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Sample size formulae for trials comparing group and individual treatments in a multilevel model
Author(s) -
Moerbeek Mirjam,
Wong Weng Kee
Publication year - 2007
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/sim.3115
Subject(s) - sample size determination , treatment and control groups , research design , physical therapy , statistics , medicine , clinical trial , sample (material) , treatment effect , psychology , mathematics , chemistry , chromatography , traditional medicine
In disease screening and prevention trials, subjects in the experimental condition are frequently nested within therapy groups, whereas subjects in the control group receive individual or no therapy and are therefore not nested within groups. Outcomes of subjects within the same therapy group are expected to be more alike than outcomes of subjects within different therapy groups. Ignoring this dependency in the design stage may result in less powerful designs. This paper presents a multilevel model for analyzing such trials and sample size formulae for continuous and binary outcomes with unequal variances and costs across groups. The proposed optimal design ensures that there is adequate power to detect a treatment effect with either minimal cost or a minimal number of subjects. We apply our strategy and design an improved trial where all subjects with musculoskeletal pain received conventional therapy and subjects in the intervention arm participated in a group‐learning program. Copyright © 2007 John Wiley & Sons, Ltd.