{"id":15222,"date":"2018-05-09T05:17:01","date_gmt":"2018-05-09T05:17:01","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=15222"},"modified":"2021-12-05T22:28:50","modified_gmt":"2021-12-05T16:58:50","slug":"sas-power-sample-size","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/sas-power-sample-size\/","title":{"rendered":"SAS Power and Sample Size Analysis Procedures"},"content":{"rendered":"<p>We looked at different types of analysis and the procedures used for performing it in the previous <strong>STAT tutorials<\/strong>, today we will learn about SAS power and sample size analysis. Moreover, we will also discuss how can we use power and sample size procedures in SAS\/STAT:\u00a0PROC GLMPOWER &amp;\u00a0PROC\u00a0POWER with examples and syntax.<\/p>\n<p>So, let&#8217;s start with SAS\/STAT Power and Sample Size Analysis.<\/p>\n<h3>SAS Power and Sample Size Analysis<\/h3>\n<p>The most important aspect of any study is determining the appropriate sample size to answer the research question.<strong>\u00a0<\/strong>We can improve the resource usage and design of a study through SAS Power and sample size analysis, thereby improving chances of obtaining results that exhibit maximum efficiency. In any generic statistical testing procedure, the motive is to assume that Type I errors are more costly than Type II errors.<\/p>\n<p>SAS\/STAT Power and sample size analysis helps you achieve the desired balance between the Type I and Type II errors. With the most efficient designs and sample sizes, you can improve your chances of detecting effects that might otherwise have been ignored. This will save money, time and perhaps minimizes the risks to subjects.<\/p>\n<h3>Procedures for Power and Sample Size Analysis in SAS\/STAT<\/h3>\n<p>Following procedures are used to compute SAS power and sample size analysis of a sample data. Let us explore it.<\/p>\n<h4>a. PROC POWER<\/h4>\n<p>The PROC POWER procedure performs SAS power and sample analysis, which covers a variety of sample analysis such as <strong>t-tests, one way ANOVA<\/strong>, <strong>regression<\/strong> and <strong>correlation<\/strong>. This procedure also performs different tasks such as determining the sample size required to get an appropriate result with adequate probability (power).<\/p>\n<p>It characterizes the power of a study to extract meaning from it and it also conducts an analysis to assess the effect of a required sample size to other factors.<\/p>\n<p>The PROC POWER procedure uses the following inputs<\/p>\n<ul>\n<li>Significance level (alpha)<\/li>\n<li>Statistical model and test<\/li>\n<li>Design<\/li>\n<li>Surmised effects and variability<\/li>\n<li>Power<\/li>\n<li>Sample size<\/li>\n<\/ul>\n<p><strong>A Syntax of PROC POWER &#8211;<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\">PROC POWER &lt;options&gt;;\r\n&lt;analysis statements&gt;;<\/pre>\n<p>The\u00a0PROC POWER and minimum one analysis statements are required. Different analysis statements can be specified depending on the type of analysis we wish to perform such as:<br \/>\nCOXREG,\u00a0CUSTOM,\u00a0LOGISTIC,\u00a0MULTREG,\u00a0ONECORR,\u00a0ONESAMPLEFREQ,\u00a0ONESAMPLEMEANS,\u00a0ONEWAYANOVA,\u00a0PAIREDFREQ,\u00a0PAIREDMEANS,\u00a0TWOSAMPLEFREQ,\u00a0TWOSAMPLEMEANS,\u00a0TWOSAMPLESURVIVAL, and\u00a0TWOSAMPLEWILCOXON.<\/p>\n<p><strong>PROC POWER\u00a0Example-<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\">proc power;\r\n\u00a0\u00a0 onewayanova test=overall\r\n\u00a0\u00a0\u00a0\u00a0 groupmeans=0.5|0\r\n\u00a0\u00a0\u00a0\u00a0 stddev=1\r\n\u00a0\u00a0\u00a0\u00a0 ntotal=6\r\n\u00a0\u00a0\u00a0\u00a0 power=.;\r\n\u00a0\u00a0\u00a0\u00a0 plot x=n min=6 max=27;\r\nrun;<\/pre>\n<div id=\"attachment_15285\" style=\"width: 225px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/PROC-POWER-OUTPUT-1.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-15285\" class=\"wp-image-15285 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/PROC-POWER-OUTPUT-1.png\" alt=\"SAS Power and Sample Size Analysis\" width=\"215\" height=\"280\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/PROC-POWER-OUTPUT-1.png 215w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/PROC-POWER-OUTPUT-1-115x150.png 115w\" sizes=\"auto, (max-width: 215px) 100vw, 215px\" \/><\/a><p id=\"caption-attachment-15285\" class=\"wp-caption-text\">SAS PROC\u00a0POWER<\/p><\/div>\n<div id=\"attachment_15287\" style=\"width: 734px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/PROC-POWER-OUTPUT-2.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-15287\" class=\"wp-image-15287 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/PROC-POWER-OUTPUT-2.png\" alt=\"SAS Power and Sample Size Analysis\" width=\"724\" height=\"563\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/PROC-POWER-OUTPUT-2.png 724w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/PROC-POWER-OUTPUT-2-150x117.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/PROC-POWER-OUTPUT-2-300x233.png 300w\" sizes=\"auto, (max-width: 724px) 100vw, 724px\" \/><\/a><p id=\"caption-attachment-15287\" class=\"wp-caption-text\">SAS\/STAT Power and Sample Size Analysis &#8211; PROC\u00a0POWER<\/p><\/div>\n<h4>b. PROC GLMPOWER<\/h4>\n<p>The GLMPOWER procedure in SAS\/STAT performs power and sample size analysis for linear models. Just like the PROC POWER, this procedure also performs different tasks such as determining the sample size required to get an appropriate result with adequate probability (power).<\/p>\n<p>It characterizes the power of a study to extract meaning from it and it also conducts an analysis to assess the effect of a required sample size to other factors.<br \/>\n<strong>A Syntax of PROC GLMPOWER-<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\">PROCGLMPOWER\u00a0DATASET;\r\nMODEL DEPENDENT variables =independent effects;\r\nPOWER &lt;options&gt;;<\/pre>\n<p>The\u00a0PROC GLMPOWER,\u00a0MODEL, and POWER statements are required.<br \/>\n<strong>PROC GLMPOWER Example-<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\">proc GLMPOWER DATA=sashelp.cars;\r\n\u00a0\u00a0 model mpg_highway=enginesize|horsepower;\r\n\u00a0\u00a0\u00a0\u00a0 power\r\n\u00a0\u00a0\u00a0\u00a0\u00a0 stddev = 5\r\n\u00a0\u00a0\u00a0\u00a0\u00a0 ntotal = 60\r\n\u00a0\u00a0\u00a0\u00a0\u00a0 power\u00a0 = .;\r\nrun;<\/pre>\n<div id=\"attachment_15288\" style=\"width: 327px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/proc-glmpower-output-1.png\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-15288\" class=\"wp-image-15288 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/proc-glmpower-output-1.png\" alt=\"SAS Power and Sample Size Analysis\" width=\"317\" height=\"317\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/proc-glmpower-output-1.png 317w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/proc-glmpower-output-1-150x150.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/proc-glmpower-output-1-300x300.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2018\/05\/proc-glmpower-output-1-100x100.png 100w\" sizes=\"auto, (max-width: 317px) 100vw, 317px\" \/><\/a><p id=\"caption-attachment-15288\" class=\"wp-caption-text\">SAS PROC GLMPOWER<\/p><\/div>\n<p>This was all\u00a0about SAS Power and Sample Size Analysis Tutorial. Hope you like our explanation<b><\/b>.<\/p>\n<h3>Conclusion<\/h3>\n<p>Hence, in this SAS\/STAT tutorial, we studied about SAS\/STAT Power and Sample Size Analysis. In conclusion, we saw the procedures used for power and sample size analysis in SAS\/STAT: PROC GLMPOWER and PROC POWER with example and syntax.\u00a0Furthermore, if you have any doubt, feel free to ask in the comment section.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We looked at different types of analysis and the procedures used for performing it in the previous STAT tutorials, today we will learn about SAS power and sample size analysis. Moreover, we will also&#46;&#46;&#46;<\/p>\n","protected":false},"author":6,"featured_media":15282,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[56],"tags":[8054,10030,10031,10075,10076,10077,11876,12117,12181,12219,12235,14066,14073],"class_list":["post-15222","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sas-stat","tag-lackfit-sas","tag-proc-glmpower","tag-proc-glmpower-example","tag-proc-power","tag-proc-power-example","tag-proc-power-sas-example","tag-sample-size-calculator","tag-sas-power-function","tag-sas-proc-power","tag-sas-pss","tag-sas-sample-size-calculation-proportion","tag-syntax-of-proc-glmpower","tag-syntax-of-proc-power"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>SAS Power and Sample Size Analysis Procedures - DataFlair<\/title>\n<meta name=\"description\" content=\"What is SAS\/STAT Power and Sample Size Analysis- Procedures used in power &amp; sample size in SAS\/STAT, PROC POWER, PROC GLMPOWER with examples and syntax\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/sas-power-sample-size\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"SAS Power and Sample Size Analysis Procedures - 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