Correlation analysis for longitudinal data: Applications to HIV and psychosocial research

Xin M. Tu, C. Feng, J. Kowalski, W. Tang, H. Wang, C. Wan, Y. Ma

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)4116-4138
Number of pages23
JournalStatistics in Medicine
Issue number22
StatePublished - Sep 30 2007
Externally publishedYes


  • Intraclass correlation
  • Missing data
  • Monotone missing data patterns
  • Proxy outcomes
  • U-statistics

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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