  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.
