are missing. The proposed approach consists in assessing the inferences obtained across a family of MNAR pattern-mixture models indexed by a so-called sensitivity parameter that quantifies the departure from MAR. The approach was prompted by a randomized clinical trial investigating the benefits of a treatment for sleep-maintenance insomnia, from which 22% of the individuals had dropped-out before the study end. The second aim was to build on the existing theory for incomplete multivariate data to develop methods for competing risks data with missing causes of failure. The competing risks model is an extension of the standard survival analysis model in which failures from different causes are distinguished. Strategies for modeling competing risks functionals, such as the cause-specific hazards (CSH) and the cumulative incidence function (CIF), generally assume that the cause of failure is known for all patients, but this is not always the case. Some methods for regression with missing causes under the MAR assumption have already been proposed, especially for semi-parametric modeling of the CSH. But other useful models have received little attention, and MNAR modeling and sensitivity analysis approaches have never been considered in this setting. We propose a general framework for semi-parametric regression modeling of the CIF under MAR using inverse probability weighting and multiple imputation ideas. Also under MAR, we propose a direct likelihood approach for parametric regression modeling of the CSH and the CIF. Furthermore, we consider MNAR pattern-mixture models in the context of sensitivity analyses. In the competing risks literature, a starting point for methodological developments for handling missing causes was a stage II breast cancer randomized clinical trial in which 23% of the deceased women had missing cause of death. We use these data to illustrate the practical value of the proposed approaches.