Department of Epidemiology and Preventive Medicine, University of Maryland, Baltimore, MD, USA.
Stat Med. 2010 Sep 30;29(22):2282-96. doi: 10.1002/sim.3985.
We propose a semiparametric marginal modeling approach for longitudinal analysis of cohorts with data missing due to death and non-response to estimate regression parameters interpreted as conditioned on being alive. Our proposed method accommodates outcomes and time-dependent covariates that are missing not at random with non-monotone missingness patterns via inverse-probability weighting. Missing covariates are replaced by consistent estimates derived from a simultaneously solved inverse-probability-weighted estimating equation. Thus, we utilize data points with the observed outcomes and missing covariates beyond the estimated weights while avoiding numerical methods to integrate over missing covariates. The approach is applied to a cohort of elderly female hip fracture patients to estimate the prevalence of walking disability over time as a function of body composition, inflammation, and age.
我们提出了一种半参数边缘建模方法，用于对因死亡和对响应缺失而导致数据缺失的队列进行纵向分析，以估计回归参数，这些参数被解释为在存活条件下的参数。我们提出的方法通过逆概率加权来适应具有非单调缺失模式的非随机缺失的结局和时变协变量。缺失的协变量由同时解决的逆概率加权估计方程中得出的一致估计值所取代。因此，我们在避免使用数值方法对缺失协变量进行积分的同时，利用了超出估计权重的具有观察结局和缺失协变量的数据点。该方法应用于一组老年女性髋部骨折患者，以估计随着时间的推移，身体成分、炎症和年龄对行走障碍的患病率的影响。
