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Handling Missingness Value on Jointly Measured Time-Course and Time-to-event Data
Methodology · Submitted 7 Jan 2021
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim This article presents an effective and detailed way to handle the missing values in the covariates and response variable.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | 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. | background 1.00 | 0.05 | 0.04 | asserted 1.00 |
| s1 | This procedure is successfully implemented in biomarker studies to examine parents with the occurrence of tumor. | background 0.98 | 0.23 | 0.03 | asserted 1.00 |
| s2 | 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. | background 0.97 | 0.07 | 0.02 | asserted 1.00 |
| s3 | The occurrence of missingness is very common due to the dropout of patients from the study. | background 1.00 | 0.06 | 0.02 | asserted 1.00 |
| s4 | This article presents an effective and detailed way to handle the missing values in the covariates and response variable. | contribution 0.99 | 0.79 | 0.14 | asserted 1.00 |
| s5 | This study discusses the effect of different multiple imputation techniques on the inferences of joint modeling implemented on imputed datasets. | contribution 0.82 | 0.69 | 0.06 | asserted 0.77 |
| s6 | 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. | method 0.97 | 0.92 | 0.05 | asserted 0.99 |
| s7 | This analysis is further illustrated with the longitudinal and survival outcomes of biomarkers' study by assessing proper codes in R programming language. | method 0.79 | 0.86 | 0.07 | asserted 0.99 |
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The paper
Original 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 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.
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