a casual tutorial on sample size planning for multiple regression models d. keith williams m.p.h....
TRANSCRIPT
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A Casual Tutorial on Sample Size Planning for Multiple Regression Models
D. Keith Williams M.P.H. Ph.D.Department of Biostatistics
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Area = 0.16
1.00
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Area = 0.47
2.00
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Area = 0.81
3.00
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3.87
Area = 0.955
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Buzzwords
• 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!)
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The Non Centrality ParameterTwo Group t-test
21
21
11nn
221 nndf
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An Example Scenario
• 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
236.2
101
101
2
2
11
21
21
nn
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Rejection region for two tailed t-test alpha=0.05, df = 18
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Noncentrality value =2.236, Critical value = |2.101|Table B.5, Values between 2.0 and 3.0, alpha = 0.05, df = 18Power between 0.47 and 0.81, SAS calculation 0.56195
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The Key Point of the Review
• 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.
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Regression Power and Sample Size
• Power for specific predictors in the presence of other covariates in a model.
• More complex to conceptualize than testing differences among means.
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Example Data Set
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The Hypothetical ScenarioA model with 4 terms
Predictors for PSA of interest that we choose to power:
1.SVI2.c_volume
Two Covariates to be included : cpen, gleason
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Approaches in Estimating the Parameters to Calculate Power
Plan A• Complete specification of the parts for the
expression:
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Details
gleasoncopenvolCSVIy43210
_
gleasoncopeny430
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
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Full Model
Root MSE 30.98987 R-Square 0.4467
Dependent Mean 23.73013 Adj R-Sq 0.4226
Coeff Var 130.59291Predictors of interest
Note
Parameter Estimates
Variable DFParameter
EstimateStandard
Error t Value Pr > |t|
Intercept 1 -40.76878 33.24420 -1.23 0.2232
c_volume 1 2.02821 0.58404 3.47 0.0008
svi 1 17.85690 10.75049 1.66 0.1001
cpen 1 1.10381 1.32538 0.83 0.4071
gleason 1 6.39294 5.02522 1.27 0.2065
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Reduced Model
Root MSE 33.42074 R-Square 0.3424
Dependent Mean 23.73013 Adj R-Sq 0.3285
Coeff Var 140.83671
Note
R-Square difference
0.45 – 0.34=
0.11
Parameter Estimates
Variable DFParameter
EstimateStandard
Error t Value Pr > |t|
Intercept 1 -71.59827 34.91893 -2.05 0.0431
cpen 1 4.82868 1.01632 4.75 <.0001
gleason 1 12.28661 5.19873 2.36 0.0202
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proc power ;multreg model=fixedalpha= .05nfullpredictors= 4ntestpredictors= 2rsqfull=0.45rsqdiff=0.11ntotal= 97 80 70 60 50 40power=. ;plot x=n min=40 max=100key = oncurvesyopts=(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
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51. 45 95. 14
0. 8
0. 98
40 50 60 70 80 90 100
Tot al Sampl e Si ze
0. 65
0. 70
0. 75
0. 80
0. 85
0. 90
0. 95
1. 00
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Great, but I don’t have a dataset
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Pearson Correlation Coefficients, N = 97Prob > |r| under H0: Rho=0
psa c_volume svi cpen gleason
psa 1.00000 0.62415<.0001
0.52862<.0001
0.55079<.0001
0.42958<.0001
c_volume 0.62415<.0001
1.00000 0.58174<.0001
0.69290<.0001
0.48144<.0001
svi 0.52862<.0001
0.58174<.0001
1.00000 0.68028<.0001
0.42857<.0001
cpen 0.55079<.0001
0.69290<.0001
0.68028<.0001
1.00000 0.46157<.0001
gleason 0.42958<.0001
0.48144<.0001
0.42857<.0001
0.46157<.0001
1.00000
Use the Correlation Matrix
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Pearson Correlation Coefficients, N = 97Prob > |r| under H0: Rho=0
psa c_volume svi cpen gleason
psa 0.62415<.0001
0.52862<.0001
0.55079<.0001
0.42958<.0001
c_volume
svi
cpen
gleason
Piece 1Correlation of Y with all Predictors
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Pearson Correlation Coefficients, N = 97Prob > |r| under H0: Rho=0
psa c_volume svi cpen gleason
psa
c_volume 1.00000 0.58174<.0001
0.69290<.0001
0.48144<.0001
svi 0.58174<.0001
1.00000 0.68028<.0001
0.42857<.0001
cpen 0.69290<.0001
0.68028<.0001
1.00000 0.46157<.0001
gleason 0.48144<.0001
0.42857<.0001
0.46157<.0001
1.00000
Piece 2 Correlation of All Predictors with Each Other
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Pearson Correlation Coefficients, N = 97Prob > |r| under H0: Rho=0
psa c_volume svi cpen gleason
psa 0.55079<.0001
0.42958<.0001
c_volume
svi
cpen
gleason
Piece 3 Correlation of Y with Reduced Model Predictors
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Pearson Correlation Coefficients, N = 97Prob > |r| under H0: Rho=0
psa c_volume svi cpen gleason
psa
c_volume
svi
cpen 1.00000 0.46157<.0001
gleason 0.46157<.0001
1.00000
Piece 4Correlation of All Reduced Predictors with Each Other
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Matrix Arithmetic with Correlation Matrix
45.0
4.
6.
5.
6.
*
15.4.5.
5.17.7.
4.7.16.
5.7.6.1
*4.6.5.6.
1
2
FullR
34.04.
6.*
15.
5.1*4.6.2
Re
ducedR
11.034.045.02
Re
2 ducedFull
RR
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Hold on, we will find out to do this arithmetic later
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Different Rsquare Reductionsproc power ;multreg model=fixedalpha= .05nfullpredictors= 4ntestpredictors= 2rsqfull=0.45
rsqdiff=0.11 .10 .09 .08ntotal= 97 80 70 60 50 40power=. ;plot x=n min=40 max=100key = oncurvesyopts=(ref=0.8 .977 crossref=yes);run;
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51. 45
56. 25
62. 11 69. 44 95. 14
0. 8
0. 98
40 50 60 70 80 90 100
Tot al Sampl e Si ze
0. 5
0. 6
0. 7
0. 8
0. 9
1. 0
R- squar e Di ff =0. 11
R- squar e Di ff =0. 1
R- squar e Di ff =0. 09
R- squar e Di ff =0. 08
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Matrix Arithmetic with Compound Correlation Matrix
22.0
2.
2.
35.
35.
*
12.2.2.
2.12.2.
2.2.12.
2.2.2.1
*2.2.35.35.
1
2
FullR
07.02.
2.*
12.
2.1*2.2.2
Re
ducedR
15.007.022.02
Re
2 ducedFull
RR
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proc iml;%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
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proc power ;multreg model=fixedalpha= .05nfullpredictors= 4ntestpredictors= 2rsqfull=0.22rsqdiff=0.15 .16ntotal= 40 50 60 70power=. ;plot x=n min=40 max=100key = oncurvesyopts=(ref=0.8 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 0.22
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
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53. 38
50. 27
0. 8
40 50 60 70 80 90 100
Tot al Sampl e Si ze
0. 65
0. 70
0. 75
0. 80
0. 85
0. 90
0. 95
1. 00
R- squar e Di ff =0. 15
R- squar e Di ff =0. 16
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Plan B
• 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.
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Using Our Example
• 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
408.034.1
34.045.00
1 2
22
red
redfull
R
RR
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proc power ;multreg model=fixedalpha= .05nfullpredictors= 4 ntestpredictors= 2partialcorr= .408 .35ntotal= 97 80 60 50 40power=. ;plot x=n min=40 max=100key = oncurvesyopts=(ref=.8 .85 .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
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
Note n=4*10=40under powers
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51. 51 67. 9257. 92 76. 55 95. 27
0. 8
0. 85
0. 98
40 50 60 70 80 90 100
Tot al Sampl e Si ze
0. 5
0. 6
0. 7
0. 8
0. 9
1. 0
Par t i al Cor r =0. 408
Par t i al Cor r =0. 36
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Plan CUse the Table from Gatsonis and
Sampson (1989)
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U : the number of predictors of interest=2p : the total number of predictors in the model=4N = table value + p + 1For 80% power N = 72 + 4 + 1 = 77
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Proc Power and the Tableproc power ;multreg model=randomalpha= .05nfullpredictors= 4 ntestpredictors= 2partialcorr= .35 .40ntotal= 77power=. ;plot x=n min=60 max=120key = oncurvesyopts=(ref=.8 .90 crossref=yes);run;
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
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76. 73
99. 2775. 02
0. 8
0. 9
60 70 80 90 100 110 120
Tot al Sampl e Si ze
0. 65
0. 70
0. 75
0. 80
0. 85
0. 90
0. 95
1. 00
Par t i al Cor r =0. 35
Par t i al Cor r =0. 4
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Comments
• 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)
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Further Work for Somebody
• A corresponding multiple logistic regression approach, that is, powering more than one predictor of interest with additional covariates in the model.
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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, COJoint Statistical Meetings, Denver, CO August 3 – August 7, 2008August 3 – August 7, 2008
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Background
• Existing tools are based on Hsieh, Block, and Larsen (1998) paper, and Agresti (1996) text.– PASS– %powerlog macro
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Macro Details
• 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
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Results
0102030405060708090
100
50 75 100 150
N (Sample Size)
Po
wer
(1.5,1,1,1,1,1);rho=0.1 (2,2,1,1,1,1);rho=0 (2,2,1,1,1,1);rho=0.1
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End
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Plan CExchangeable Matrix in Plan A
12.2.2.
2.12.2.
2.2.12.
2.2.2.1
FullRxx
2.2.2.35.35.` Full
xy
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2.2.2.2.2.R̀e
duced
12.2.2.2.
.12.2.2.
..12.2.
...12.
....1
Re ducedRxx
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Pearson Correlation Coefficients, N = 97
psa c_volume svi cpen gleason c_wt age bph
psa 1.00000 0.62415 0.52862 0.55079 0.42958 0.02621 0.01720 -0.01649
c_volume
0.62415 1.00000 0.58174 0.69290 0.48144 0.00511 0.03909 -0.13321
svi 0.52862 0.58174 1.00000 0.68028 0.42857 -0.00241 0.11766 -0.11955
cpen 0.55079 0.69290 0.68028 1.00000 0.46157 0.00158 0.09956 -0.08301
gleason
0.42958 0.48144 0.42857 0.46157 1.00000 -0.02421 0.22585 0.02683
c_wt 0.02621 0.00511 -0.00241 0.00158 -0.02421 1.00000 0.16432 0.32185
age 0.01720 0.03909 0.11766 0.09956 0.22585 0.16432 1.00000 0.36634
bph -0.01649 -0.13321 -0.11955 -0.08301 0.02683 0.32185 0.36634 1.00000
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Full Correlation Matrix
psa c_volume svi cpen gleason c_wt age bph
psa 1 0.624151 0.528619 0.550793 0.42958 0.026213 0.017199 -0.01649
c_volume 0.624151 1 0.581742 0.692897 0.481438 0.005107 0.039094 -0.13321
svi 0.528619 0.581742 1 0.680284 0.428573 -0.00241 0.117658 -0.11955
cpen 0.550793 0.692897 0.680284 1 0.461566 0.001579 0.099555 -0.08301
gleason 0.42958 0.481438 0.428573 0.461566 1 -0.02421 0.225852 0.026826
c_wt 0.026213 0.005107 -0.00241 0.001579 -0.02421 1 0.164324 0.321849
age 0.017199 0.039094 0.117658 0.099555 0.225852 0.164324 1 0.366341
bph -0.01649 -0.13321 -0.11955 -0.08301 0.026826 0.321849 0.366341 1
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The Correlation of Y with All X’sFull Model
psa c_volume svi cpen gleason c_wt age bph
psa 1 0.624151 0.528619 0.550793 0.42958 0.026213 0.017199 -0.01649
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Correlation Matrix of X’sFull Model
psa c_volume svi cpen gleason c_wt age bph
psa
c_volume 1 0.581742 0.692897 0.481438 0.005107 0.039094 -0.13321
svi 0.581742 1 0.680284 0.428573 -0.00241 0.117658 -0.11955
cpen 0.692897 0.680284 1 0.461566 0.001579 0.099555 -0.08301
gleason 0.481438 0.428573 0.461566 1 -0.02421 0.225852 0.026826
c_wt 0.005107 -0.00241 0.001579 -0.02421 1 0.164324 0.321849
age 0.039094 0.117658 0.099555 0.225852 0.164324 1 0.366341
bph -0.13321 -0.11955 -0.08301 0.026826 0.321849 0.366341 1
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The Correlation of Y with All X’sReduced Model
psa c_volume svi cpen gleason c_wt age bph
psa 0.550793 0.42958 0.026213 0.017199 -0.01649
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Correlation Matrix of X’s
psa c_volume svi cpen gleason c_wt age bph
psa
c_volume
svi
cpen 1 0.461566 0.001579 0.099555 -0.08301
gleason 0.461566 1 -0.02421 0.225852 0.026826
c_wt 0.001579 -0.02421 1 0.164324 0.321849
age 0.099555 0.225852 0.164324 1 0.366341
bph -0.08301 0.026826 0.321849 0.366341 1
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Regular CorrelationsVersus
Partial Correlations3 Variables: psa c_volume svi
Pearson Correlation Coefficients, N = 97 Prob > |r| under H0: Rho=0
psa c_volume svi
psa 1.00000
0.62415 <.0001
0.52862 <.0001
c_volume 0.62415 <.0001
1.00000
0.58174 <.0001
svi 0.52862 <.0001
0.58174 <.0001
1.00000
5 Partial Variables: cpen gleason c_wt age bph
3 Variables: psa c_volume svi
Pearson Partial Correlation Coefficients, N = 97 Prob > |r| under H0: Partial Rho=0
psa c_volume svi
psa 1.00000
0.36564 0.0003
0.23248 0.0257
c_volume 0.36564 0.0003
1.00000
0.16518 0.1156
svi 0.23248 0.0257
0.16518 0.1156
1.00000
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Correlation Matrix
Obs psa c_volume svi cpen gleason c_wt age bph
1 1.00000 0.62415 0.52862 0.55079 0.42958 0.02621 0.01720 -0.01649
2 0.62415 1.00000 0.58174 0.69290 0.48144 0.00511 0.03909 -0.13321
3 0.52862 0.58174 1.00000 0.68028 0.42857 -0.00241 0.11766 -0.11955
4 0.55079 0.69290 0.68028 1.00000 0.46157 0.00158 0.09956 -0.08301
5 0.42958 0.48144 0.42857 0.46157 1.00000 -0.02421 0.22585 0.02683
6 0.02621 0.00511 -0.00241 0.00158 -0.02421 1.00000 0.16432 0.32185
7 0.01720 0.03909 0.11766 0.09956 0.22585 0.16432 1.00000 0.36634
8 -0.01649 -0.13321 -0.11955 -0.08301 0.02683 0.32185 0.36634 1.00000
Full R xyReduced Rxy
X’s of interest
Covariates in reduced model Rxx
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Correlation Matrix
Obs psa c_volume svi cpen gleason c_wt age bph
1 1.00000 0.62415 0.52862 0.55079 0.42958 0.02621 0.01720 -0.01649
2 0.62415 1.00000 0.58174 0.69290 0.48144 0.00511 0.03909 -0.13321
3 0.52862 0.58174 1.00000 0.68028 0.42857 -0.00241 0.11766 -0.11955
4 0.55079 0.69290 0.68028 1.00000 0.46157 0.00158 0.09956 -0.08301
5 0.42958 0.48144 0.42857 0.46157 1.00000 -0.02421 0.22585 0.02683
6 0.02621 0.00511 -0.00241 0.00158 -0.02421 1.00000 0.16432 0.32185
7 0.01720 0.03909 0.11766 0.09956 0.22585 0.16432 1.00000 0.36634
8 -0.01649 -0.13321 -0.11955 -0.08301 0.02683 0.32185 0.36634 1.00000
Full R xyReduced Rxy
X’s of interest
Covariates in reduced model Rxx
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Correlation Matrix
Obs psa c_volume svi cpen gleason c_wt age bph
1 1.00000 0.62415 0.52862 0.55079 0.42958 0.02621 0.01720 -0.01649
2 0.62415 1.00000 0.58174 0.69290 0.48144 0.00511 0.03909 -0.13321
3 0.52862 0.58174 1.00000 0.68028 0.42857 -0.00241 0.11766 -0.11955
4 0.55079 0.69290 0.68028 1.00000 0.46157 0.00158 0.09956 -0.08301
5 0.42958 0.48144 0.42857 0.46157 1.00000 -0.02421 0.22585 0.02683
6 0.02621 0.00511 -0.00241 0.00158 -0.02421 1.00000 0.16432 0.32185
7 0.01720 0.03909 0.11766 0.09956 0.22585 0.16432 1.00000 0.36634
8 -0.01649 -0.13321 -0.11955 -0.08301 0.02683 0.32185 0.36634 1.00000
Full R xyReduced Rxy
X’s of interest
Covariates in reduced model Rxx
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The Gold Standard ApproachSome Matrix Algebra
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=0.35
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The Gold Standard ApproachSome Matrix Algebra
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=0.35
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Pearson Correlation Coefficients, N = 97Prob > |r| under H0: Rho=0
psa c_volume svi cpen gleason
psa
c_volume 1.00000 0.58174<.0001
0.69290<.0001
0.48144<.0001
svi 0.58174<.0001
1.00000 0.68028<.0001
0.42857<.0001
cpen 0.69290<.0001
0.68028<.0001
1.00000 0.46157<.0001
gleason 0.48144<.0001
0.42857<.0001
0.46157<.0001
1.00000
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Pearson Correlation Coefficients, N = 97Prob > |r| under H0: Rho=0
cpen gleason
cpen 1.00000 0.46157<.0001
gleason 0.46157<.0001
1.00000
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Pearson Correlation Coefficients, N = 97Prob > |r| under H0: Rho=0
psa c_volume svi cpen gleason
psa 0.62415<.0001
0.52862<.0001
0.55079<.0001
0.42958<.0001
c_volume
svi
cpen
gleason
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Pearson Correlation Coefficients, N = 97Prob > |r| under H0: Rho=0
psa c_volume svi cpen gleason
psa 0.55079<.0001
0.42958<.0001
c_volume
svi
cpen
gleason
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Full Correlation Matrix
psa c_volume svi cpen gleason c_wt age bph
psa 1 0.624151 0.528619 0.550793 0.42958 0.026213 0.017199 -0.01649
c_volume 0.624151 1 0.581742 0.692897 0.481438 0.005107 0.039094 -0.13321
svi 0.528619 0.581742 1 0.680284 0.428573 -0.00241 0.117658 -0.11955
cpen 0.550793 0.692897 0.680284 1 0.461566 0.001579 0.099555 -0.08301
gleason 0.42958 0.481438 0.428573 0.461566 1 -0.02421 0.225852 0.026826
c_wt 0.026213 0.005107 -0.00241 0.001579 -0.02421 1 0.164324 0.321849
age 0.017199 0.039094 0.117658 0.099555 0.225852 0.164324 1 0.366341
bph -0.01649 -0.13321 -0.11955 -0.08301 0.026826 0.321849 0.366341 1
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The Correlation of Y with All X’sFull Model
psa c_volume svi cpen gleason c_wt age bph
psa 0.624151 0.528619 0.550793 0.42958 0.026213 0.017199 -0.01649
)( fullyx
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Correlation Matrix of X’sFull Model
psa c_volume svi cpen gleason c_wt age bph
psa
c_volume 1 0.581742 0.692897 0.481438 0.005107 0.039094 -0.13321
svi 0.581742 1 0.680284 0.428573 -0.00241 0.117658 -0.11955
cpen 0.692897 0.680284 1 0.461566 0.001579 0.099555 -0.08301
gleason 0.481438 0.428573 0.461566 1 -0.02421 0.225852 0.026826
c_wt 0.005107 -0.00241 0.001579 -0.02421 1 0.164324 0.321849
age 0.039094 0.117658 0.099555 0.225852 0.164324 1 0.366341
bph -0.13321 -0.11955 -0.08301 0.026826 0.321849 0.366341 1
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The Correlation of Y with All X’sReduced Model
psa c_volume svi cpen gleason c_wt age bph
psa 0.550793 0.42958 0.026213 0.017199 -0.01649
)( reducedyx
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Correlation Matrix of X’s
psa c_volume svi cpen gleason c_wt age bph
psa
c_volume
svi
cpen 1 0.461566 0.001579 0.099555 -0.08301
gleason 0.461566 1 -0.02421 0.225852 0.026826
c_wt 0.001579 -0.02421 1 0.164324 0.321849
age 0.099555 0.225852 0.164324 1 0.366341
bph -0.08301 0.026826 0.321849 0.366341 1
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The Calculations
05.1825.01
11.025.097
1 2
2
Re
2
Full
dFull
R
RRN
Power = 0.97
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• proc power ;• multreg • model=fixed• alpha= .05• nfullpredictors= 7 • ntestpredictors= 2• rsqfull=0.2505682• rsqdiff=0.1111111• ntotal= 50 60 70 80 97 • power=. ;• plot x=n min=60 max=100• key = oncurves• yopts=(ref=.8 .85 .9 .95 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 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
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62. 09 71. 35 86. 28
0. 85
0. 9
0. 95
60 70 80 90 100
Tot al Sampl e Si ze
0. 825
0. 850
0. 875
0. 900
0. 925
0. 950
0. 975
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Pearson Correlation Coefficients, N = 97Prob > |r| under H0: Rho=0
psa c_volume svi cpen gleason
psa 1.00000 .35 .35 .2 .2
c_volume .35 1.00000 .2 .2 .2
svi .35 .2 1.00000 .2 .2
cpen .2 .2 .2 1.00000 .2
gleason .2 .2 .2 .2 1.00000
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Matrix Arithmetic with Compound Correlation Matrix
22.0
2.
2.
35.
35.
*
12.2.2.
2.12.2.
2.2.12.
2.2.2.1
*2.2.35.35.
1
2
FullR
07.02.
2.*
12.
2.1*2.2.2
Re
ducedR
15.007.022.02
Re
2 ducedFull
RR
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proc power ;multreg model=fixedalpha= .05nfullpredictors= 4ntestpredictors= 2rsqfull=0.22rsqdiff=0.15 .16ntotal= 40 50 60 70power=. ;plot x=n min=40 max=100key = oncurvesyopts=(ref=0.8 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 0.22
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
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Calculations
2
2Re
2
1 Full
ducedFull
R
RRN
)1,(),1,([ 111 pNpFpNpFPPower
The number of predictors of interest 2
The total number of predictors in the model 4
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Approaches in Estimating the Parameters to Calculate Power
Plan A• Complete specification of the parts for the
expression:
4.1945.01
34.045.097
1 2
2
Re
2
Full
ducedFull
R
RRN
= 0.34
= 0.45
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Approaches in Estimating the Parameters to Calculate Power
Plan A• Complete specification of the parts for the
expression:
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p0
0. 0
0. 1
0. 2
0. 3
0. 4
0. 5
0. 6
0. 7
0. 8
0. 9
1. 0
z
0 10 20 30 40 50
F(2,92)
F(2,92,19.4)
Critical Value for alpha = .05
3.07
Noncentrality Parameter
19.4
Total area in blue.Power = 0.97