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1994-08-27
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34 lines
Document 0769
DOCN M9480769
TI Sample size estimation using repeated measurements on biomarkers as
outcomes.
DT 9410
AU Kirby AJ; Galai N; Munoz A; Department of Epidemiology, Johns Hopkins
School of Hygiene and; Public Health, Baltimore, Maryland.
SO Control Clin Trials. 1994 Jun;15(3):165-72. Unique Identifier : AIDSLINE
MED/94313870
AB The objectives of this paper are to (1) examine methods of using
longitudinal data in designing comparative trials and calculating sample
sizes or power and (2) show the effect of autocorrelation of repeated
measures on the assessment of sample sizes. A statistical model with a
simple regression structure for the mean trajectory of the longitudinal
data and a two-parameter model for the correlations of within-individual
observations given by corr(yt,yt+s) = gamma s theta is used. The methods
are illustrated by considering a two-group trial and investigating the
effect of different values of the correlation parameters, gamma and
theta on the sample size. The results show that taking account of the
autocorrelation structure of longitudinal data may lead to more
efficient designs. Specifically, the stronger the autocorrelation is,
the smaller the sample size that is required.
DE Acquired Immunodeficiency Syndrome/IMMUNOLOGY/PREVENTION & CONTROL
AIDS Vaccines/ADMINISTRATION & DOSAGE/IMMUNOLOGY Biological
Markers/*BLOOD Human HIV Seropositivity/IMMUNOLOGY/THERAPY
HIV-1/IMMUNOLOGY Leukocyte Count Longitudinal Studies Randomized
Controlled Trials/*STATISTICS & NUMER DATA *Sampling Studies Support,
U.S. Gov't, P.H.S. Treatment Outcome T4 Lymphocytes/IMMUNOLOGY
JOURNAL ARTICLE
SOURCE: National Library of Medicine. NOTICE: This material may be
protected by Copyright Law (Title 17, U.S.Code).