{smcl} {hline} help for {hi:sampsi_reg} {hline} {title: Calculates Sample Size or Power for Simple Linear Regression} {p 8 22} {cmdab:sampsi_reg} [, {opt null(#)} {opt alt(#)} {opt n1(#)} {opt sd1(#)} {opt a:lpha(#)} {opt p:ower(#)} {opt s:olve(string)} {opt sx(#)} {opt sy(#)} {opt var:method(string)} {opt yxcorr(#)} {opt onesided} } ] {p} {title:Description} {p 0 0} {hi: sampsi_reg} calculates the power and sample size for a simple linear regression. The theory behind this command is described in Dupont and Plummer (1998) Power and Sample Size Calculations for Studies involving Linear Regression, Controlled Clinical Trials 19:589-601. {p 0 0} The calculations require an estimate of the residual standard error. There are three methods for doing this: enter the estimate directly; enter the standard deviation of the Y's; or enter the correlation between Y and X values. {p 0 0} This command can be combined with {hi:samplesize} in order to look at multiple calculations and to plot the results. {title:Updating this command} {p 0 0} To obtain the latest version click the following to uninstall the old version {p_end} {stata ssc uninstall sampsi_reg} And click here to install the new version {stata ssc install sampsi_reg} {title:Options} {p 0 0} {opt null(#)} specifies the "null slope". {p 0 0} {opt alt(#)} specifies the "alternative slope". {p 0 0} {opt n1(#)} size of sample. {p 0 0} {opt sd1(#)} standard deviation of the residuals. {p 0 0} {opt a:lpha(#)} significance level of test; default is {hi:a(0.05)}. {p 0 0} {opt p:ower(#)} power of test; default is {hi:p(0.9)}. {p 0 0} {opt s:olve(string)} specifies whether to solve for the sample size or power; default is {hi:s(n)} solves for n and the only other choice is {hi:s(power)} solves for power. {p 0 0} {opt sx(#)} the standard deviation of the X's. {p 0 0} {opt sy(#)} the standard deviation of the Y's. {p 0 0} {opt yxcorr(#)} the correlation between Y's and X's. {p 0 0} {opt var:method(string)} specifies the method for calculating the residual standard deviation. {opt varmethod(r)} uses the Y-X correlation and {opt varmethod(sdy)} uses the standard deviation of the Y's, the default uses a direct estimate of the residual sd {opt sd1(#)}. {p 0 0} {opt onesided} one-sided test; default is two-sided. {title:Examples} {p 0 0} Calculate power for a two-sided test: {p_end} {p 2 2} {stata sampsi_reg, null(0) alt(0.25) n(100) sx(0.25) yxcorr(0.2) varmethod(r) s(power)} {p 0 0} Compute sample size: {p 2 2} {stata sampsi_reg, null(0) alt(0.25) sx(0.25) sy(1) varmethod(r) s(n)} When specifying the variance of the y's you must have a {opt varmethod} option {p 2 2} WRONG: {stata sampsi_reg, null(0) alt(5) sx(0.5) sy(12.3)}{p_end} {p 2 2} CORRECT: {stata sampsi_reg, null(0) alt(5) sx(0.5) sy(12.3) var(sdy)} {title:Author} {p} Adrian Mander, MRC Biostatistics Unit, Cambridge, UK. Email {browse "mailto:adrian.mander@mrc-bsu.cam.ac.uk":adrian.mander@mrc-bsu.cam.ac.uk} {title:See Also} Related commands: {p 2 2} {help sampsi}, {help samplesize} (if installed), {help sampclus} (if installed), {help xsampsi} (if installed), {help artmenu} (if installed)