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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

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proc power

PROC POWER

Katie Benton

University of Colorado Health Sciences Center

Colorado Health Outcomes Program

proc power2
PROC POWER
  • Study design specific options
  • How to obtain power or sample size
  • Plot statement
  • Examples
the power procedure
The POWER Procedure
  • Considerations
    • Study design
    • Model and statistical test
    • Alpha
    • Surmised effects and variability
    • Power
    • Sample size
basic components for calculations
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
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
ONECORR
  • Fisher’s z test and t tests of (partial) correlation
  • Options
    • Distribution
    • Correlation/null correlation
    • Random/fixed effects
    • Test = Pearson
onesamplefreq
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
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
ONEWAYANOVA
  • One-way ANOVA including single-degree-of-freedom contrasts
  • Options
    • Contrast
    • Group means
    • Group weights
    • Standard deviations
    • Test (contrast and overall)
pairedfreq
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
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
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
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
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)
slide15
PLOT
  • Any study design
  • Similar options to most plot procedures
  • See SAS documentation
multiple regression
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
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
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
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
Thank You

Kathryn.Benton@UCHSC.edu