Below are worked examples that provide an overview of models and illustrate how the results are calculated once you click "Calculate Results" at the end of the seventh step (“Analysis”) of the cluster or group r egression discontinuity design (GRDD) sample size calculator. In addition, a document illustrating example calculations for the variance of continuous, dichotomous, and count outcomes is provided.
Worked Examples for Detectable Difference
The three worked examples reflect the three primary variations that are addressed in this sample size calculator. These three variations are a function of the intervention assignment rule, either with group-level summaries of a non-outcome variable or group-level summaries of the outcome at pre-test. Within the latter, there are examples for the two analytic approaches (cohort, cross-sectional).
Separately, users make selections for the: Type I error rate; power; expected distribution of the primary outcome; nature of the pre-test assignment score; intraclass correlation; number of members or participants per group or cluster; expected benefit, if any, from regression adjustment for covariates; and magnitude of the intervention effect. These choices provide the parameter estimates used in the formulae but do not dictate the structure of the formulae.
The following documents provide the formula for the detectable difference for three variations and walks you through an example, showing how the detectable difference is calculated. You can open these files in any combination, save them, or print them, as they may be of help to you.
- Example based on an analysis where assignment is based on group-level summaries of a non-outcome variable (PDF)
- Example based on an analysis where assignment is based on
Worked Examples for Outcome Variance
The distribution of the outcome – continuous, dichotomous, or count – can determine its variance. The following document presents how outcome variance is calculated based on user inputs, as well as example calculations.