{smcl}
{cmd:help cluster2}
{hline}
{title:Title}
{p2colset 5 22 24 2}{...}
{p2col :{hi:cluster2} {hline 2}}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. {p_end}
{p2colreset}{...}
{title:Syntax}
{p 8 11 2}{cmd:cluster2} [{cmd:,} {it:options}]
{synoptset 22 tabbed}{...}
{synopthdr}
{synoptline}
{syntab:Main}
{synopt :{cmd:power}}compute power{p_end}
{synopt :{cmd:mdes}}compute minimum detectable effect size{p_end}
{synopt :{cmd:obs2}}compute number of level-2 clusters per treatment arm{p_end}
{synopt :{cmd:obs1}}compute number of level-1 observations per cluster{p_end}
{syntab:Options}
{synopt :{opt a(#)}}the significance level of the test; default is {cmd:a(0.05)}{p_end}
{synopt :{opt p(#)}}the power of the test; default is {cmd:p(0.8)}{p_end}
{synopt :{opt rho(#)}}the intraclass correlation coefficient; default is {cmd:rho(0)}{p_end}
{synopt :{opt rxy(#)}}the correlation coefficient between the baseline covariate (x) and the outcome (y); default is {cmd:rxy(0)}{p_end}
{synopt :{opt d(#)}}the standardized effect size, delta (b/sd); default is {cmd:d(1)}{p_end}
{synopt :{opt n2(#)}}number of level-2 clusters in each treatment arm; default is {cmd:n3(1)}{p_end}
{synopt :{opt n1(#)}}number of level-1 observations, per cluster; default is {cmd:n1(1)}{p_end}
{synoptline}
{title:Description}
{pstd}{cmd: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.
{title:Examples}
{p 5 8 2}1. Determine power in a 2-level design with no covariates{p_end}
{phang2}{cmd:. cluster2, rho(0.2) d(0.4) n2(50) n1(15) power}
{p 5 8 2}2. Determine power in a 2-level design with baseline covariate and correlation(x,y) of 0.6 {p_end}
{phang2}{cmd:. cluster2, rho(0.2) rxy(0.6) d(0.4) n2(50) n1(15) power}
{p 5 8 2}3. Determine minimum detectable effect size in a 2-level design with no covariates {p_end}
{phang2}{cmd:. cluster2, rho(0.2) p(0.9) n2(50) n1(15) mdes}
{p 5 8 2}4. Determine minimum detectable effect size in a 2-level design with baseline covariate and correlation(x,y) of 0.4 {p_end}
{phang2}{cmd:. cluster2, rho(0.2) rxy(.4) p(0.9) n2(50) n1(15) mdes}
{p 5 8 2}7. Determine number of clusters in a 2-level design with no covariates{p_end}
{phang2}{cmd:. cluster2, n1(12) d(0.25) rho(0.15) obs2}
{p 5 8 2}8. Determine number of clusters in a 2-level design with baseline covariate and correlation(x,y) of 0.65 {p_end}
{phang2}{cmd:. cluster2, n1(12) d(0.25) rho(0.15) rxy(0.65) obs2}
{p 5 8 2}9. Determine number of level-1 observations in a 2-level design with no covariates{p_end}
{phang2}{cmd:. cluster2, n2(50) d(0.25) rho(0.15) obs1}
{p 5 8 2}10. Determine number of level-1 observations in a 2-level design with baseline covariate and correlation(x,y) of 0.65 {p_end}
{phang2}{cmd:. cluster2, n2(50) d(0.25) rho(0.15) rxy(0.65) obs1}
{title:Saved results}
{pstd}{cmd:cluster2} saves the following in {cmd:r()}:
{synoptset 15 tabbed}{...}
{p2col 5 15 19 2: Macros}{p_end}
{synopt:{cmd:r(alpha)}}Alpha{p_end}
{synopt:{cmd:r(rho)}}ICC{p_end}
{synopt:{cmd:r(rxy)}}Correlation(x,y){p_end}
{synopt:{cmd:r(power)}}Power{p_end}
{synopt:{cmd:r(delta)}}Delta, detectable effect size{p_end}
{synopt:{cmd:r(obs2)}}Number of level-2 observations{p_end}
{synopt:{cmd:r(obs1)}}Number of level-1 observations{p_end}
{synopt:{cmd:r(N)}}Total number of observations (T+C) {p_end}
{title:Author}
{pstd}Wael Moussa{p_end}
{pstd}FHI 360{p_end}
{pstd}Washington, DC{p_end}
{pstd}wmoussa@fhi360.org{p_end}
{title:References}
{pstd}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.{p_end}
{title:Disclaimer}
{pstd}Any errors are the author's alone. Please email wmoussa@fhi360.org to report any issues.{p_end}