Matsouaka Roland A, Atem Folefac D
Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA.
Program for Comparative Effectiveness Methodology, Duke Clinical Research Institute, Durham, North Carolina, USA.
Stat Med. 2020 Nov 30;39(27):4001-4015. doi: 10.1002/sim.8704. Epub 2020 Aug 10.
In a longitudinal study, measures of key variables might be incomplete or partially recorded due to drop-out, loss to follow-up, or early termination of the study occurring before the advent of the event of interest. In this paper, we focus primarily on the implementation of a regression model with a randomly censored predictor. We examine, particularly, the use of inverse probability weighting methods in a generalized linear model (GLM), when the predictor of interest is right-censored, to adjust for censoring. To improve the performance of the complete-case analysis and prevent selection bias, we consider three different weighting schemes: inverse censoring probability weights, Kaplan-Meier weights, and Cox proportional hazards weights. We use Monte Carlo simulation studies to evaluate and compare the empirical properties of different weighting estimation methods. Finally, we apply these methods to the Framingham Heart Study data as an illustrative example to estimate the relationship between age of onset of a clinically diagnosed cardiovascular event and low-density lipoprotein among cigarette smokers.
在一项纵向研究中，由于失访、随访丢失或在感兴趣事件出现之前研究提前终止，关键变量的测量可能不完整或部分记录缺失。在本文中，我们主要关注具有随机删失预测变量的回归模型的实施。特别地，当感兴趣的预测变量被右删失时，我们研究在广义线性模型（GLM）中使用逆概率加权方法来调整删失。为了提高完全病例分析的性能并防止选择偏倚，我们考虑三种不同的加权方案：逆删失概率权重、Kaplan-Meier权重和Cox比例风险权重。我们使用蒙特卡罗模拟研究来评估和比较不同加权估计方法的实证性质。最后，我们将这些方法应用于弗雷明汉心脏研究数据作为一个示例，以估计临床诊断心血管事件的发病年龄与吸烟者中低密度脂蛋白之间的关系。
