Abstract

Joint modeling technique is a recent advancement in effectively analyzing the longitudinal history of patients with the occurrence of an event of interest attached to it. This procedure is successfully implemented in biomarker studies to examine parents with the occurrence of tumor. One of the typical problem that influences the necessary inference is the presence of missing values in the longitudinal responses as wel l as in covariates. The occurrence of missingness is very common due to the dropout of patients from the study. This article presents an effective and detailed way to handle the missing values in the covariates and response variable. This study discusses the effect of different multiple imputation techniques on the inferences of joint modeling implemented on imputed datasets. A simulation study is carried out to replicate the complex data structures and conveniently perform our analysis to show its efficacy in terms of parameter estimation. This analysis is further illustrated with the longitudinal and survival outcomes of biomarkers’ study by assessing proper codes in R programming language.

摘要

联合建模技术是有效地分析患者的纵向病史并伴随有感兴趣事件发生的最新进展。该程序已成功用于生物标志物研究中，以检查是否有肿瘤发生的父母。影响必要推论的典型问题之一是纵向响应中存在缺失值，与协变量一样。由于患者退出研究，失踪的发生非常普遍。本文提出了一种有效而详细的方法来处理协变量和响应变量中的缺失值。本研究讨论了不同的多重插补技术对在插补数据集上实现的联合建模推论的影响。进行了模拟研究以复制复杂的数据结构，并方便地执行我们的分析以显示其在参数估计方面的功效。通过评估R编程语言中的适当代码，可以用生物标志物研究的纵向和生存结果进一步说明这一分析。