Zaninotto, P;
sacker, A;
(2017)
Missing Data in Longitudinal Surveys: A Comparison of Performance of Modern Techniques.
Journal of Modern Applied Statistical Methods
, 16
(2)
pp. 378-402.
10.22237/jmasm/1509495600.
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Abstract
Using a simulation study, the performance of complete case analysis, full information maximum likelihood, multivariate normal imputation, multiple imputation by chained equations and two-fold fully conditional specification to handle missing data were compared in longitudinal surveys with continuous and binary outcomes, missing covariates, and an interaction term.
| Type: | Article |
|---|---|
| Title: | Missing Data in Longitudinal Surveys: A Comparison of Performance of Modern Techniques |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.22237/jmasm/1509495600 |
| Publisher version: | https://doi.org/10.22237/jmasm/1509495600 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Chained equations, longitudinal data, maximum likelihood, missing data, random intercepts, two-fold fully conditional specification |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health > Epidemiology and Public Health |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10039395 |
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