Premium
Accounting for expected attrition in the planning of community intervention trials
Author(s) -
Taljaard Monica,
Donner Allan,
Klar Neil
Publication year - 2006
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.2733
Subject(s) - attrition , sample size determination , statistics , randomization , cohort , demography , population , cluster randomised controlled trial , sample (material) , cluster (spacecraft) , psychological intervention , standard error , econometrics , medicine , randomized controlled trial , mathematics , computer science , environmental health , surgery , chemistry , dentistry , chromatography , psychiatry , sociology , programming language
Trials in which intact communities are the units of randomization are increasingly being used to evaluate interventions which are more naturally administered at the community level, or when there is a substantial risk of treatment contamination. In this article we focus on the planning of community intervention trials in which k communities (for example, medical practices, worksites, or villages) are to be randomly allocated to each of an intervention and a control group, and fixed cohorts of m individuals enrolled in each community prior to randomization. Formulas to determine k or m may be obtained by adjusting standard sample size formulas to account for the intracluster correlation coefficient ρ . In the presence of individual‐level attrition however, observed cohort sizes are likely to vary. We show that conventional approaches of accounting for potential attrition, such as dividing standard sample size formulas by the anticipated follow‐up rate π or using the average anticipated cohort size m π , may, respectively, overestimate or underestimate the required sample size when cluster follow‐up rates are highly variable, and m or ρ are large. We present new sample size estimation formulas for the comparison of two means or two proportions, which appropriately account for variation among cluster follow‐up rates. These formulas are derived by specifying a model for the binary missingness indicators under the population‐averaged approach, assuming an exchangeable intracluster correlation coefficient, denoted by τ . To aid in the planning of future trials, we recommend that estimates for τ be reported in published community intervention trials. Copyright © 2006 John Wiley & Sons, Ltd.