Sample size and power calculations for open cohort longitudinal cluster randomized trials
Autor: | Andrew Copas, Andrew Forbes, Jessica Kasza, Richard Hooper |
---|---|
Rok vydání: | 2020 |
Předmět: |
Statistics and Probability
Epidemiology Sample (statistics) Disease cluster intra cluster correlation 01 natural sciences law.invention 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Randomized controlled trial law Statistics mixed effects models 030212 general & internal medicine 0101 mathematics cluster crossover trial Research Articles Mathematics Autocorrelation Sampling (statistics) stepped wedge Autoregressive model Sample size determination Cohort Research Article |
Zdroj: | Statistics in Medicine |
ISSN: | 1097-0258 0277-6715 |
DOI: | 10.1002/sim.8519 |
Popis: | When calculating sample size or power for stepped wedge or other types of longitudinal cluster randomized trials, it is critical that the planned sampling structure be accurately specified. One common assumption is that participants will provide measurements in each trial period, that is, a closed cohort, and another is that each participant provides only one measurement during the course of the trial. However some studies have an "open cohort" sampling structure, where participants may provide measurements in variable numbers of periods. To date, sample size calculations for longitudinal cluster randomized trials have not accommodated open cohorts. Feldman and McKinlay (1994) provided some guidance, stating that the participant-level autocorrelation could be varied to account for the degree of overlap in different periods of the study, but did not indicate precisely how to do so. We present sample size and power formulas that allow for open cohorts and discuss the impact of the degree of "openness" on sample size and power. We consider designs where the number of participants in each cluster will be maintained throughout the trial, but individual participants may provide differing numbers of measurements. Our results are a unification of closed cohort and repeated cross-sectional sample results of Hooper et al (2016), and indicate precisely how participant autocorrelation of Feldman and McKinlay should be varied to account for an open cohort sampling structure. We discuss different types of open cohort sampling schemes and how open cohort sampling structure impacts on power in the presence of decaying within-cluster correlations and autoregressive participant-level errors. |
Databáze: | OpenAIRE |
Externí odkaz: |