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An imputation strategy for incomplete longitudinal ordinal data
Author(s) -
Demirtas Hakan,
Hedeker Donald
Publication year - 2008
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.3239
Subject(s) - imputation (statistics) , ordinal data , computer science , binary data , multivariate statistics , ordinal scale , missing data , statistics , binary number , ordinal regression , longitudinal data , econometrics , mathematics , data mining , artificial intelligence , arithmetic
A new quasi‐imputation strategy for correlated ordinal responses is proposed by borrowing ideas from random number generation. The essential idea is collapsing ordinal levels to binary ones and converting correlated binary outcomes to multivariate normal outcomes in a sensible way so that re‐conversion to the binary and then ordinal scale, after conducting multiple imputation, yields the original marginal distributions and correlations. This conversion process ensures that the correlations are transformed reasonably, which in turn allows us to take advantage of well‐developed imputation techniques for Gaussian outcomes. We use the phrase ‘quasi’ because the original observations are not guaranteed to be preserved. We present an application using a data set from psychiatric research. We conclude that the proposed method may be a promising tool for handling incomplete longitudinal or clustered ordinal outcomes. Copyright © 2008 John Wiley & Sons, Ltd.