help cluster2

Title

cluster2 -- Determination of sample size, power, and minimum detectable effect size for two-level cluster randomized trials with continuous outcomes, with or without the inclusion of a baseline covariate.

Syntax

cluster2 [, options]

Main
power compute power

mdes compute minimum detectable effect size

obs2 compute number of level-2 clusters per treatment arm

obs1 compute number of level-1 observations per cluster

Options
a(#) the significance level of the test; default is a(0.05)

p(#) the power of the test; default is p(0.8)

rho(#) the intraclass correlation coefficient; default is rho(0)

rxy(#) the correlation coefficient between the baseline covariate (x) and the outcome (y); default is rxy(0)

d(#) the standardized effect size, delta (b/sd); default is d(1)

n2(#) number of level-2 clusters in each treatment arm; default is n3(1)

n1(#) number of level-1 observations, per cluster; default is n1(1)

Description

cluster2 The program ^cluster2^ conducts a generalized power analysis for two-level cluster-randomized trials with, or without, the inclusion of a baseline pretest/covariate.

Examples

1. Determine power in a 2-level design with no covariates

. cluster2, rho(0.2) d(0.4) n2(50) n1(15) power

2. Determine power in a 2-level design with baseline covariate and correlation(x,y) of 0.6

. cluster2, rho(0.2) rxy(0.6) d(0.4) n2(50) n1(15) power

3. Determine minimum detectable effect size in a 2-level design with no covariates

. cluster2, rho(0.2) p(0.9) n2(50) n1(15) mdes

4. Determine minimum detectable effect size in a 2-level design with baseline covariate and correlation(x,y) of 0.4

. cluster2, rho(0.2) rxy(.4) p(0.9) n2(50) n1(15) mdes

7. Determine number of clusters in a 2-level design with no covariates

. cluster2, n1(12) d(0.25) rho(0.15) obs2

8. Determine number of clusters in a 2-level design with baseline covariate and correlation(x,y) of 0.65

. cluster2, n1(12) d(0.25) rho(0.15) rxy(0.65) obs2

9. Determine number of level-1 observations in a 2-level design with no covariates

. cluster2, n2(50) d(0.25) rho(0.15) obs1

10. Determine number of level-1 observations in a 2-level design with baseline covariate and correlation(x,y) of 0.65

. cluster2, n2(50) d(0.25) rho(0.15) rxy(0.65) obs1

Saved results

cluster2 saves the following in r():

Macros

r(alpha) Alpha

r(rho) ICC

r(rxy) Correlation(x,y)

r(power) Power

r(delta) Delta, detectable effect size

r(obs2) Number of level-2 observations

r(obs1) Number of level-1 observations

r(N) Total number of observations (T+C)

Author

Wael Moussa

FHI 360

Washington, DC

wmoussa@fhi360.org

References

Bloom, H.S., Richburg-Hayes, L. and Black, A.R., 2007. Using covariates to improve precision: Empirical guidelines for studies that randomize schools to measure the impacts of educational interventions. Educational Evaluation and Policy Analysis, 29(1), pp.30-59.

Disclaimer

Any errors are the author's alone. Please email wmoussa@fhi360.org to report any issues.