The Role of Missing Data Imputation in Clinical Studies

Bibliographic Details
Main Author:	Peng, Zhimin
Language:	English
Published:	University of Cincinnati / OhioLINK 2018
Subjects:	Biostatistics missing data imputation Clinical Studies
Online Access:	http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535705430538222

id	ndltd-OhioLink-oai-etd.ohiolink.edu-ucin1535705430538222
record_format	oai_dc
spelling	ndltd-OhioLink-oai-etd.ohiolink.edu-ucin15357054305382222021-08-03T07:08:29Z The Role of Missing Data Imputation in Clinical Studies Peng, Zhimin Biostatistics missing data imputation Clinical Studies Missing data is a common problem in clinical study. Removing missing data with complete case analysis (CCA) could lower power and bias the statistical conclusion. A variety of approaches have been used to deal with missing data. Several basic imputation methods would be introduced in this study. With the datasets derived from Teen-Longitudinal Assessment of Bariatric Surgery (Teen-LABS) study, group mean imputation (Gmean), total mean imputation (Tmean), expectation-maximization (EM) Algorithm, Markov chain Monte Carlo (MCMC) and fully conditional specification (FCS) imputation methods will be compared. Mean absolute error (MAE), root mean squared error (RMSE), mean of parameter bias, standard error of parameter bias will be used as evaluation criteria. Our results suggest that FCS is the rigorous statistical procedure for rare event data with high missing rate and binary outcome, which deserves more application in practice. 2018 English text University of Cincinnati / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535705430538222 http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535705430538222 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection	NDLTD
language	English
sources	NDLTD
topic	Biostatistics missing data imputation Clinical Studies
spellingShingle	Biostatistics missing data imputation Clinical Studies Peng, Zhimin The Role of Missing Data Imputation in Clinical Studies
author	Peng, Zhimin
author_facet	Peng, Zhimin
author_sort	Peng, Zhimin
title	The Role of Missing Data Imputation in Clinical Studies
title_short	The Role of Missing Data Imputation in Clinical Studies
title_full	The Role of Missing Data Imputation in Clinical Studies
title_fullStr	The Role of Missing Data Imputation in Clinical Studies
title_full_unstemmed	The Role of Missing Data Imputation in Clinical Studies
title_sort	role of missing data imputation in clinical studies
publisher	University of Cincinnati / OhioLINK
publishDate	2018
url	http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535705430538222
work_keys_str_mv	AT pengzhimin theroleofmissingdataimputationinclinicalstudies AT pengzhimin roleofmissingdataimputationinclinicalstudies
_version_	1719454635674042368

The Role of Missing Data Imputation in Clinical Studies

Similar Items