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 Medjournal_title
Statistics in medicineauthors
Tu XM,Feng C,Kowalski J,Tang W,Wang H,Wan C,Ma Ydoi
10.1002/sim.2857subject
Has Abstractpub_date
2007-09-30 00:00:00pages
4116-38issue
22eissn
0277-6715issn
1097-0258journal_volume
26pub_type
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