A test of the missing data mechanism for repeated measures data


Journal Article

The occurrence of missing data is an often unavoidable consequence of repeated measures studies. Fortunately, multivariate general linear models such as growth curve models and linear mixed models with random effects have been well developed to analyze incomplete normally-distributed repeated measures data. Most statistical methods have assumed that the missing data occur at random. This assumption may include two types of missing data mechanism: missing completely at random (MCAR) and missing at random (MAR) in the sense of Rubin (1976). In this paper, we develop a test procedure for distinguishing these two types of missing data mechanism for incomplete normally-distributed repeated measures data. The proposed test is similar in spiril to the test of Park and Davis (1992) derived the test for incomplete normally-distribrlted repeated nxasurcs data using linear mixed models. while Park and Davis (1992) cleirved thr test for inconrplete repeatctl categorical data in the framework of Grizzle Starmer, and koch(1969). The proposed procedure can be applied easily to any other multivariate general linear model which allow for missing data. The test is illustrated using the hip-rcplacernent patient data from Crowder and Hand (1990). © 1993, Taylor & Francis Group, LLC. All rights reserved.

Full Text

Cited Authors

  • Park, T; Lee, S; Woolson, RF

Published Date

  • January 1, 1993

Published In

Volume / Issue

  • 22 / 10

Start / End Page

  • 2813 - 2829

Electronic International Standard Serial Number (EISSN)

  • 1532-415X

International Standard Serial Number (ISSN)

  • 0361-0926

Digital Object Identifier (DOI)

  • 10.1080/03610929308831187

Citation Source

  • Scopus