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Application of GEE procedures for sample size calculations in repeated measures experiments
Author(s) -
Rochon James
Publication year - 1998
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19980730)17:14<1643::aid-sim869>3.0.co;2-3
Subject(s) - generalized estimating equation , sample size determination , statistics , gee , mathematics , wald test , exponential family , outcome (game theory) , sample (material) , binary number , estimating equations , econometrics , statistical hypothesis testing , maximum likelihood , physics , arithmetic , mathematical economics , thermodynamics
Derivation of the minimum sample size is an important consideration in an applied research effort. When the outcome is measured at a single time point, sample size procedures are well known and widely applied. The corresponding situation for longitudinal designs, however, is less well developed. In this paper, we adapt the generalized estimating equation (GEE) approach of Liang and Zeger to sample size calculations for discrete and continuous outcome variables. The non‐central version of the Wald χ 2 test is considered. We use the damped exponential family of correlation structures described in Muñoz et al. for the ‘working’ correlation matrix among the repeated measures. We present a table of minimum sample sizes for binary outcomes, and discuss extensions that account for unequal allocation, staggered entry and loss to follow‐up. © 1998 John Wiley & Sons, Ltd.