1 / 21

210 likes | 1.78k Views

PROC POWER. Katie Benton University of Colorado Health Sciences Center Colorado Health Outcomes Program. PROC POWER. Study design specific options How to obtain power or sample size Plot statement Examples. The POWER Procedure. Considerations Study design Model and statistical test

Download Presentation
## PROC POWER

**An Image/Link below is provided (as is) to download presentation**
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.
Content is provided to you AS IS for your information and personal use only.
Download presentation by click this link.
While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

**PROC POWER**Katie Benton University of Colorado Health Sciences Center Colorado Health Outcomes Program**PROC POWER**• Study design specific options • How to obtain power or sample size • Plot statement • Examples**The POWER Procedure**• Considerations • Study design • Model and statistical test • Alpha • Surmised effects and variability • Power • Sample size**Basic Components for Calculations**• Alpha • Default = .05 • Sides • 1, 2, U, L • Distribution • Dist = lognormal or normal • Sample size • Ntotal or npergroup (can specify group weights) • Power**MULTREG**• Tests of one or more coefficients in multiple linear regression • Options • Fixed/random effects • Number of predictors (full and reduced models) • No intercept (noint) • Rsquare and rsquare difference (between full and reduced models) • Partial correlations • Test = typeIII**ONECORR**• Fisher’s z test and t tests of (partial) correlation • Options • Distribution • Correlation/null correlation • Random/fixed effects • Test = Pearson**ONESAMPLEFREQ**• Tests of a single binomial proportion • Options • Method (exact/normal) • Null and group proportions (expected proportion of “successes”) • Test (adjusted Z, exact, Z)**ONESAMPLEMEANS**• One-sample t test, confidence interval precision, or equivalence test • Options • CI • CV (coefficient of variation) • Distribution • Mean/null mean • Probability (conditional/unconditional) • Standard deviation • Test (equivalence or t)**ONEWAYANOVA**• One-way ANOVA including single-degree-of-freedom contrasts • Options • Contrast • Group means • Group weights • Standard deviations • Test (contrast and overall)**PAIREDFREQ**• McNemar’s test for paired proportions • Options • Discordant proportions/differences/ratios • Distribution (exact, exact conditional, normal) • Number of pairs • Null proportion or reference proportion**PAIREDMEANS**• Paired t test, confidence interval precision, or equivalence test • Options • CI • Correlation • CV (coefficient of variation) • Distribution • Mean difference/group means/mean ratio/paired means • Number of pairs • Standard deviations • Probability (conditional/unconditional)**TWOSAMPLEFREQ**• Chi-square, likelihood ratio, and Fisher’s exact tests for two independent proportions • Options • Group proportions • Group N’s and weights • Null OR, RR, or proportion difference • Test (Fisher, LRCHI, PCHI)**TWOSAMPLEMEANS**• Two-sample t test (pooled/unpooled), confidence interval precision, or equivalence test • Options • CI • CV • Group N’s, weights, means, and standard deviations • Mean/null difference or ratio • Probability • Test (diff, diff_satt, equiv_diff/ratio, ratio)**TWOSAMPLESURVIVAL**• Log-rank, Gehan, and Tarone-Ware tests for comparing two survival curves • Options • Accrual time, follow-up time, total time • Curve (defining features) • Group loss (group loss exponential hazards) • Group N’s • Group survival hazards, HR • Number of subintervals • Test (Gehan, TaroneWare, LogRank)**PLOT**• Any study design • Similar options to most plot procedures • See SAS documentation**Multiple Regression**proc power; multreg model = random nfullpredictors = 7 ntestpredictors = 1 partialcorr = 0.35 ntotal = 100 power = .; plot x=n min=50 max=150; run;**MULTREG Output**The POWER Procedure Type III F Test in Multiple Regression Fixed Scenario Elements Method Exact Model Random X Number of Predictors in Full Model 7 Number of Test Predictors 1 Partial Correlation 0.35 Total Sample Size 100 Alpha 0.05 Computed Power Power 0.939**Survival Analysis**proc power; twosamplesurvival test=logrank curve("Standard") = 5 : 0.5 curve("Proposed") = (1 to 5 by 1):(0.95 0.9 0.75 0.7 0.6) groupsurvival = "Standard" | "Proposed" accrualtime = 2 followuptime = 3 groupmedlosstimes = 10 | 20 5 power = 0.8 npergroup = .; run;**Survival Output**The POWER Procedure Log-Rank Test for Two Survival Curves Fixed Scenario Elements Method Lakatos normal approximation Accrual Time 2 Follow-up Time 3 Group 1 Survival Curve Standard Form of Survival Curve 1 Exponential Group 2 Survival Curve Proposed Form of Survival Curve 2 Piecewise Linear Group 1 Median Loss Time 10 Nominal Power 0.8 Number of Sides 2 Number of Time Sub-Intervals 12 Alpha 0.05 Computed N Per Group Median Loss Actual N Per Index Time 2 Power Group 1 20 0.800 228 2 5 0.801 234**Thank You**Kathryn.Benton@UCHSC.edu

More Related