Chen Bingshu E, Cook Richard J
National Cancer Institute of Canada, Queen's University, Kingston, ON, Canada K7L 3N6.
Lifetime Data Anal. 2009 Mar;15(1):41-58. doi: 10.1007/s10985-008-9091-3. Epub 2008 Jul 12.
In many clinical studies, subjects are at risk of experiencing more than one type of potentially recurrent event. In some situations, however, the occurrence of an event is observed, but the specific type is not determined. We consider the analysis of this type of incomplete data when the objectives are to summarize features of conditional intensity functions and associated treatment effects, and to study the association between different types of event. Here we describe a likelihood approach based on joint models for the multi-type recurrent events where parameter estimation is obtained from a Monte-Carlo EM algorithm. Simulation studies show that the proposed method gives unbiased estimators for regression coefficients and variance-covariance parameters, and the coverage probabilities of confidence intervals for regression coefficients are close to the nominal level. When the distribution of the frailty variable is misspecified, the method still provides estimators of the regression coefficients with good properties. The proposed method is applied to a motivating data set from an asthma study in which exacerbations were to be sub-typed by cellular analysis of sputum samples as eosinophilic or non-eosinophilic.
在许多临床研究中，受试者面临经历不止一种类型潜在复发事件的风险。然而，在某些情况下，虽观察到事件发生，但具体类型未确定。当目标是总结条件强度函数的特征及相关治疗效果，并研究不同类型事件之间的关联时，我们考虑对这类不完全数据进行分析。在此，我们描述一种基于多类型复发事件联合模型的似然方法，其中参数估计通过蒙特卡罗期望最大化（EM）算法获得。模拟研究表明，所提出的方法能给出回归系数和方差 - 协方差参数的无偏估计量，且回归系数置信区间的覆盖概率接近名义水平。当脆弱变量的分布设定错误时，该方法仍能提供具有良好性质的回归系数估计量。所提出的方法应用于一项哮喘研究的激励数据集，在该研究中，通过对痰液样本进行细胞分析将病情加重分为嗜酸性粒细胞性或非嗜酸性粒细胞性。
