Correlation analysis for longitudinal data: applications to HIV and psychosocial research.

Abstract:

: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.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Tu XM,Feng C,Kowalski J,Tang W,Wang H,Wan C,Ma Y

doi

10.1002/sim.2857

subject

Has Abstract

pub_date

2007-09-30 00:00:00

pages

4116-38

issue

22

eissn

0277-6715

issn

1097-0258

journal_volume

26

pub_type

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