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).