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A residuals‐based transition model for longitudinal analysis with estimation in the presence of missing data
Author(s) -
KoruSengul Tulay,
Stoffer David S.,
Day Nancy L.
Publication year - 2006
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2757
Subject(s) - imputation (statistics) , missing data , covariate , econometrics , computer science , longitudinal data , statistics , regression , autoregressive model , data mining , mathematics
We propose a transition model for analysing data from complex longitudinal studies. Because missing values are practically unavoidable in large longitudinal studies, we also present a two‐stage imputation method for handling general patterns of missing values on both the outcome and the covariates by combining multiple imputation with stochastic regression imputation. Our model is a time‐varying autoregression on the past innovations (residuals), and it can be used in cases where general dynamics must be taken into account, and where the model selection is important. The entire estimation process was carried out using available procedures in statistical packages such as SAS and S‐PLUS. To illustrate the viability of the proposed model and the two‐stage imputation method, we analyse data collected in an epidemiological study that focused on various factors relating to childhood growth. Finally, we present a simulation study to investigate the behaviour of our two‐stage imputation procedure. Copyright © 2006 John Wiley & Sons, Ltd.