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 well 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.
翻译:联合建模技术是最近有效分析病人纵向历史并发生相关事件的一个进步,该程序在生物标志研究中成功实施,对父母进行肿瘤的发生进行检查。影响必要推断的典型问题之一是在纵向反应和共变中存在缺失的数值。由于病人退出研究,失踪的发生率非常常见。本文章提供了处理共变和响应变量中缺失值的有效和详细方法。本研究报告讨论了不同多重估算技术对在估算数据集时采用联合建模的推论的影响。进行了模拟研究,复制复杂的数据结构,并方便地进行了分析,以显示其在参数估计方面的功效。这一分析还进一步通过用R编程语言评估生物标志者研究的纵向和生存结果而得到说明。