Illustration of missing data handling technique generated from hepatitis C induced hepatocellular carcinoma cohort study

作     者：Jose, Jesna Vishwakarma, Gajendra K. Bhattacharjee, Atanu 

作者机构：Indian Inst Technol Dhanbad Dept Math Comp Dhanbad 826004 Bihar India Tata Mem Hosp Ctr Canc Epidemiol Sect Biostat Navi Mumbai India Homi Bhabha Natl Inst Mumbai Maharashtra India 

出 版 物：《JOURNAL OF KING SAUD UNIVERSITY SCIENCE》 (沙特国王大学学报：科学)

年 卷 期：2021年第33卷第4期

页      面：101403-101403页

核心收录：

学科分类：07[理学] 08[工学] 

基　　金：Council of Scientific and Industrial Research  Government of India 

主　　题：EM algorithm Regression method Predictive mean matching Imputation Handling missing data 

摘      要：Background and Objectives: Missing outcome data are a common occurrence for most clinical research trials. The complete case analysis is a widely adopted method to tackle with missing observations. However, it reduced the sample size of the study and thus have an impact on statistical power. Hence every effort should be made to reduce the amount of missing data. The objective of this work is to provide the application of different analytical tools to handle missing data imputation techniques through illustration. Methods: We used Imputation techniques such as EM algorithm, MCMC, Regression, and Predictive Mean matching methods and compared the results on hepatitis C virus-induced hepatocellular carcinoma (HCV-HCC) data. The statistical models by Generalized Estimating Equations, Time-dependent Cox Regression, and Joint Modeling were applied to obtain the statistical inference on imputed data. The missing data handling technique compatible with Principle Component Analysis (PCA) was found suitable to work with high dimensional data. Results: Joint modelling provides a slightly lower standard error than other analytical methods each imputation. Accordingly, to our methodology, Joint Modeling analysis with the EM algorithm imputation method has appeared to be the most appropriate method with HCV-HCC data. However, Generalized Estimating Equations and Time-dependent Cox Regression methods were relatively easy to run. Conclusion: The multiple imputation methods are efficient to provide inference with missing data. It is technically robust than any ad hoc approach to working with missing data. (c) 2021 Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).