Nonparametric tests for two-group comparisons of dependent observations obtained at varying time points.

TitleNonparametric tests for two-group comparisons of dependent observations obtained at varying time points.
Publication TypeJournal Article
Year of Publication2007
AuthorsMay S, Degruttola V
JournalBiometrics
Volume63
Issue1
Pagination194-200
Date Published2007 Mar
ISSN0006-341X
KeywordsAnalysis of Variance, Biometry, Computer Simulation, HIV Infections, HIV-1, Humans, Observer Variation, Statistics, Nonparametric, Viral Load
Abstract

We propose new tests for two-group comparisons of repeated measures of a response where the repeated measures might be obtained at arbitrary time points that differ over individuals. The tests are almost U-statistics in that the kernel contains some unknown parameters that need to be estimated from the data. Our methods are designed for settings in which response means of one group are strictly greater than the response means of the other group. The tests do not make any assumptions regarding the distribution of the repeated measures except that one of the tests assumes that the repeated measures can be grouped into distinct periods of observations (e.g., around fixed follow-up time points) such that the covariance between scores only depends on the periods the observations belong to and that the covariance matrices are the same in the two groups. The tests are valid even if the probability that a response is observed depends on the level of response provided that the missing data mechanism is the same in both groups. Inference can conveniently be based on resampling. We provide asymptotic results for the test statistics. We investigate size and power of the tests and use them to assess differences in viral load decline for drug-resistant and drug-sensitive human immunodeficiency virus (HIV)-1 infected patients.

DOI10.1111/j.1541-0420.2006.00676.x
Alternate JournalBiometrics
PubMed ID17447945
Grant List5 U01 AI043638 / AI / NIAID NIH HHS / United States
R01 AI 51164 / AI / NIAID NIH HHS / United States