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Correlation analysis for longitudinal data: applications to HIV and psychosocial research
Author(s) -
Tu X. M.,
Feng C.,
Kowalski J.,
Tang W.,
Wang H.,
Wan C.,
Ma Y.
Publication year - 2007
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.2857
Subject(s) - missing data , psychosocial , correlation , longitudinal data , computer science , inference , econometrics , longitudinal study , statistics , data mining , data science , psychology , machine learning , mathematics , artificial intelligence , psychiatry , geometry
Correlation analysis is widely used in biomedical and psychosocial research for assessing rater reliability, precision of diagnosis and accuracy of proxy outcomes. The popularity of longitudinal study designs has propelled the proliferation in recent years of new methods for longitudinal and other multi‐level clustered data designs, such as the mixed‐effect models and generalized estimating equations. Despite these advances, research and methodological development on addressing missing data for correlation analysis is woefully lacking. In this paper, we consider non‐parametric inference for the product–moment correlation within a longitudinal data setting and address missing data under both the missing completely at random and missing at random assumptions. We illustrate the approach with real study data in mental health and HIV prevention research. Copyright © 2007 John Wiley & Sons, Ltd.