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# A Casual Tutorial on Sample Size Planning for Multiple Regression Models - PowerPoint PPT Presentation

A Casual Tutorial on Sample Size Planning for Multiple Regression Models D. Keith Williams M.P.H. Ph.D. Department of Biostatistics. Area = 0.16. 1.00. Area = 0.47. 2.00. Area = 0.81. 3.00. Area = 0.955. 3.87. Buzzwords.

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for Multiple Regression Models

D. Keith Williams M.P.H. Ph.D.

Department of Biostatistics

1.00

2.00

3.00

3.87

• Beta () = P(Type II error) = P(Conclude the experimental groups are the same when they really are different)

• Power = 1 -  = P(Conclude experimental groups are different when they really are!)

• Alpha =0.05, sigma=2

• |mu1 – mu2| = 2, that is, a two unit diff in means for a population

• Propose n1 = 10 and n2 = 10

Noncentrality value =2.236, Critical value = |2.101|

Table B.5, Values between 2.0 and 3.0, alpha = 0.05, df = 18

Power between 0.47 and 0.81, SAS calculation 0.56195

• One conjectures the difference in means to estimate power in studies that compare means.

• In regression models, one conjectures the difference in R-square between a model that includes predictors of interest and a model without these predictors.

• Power for specific predictors in the presence of other covariates in a model.

• More complex to conceptualize than testing differences among means.

The Hypothetical ScenarioA model with 4 terms

Predictors for PSA of interest that we choose to power:

• SVI

• c_volume

Two Covariates to be included :

cpen, gleason

• Complete specification of the parts for the expression:

The full model

We want to power the test that a model with these

2 predictors is statistically better than a model excluding them.

The reduced model

Full Model

Predictors of interest

Note

R-Square difference

0.45 – 0.34=

0.11

procpower ;

multreg

model=fixed

alpha= .05

nfullpredictors= 4

ntestpredictors= 2

rsqfull=0.45

rsqdiff=0.11

ntotal= 978070605040

power=. ;

plot x=n min=40 max=100

key = oncurves

yopts=(ref=0.8.977 crossref=yes)

;

run;

The POWER Procedure

Type III F Test in Multiple Regression

Fixed Scenario Elements

Method Exact

Model Fixed X

Number of Predictors in Full Model 4

Number of Test Predictors 2

Alpha 0.05

R-square of Full Model 0.45

Difference in R-square 0.11

Computed Power

N

Index Total Power

1 97 0.979

2 80 0.949

3 70 0.916

4 60 0.864

5 50 0.787

6 40 0.677

Correlation of Y with all Predictors

Correlation of All Predictors with Each Other

Correlation of Y with Reduced Model Predictors

Correlation of All Reduced Predictors with Each Other

procpower ;

multreg

model=fixed

alpha= .05

nfullpredictors= 4

ntestpredictors= 2

rsqfull=0.45

rsqdiff=0.11.10.09.08

ntotal= 978070605040

power=. ;

plot x=n min=40 max=100

key = oncurves

yopts=(ref=0.8.977 crossref=yes)

;

run;

prociml;

%let phi=0.35;

%let rx=0.2;

phi_yx_full={&phi,&phi,.2,.2};

rxx_full={1 &rx &rx &rx ,

&rx 1 &rx &rx ,

&rx &rx 1 &rx ,

&rx &rx &rx 1 };

phi_yx_red={&rx,&rx};

rxx_red={1 &rx ,

&rx 1 };

r2_full=(phi_yx_full)` * (rxx_full**(-1)) * (phi_yx_full);

r2_red=phi_yx_red` * rxx_red**(-1) * phi_yx_red;

r2diff=r2_full-r2_red;

partial = (r2diff/(1-r2_red))**.5;

print r2_full r2_red r2diff partial;

run;quit;

R2_FULL R2_RED R2DIFF PARTIAL

0.2171875 0.0666667 0.1505208 0.4015873

Type III F Test in Multiple Regression

Fixed Scenario Elements

Method Exact

Model Fixed X

Number of Predictors in Full Model 4

Number of Test Predictors 2

Alpha 0.05

R-square of Full Model 0.22

Computed Power

R-square N

Index Diff Total Power

1 0.15 40 0.659

2 0.15 50 0.770

3 0.15 60 0.850

4 0.15 70 0.905

5 0.16 40 0.689

6 0.16 50 0.798

7 0.16 60 0.873

8 0.16 70 0.923

procpower ;

multreg

model=fixed

alpha= .05

nfullpredictors= 4

ntestpredictors= 2

rsqfull=0.22

rsqdiff=0.15.16

ntotal= 40506070

power=. ;

plot x=n min=40 max=100

key = oncurves

yopts=(ref=0.8 crossref=yes)

;

run;

• Specify the typical value of the multiple partial correlation coefficient between Y and X.

• Multiple correlation coefficient describes the overall relationship between Y and 2 or more predictors controlling for still other variables.

• Say that we conjecture that the partial correlation between our Y and X’s of interest is:

• For our example this value was 0.408

Recall Rsqare diff in full and reduced models

Type III F Test in Multiple Regression

Fixed Scenario Elements

Method Exact

Model Fixed X

Number of Predictors in Full Model 4

Number of Test Predictors 2

Alpha 0.05

Computed Power

Partial N

Index Corr Total Power

1 0.408 97 0.979

2 0.408 80 0.949

3 0.408 60 0.864

4 0.408 50 0.787

5 0.408 40 0.677

6 0.350 97 0.910

7 0.350 80 0.843

8 0.350 60 0.713

9 0.350 50 0.623

10 0.350 40 0.514

procpower ;

multreg

model=fixed

alpha= .05

nfullpredictors= 4

ntestpredictors= 2

partialcorr= .408.35

ntotal= 9780605040

power=. ;

plot x=n min=40 max=100

key = oncurves

yopts=(ref=.8.85.977 crossref=yes)

;run;

Note n=4*10=40

under powers

Plan CUse the Table from Gatsonis and Sampson (1989)

p : the total number of predictors in the model=4

N = table value + p + 1

For 80% power N = 72 + 4 + 1 = 77

The POWER Procedure

Type III F Test in Multiple Regression

Fixed Scenario Elements

Method Exact

Model Random X

Number of Predictors in Full Model 4

Number of Test Predictors 2

Alpha 0.05

Total Sample Size 77

Computed Power

Partial

Index Corr Power

1 0.35 0.802

2 0.40 0.908

procpower ;

multreg

model=random

alpha= .05

nfullpredictors= 4

ntestpredictors= 2

partialcorr= .35.40

ntotal= 77

power=. ;

plot x=n min=60 max=120

key = oncurves

yopts=(ref=.8.90 crossref=yes)

;run;

• Power and sample size is ‘tricky.’

• The n= 10 for each predictor will almost always under power a study.

• Plan A or B using the matrix mult is likely the best. One can specify regular correlations instead of partial correlations.

• This talk was developed with fixed effects, arguably one should plan for random effects unless for an experiment. SAS can easily calculate this. Gatsonis tables provide power for random effect settings. (usually n’s are close)

• A corresponding multiple logistic regression approach, that is, powering more than one predictor of interest with additional covariates in the model.

### An Algorithm for Estimating Power and Sample Size for Logistic Models with One or More Independent Variables of Interest

Jay Northern

D. Keith Williams, PhD

Zoran Bursac, PhD

Joint Statistical Meetings, Denver, CO August 3 – August 7, 2008

Background Logistic Models with One or More Independent Variables of Interest

• Existing tools are based on Hsieh, Block, and Larsen (1998) paper,and Agresti (1996) text.

• PASS

• %powerlog macro

Macro Details Logistic Models with One or More Independent Variables of Interest

• Fit the full and the reduced model

• In the reduced model one can exclude one or more covariates of interest in order to test them simultaneously in the presence of other covariates

• Perform the likelihood ratio test with appropriate chi-square critical value based on correct number of degrees of freedom

Results Logistic Models with One or More Independent Variables of Interest

End Logistic Models with One or More Independent Variables of Interest

Plan C Logistic Models with One or More Independent Variables of Interest Exchangeable Matrix in Plan A

Full Correlation Matrix Logistic Models with One or More Independent Variables of Interest

The Correlation of Y with All X’s Logistic Models with One or More Independent Variables of Interest Full Model

Correlation Matrix of X’s Logistic Models with One or More Independent Variables of Interest Full Model

The Correlation of Y with All X’s Logistic Models with One or More Independent Variables of Interest Reduced Model

Correlation Matrix of X’s Logistic Models with One or More Independent Variables of Interest

Regular Correlations Logistic Models with One or More Independent Variables of Interest Versus Partial Correlations

Correlation Matrix Logistic Models with One or More Independent Variables of Interest

Reduced Rxy

Full R xy

X’s of interest

Covariates in reduced model Rxx

Correlation Matrix Logistic Models with One or More Independent Variables of Interest

Reduced Rxy

Full R xy

X’s of interest

Covariates in reduced model Rxx

Correlation Matrix Logistic Models with One or More Independent Variables of Interest

Reduced Rxy

Full R xy

X’s of interest

Covariates in reduced model Rxx

The Gold Standard Approach Logistic Models with One or More Independent Variables of Interest Some Matrix Algebra

=0.35 Logistic Models with One or More Independent Variables of Interest

The Gold Standard Approach Logistic Models with One or More Independent Variables of Interest Some Matrix Algebra

=0.35 Logistic Models with One or More Independent Variables of Interest

Full Correlation Matrix Logistic Models with One or More Independent Variables of Interest

The Correlation of Y with All X’s Logistic Models with One or More Independent Variables of Interest Full Model

Correlation Matrix of X’s Logistic Models with One or More Independent Variables of Interest Full Model

The Correlation of Y with All X’s Logistic Models with One or More Independent Variables of Interest Reduced Model

Correlation Matrix of X’s Logistic Models with One or More Independent Variables of Interest

The Calculations Logistic Models with One or More Independent Variables of Interest

Power = 0.97

The POWER Procedure Logistic Models with One or More Independent Variables of Interest

Type III F Test in Multiple Regression

Fixed Scenario Elements

Method Exact

Model Fixed X

Number of Predictors in Full Model 7

Number of Test Predictors 2

Alpha 0.05

R-square of Full Model 0.250568

R-square of Reduced Model 0.111111

Computed Power

N

Index Total Power

1 50 0.753

2 60 0.836

3 70 0.894

4 80 0.933

5 97 0.970

procpower ;

multreg

model=fixed

alpha= .05

nfullpredictors= 7

ntestpredictors= 2

rsqfull=0.2505682

rsqdiff=0.1111111

ntotal= 5060708097

power=. ;

plot x=n min=60 max=100

key = oncurves

yopts=(ref=.8.85.9.95 crossref=yes)

;

run;

Matrix Arithmetic with Compound Correlation Matrix Logistic Models with One or More Independent Variables of Interest

The POWER Procedure Logistic Models with One or More Independent Variables of Interest

Type III F Test in Multiple Regression

Fixed Scenario Elements

Method Exact

Model Fixed X

Number of Predictors in Full Model 4

Number of Test Predictors 2

Alpha 0.05

R-square of Full Model 0.22

Computed Power

R-square N

Index Diff Total Power

1 0.15 40 0.659

2 0.15 50 0.770

3 0.15 60 0.850

4 0.15 70 0.905

5 0.16 40 0.689

6 0.16 50 0.798

7 0.16 60 0.873

8 0.16 70 0.923

procpower ;

multreg

model=fixed

alpha= .05

nfullpredictors= 4

ntestpredictors= 2

rsqfull=0.22

rsqdiff=0.15.16

ntotal= 40506070

power=. ;

plot x=n min=40 max=100

key = oncurves

yopts=(ref=0.8 crossref=yes)

;

run;

Calculations Logistic Models with One or More Independent Variables of Interest

The number of predictors of interest

2

The total number of predictors in the model

4

Approaches in Estimating the Parameters to Calculate Power Logistic Models with One or More Independent Variables of Interest Plan A

• Complete specification of the parts for the expression:

= 0.45

= 0.34

Approaches in Estimating the Parameters to Calculate Power Logistic Models with One or More Independent Variables of Interest Plan A

• Complete specification of the parts for the expression:

Total area in blue. Logistic Models with One or More Independent Variables of Interest

Power = 0.97

F(2,92)

F(2,92,19.4)

3.07

19.4

Critical Value for alpha = .05

Noncentrality Parameter