The effect of cluster randomization on sample size in prevention research.

BACKGROUND

This paper concerns the issue of cluster randomization in primary care practice intervention trials. We present information on the cluster effect of measuring the performance of various preventive maneuvers between groups of physicians based on a successful trial. We discuss the intracluster correlation coefficient of determining the required sample size and the implications for designing randomized controlled trials where groups of subjects (e.g., physicians in a group practice) are allocated at random.

METHODS

We performed a cross-sectional study involving data from 46 participating practices with 106 physicians collected using self-administered questionnaires and a chart audit of 100 randomly selected charts per practice. The population was health service organizations (HSOs) located in Southern Ontario. We analyzed performance data for 13 preventive maneuvers determined by chart review and used analysis of variance to determine the intraclass correlation coefficient. An index of "up-to-datedness" was computed for each physician and practice as the number of a recommended preventive measure done divided by the number of eligible patients. An index called "inappropriateness" was computed in the same manner for the not-recommended measures. The intraclass correlation coefficients for 2 key study outcomes (up-to-datedness and inappropriateness) were also calculated and compared.

RESULTS

The mean up-to-datedness score for the practices was 53.5% (95% confidence interval [CI], 51.0%-56.0%), and the mean inappropriateness score was 21.5% (95% CI, 18.1%-24.9%). The intraclass correlation for up-to-datedness was 0.0365 compared with inappropriateness at 0.1790. The intraclass correlation for preventive maneuvers ranged from 0.005 for blood pressure measurement to 0.66 for chest radiographs of smokers, and as a consequence required the sample size ranged from 20 to 42 physicians per group.

CONCLUSIONS

Randomizing by practice clusters and analyzing at the level of the physician has important implications for sample size requirements. Larger intraclass correlations indicate interdependence among the physicians within a cluster; as a consequence, variability within clusters is reduced, and the required sample size increased. The key finding that many potential outcome measures perform differently in terms of the intracluster correlation reinforces the need for researchers to carefully consider the selection of outcome measures and adjust sample sizes accordingly when the unit of analysis and randomization are not the same.