Comparison of intraclass correlation coefficient estimates and standard errors between using cross-sectional and repeated measurement data: the Safety Check cluster randomized trial

Journal Name: 
Contemp Clin Trials
Authors: 
Ip,E.H.
Wasserman,R.
Barkin,S.
Abstract: 
Designing cluster randomized trials in clinical studies often requires accurate estimates of intraclass correlation, which quantifies the strength of correlation between units, such as participants, within a cluster, such as a practice. Published ICC estimates, even when available, often suffer from the problem of wide confidence intervals. Using data from a national, randomized, controlled study concerning violence prevention for children--the Safety Check--we compare the ICC values derived from two approaches only baseline data and using both baseline and follow-up data. Using a variance component decomposition approach, the latter method allows flexibility in handling complex data sets. For example, it allows for shifts in the outcome variable over time and for an unbalanced cluster design. Furthermore, we evaluate the large-sample formula for ICC estimates and standard errors using the bootstrap method. Our findings suggest that ICC estimates range from 0.012 to 0.11 for providers within practice and range from 0.018 to 0.11 for families within provider. The estimates derived from the baseline-only and repeated-measurements approaches agree quite well except in cases in which variation over repeated measurements is large. The reductions in the widths of ICC confidence limits from using repeated measurement over baseline only are, respectively, 62% and 42% at the practice and provider levels. The contribution of this paper therefore includes two elements, which are a methodology for improving the accuracy of ICC, and the reporting of such quantities for pediatric and other researchers who are interested in designing clustered randomized trials similar to the current study
3
2011
Volume: 
32
Pages: 
225-232
Keywords: 
Bias (Epidemiology), Child, Child,Preschool, clinical, Cluster Analysis, Comparative Study, Comparison, Confidence Intervals, Cross-Sectional Studies, Data Interpretation,Statistical, electronic, Family, Health, Health Policy, Humans, Mass Media, Medicine, Methods, Outcome Assessment (Health Care), Paper, Parenting, Pediatrics, prevention & control, provider, psychology, Public Health, Randomized Controlled Trials as Topic, Research, Research Support, Safety, Standards, statistics & numerical data, Statistics as Topic, support, Time, Universities, Violence