This example demonstrates a twosided group sequential test that uses an error spending design with early stopping to reject
the null hypothesis. A study is conducted to examine the effects of Age
(years), Weight
(kg), RunTime
(time in minutes to run 1.5 miles), RunPulse
(heart rate while running), and MaxPulse
(maximum heart rate recorded while running) on Oxygen
(oxygen intake rate, ml per kg body weight per minute). The primary interest is whether oxygen intake rate is associated
with weight.
The hypothesis is tested using the following linear model:

The null hypothesis is , where is the regression parameter for the variable Weight
. Suppose that is the reference improvement that should be detected at a 0.90 level. Then the maximum information can be derived in the SEQDESIGN procedure.
Following the derivations in the section “Test for a Parameter in the Regression Model” in the chapter “The SEQDESIGN Procedure,” the required sample size can be derived from

where is the variance of the response variable in the regression model, is the proportion of variance of Weight
explained by other covariates, and is the variance of Weight
.
Further suppose that from past experience, , , and . Then the required sample size can be derived using the SAMPLESIZE statement in the SEQDESIGN procedure.
The following statements invoke the SEQDESIGN procedure and request a threestage group sequential design for normally distributed data to test the null hypothesis of a regression parameter against the alternative :
ods graphics on; proc seqdesign altref=0.10; OBFErrorFunction: design method=errfuncobf nstages=3 info=cum(2 3 4) ; samplesize model=reg(variance=5 xvariance=64 xrsquare=0.10); ods output Boundary=Bnd_Fit; run; ods graphics off;
By default (or equivalently if you specify ALPHA=0.05 and BETA=0.10), the procedure uses a Type I error probability 0.05 and a Type II error probability 0.10. The ALTREF=0.10 option specifies a power of at the alternative hypothesis . The INFO=CUM(2 3 4) option specifies that the study perform the first interim analysis with information proportion —that is, after half of the total observations are collected.
The ODS OUTPUT statement with the BOUNDARY=BND_FIT option creates an output data set named BND_FIT
which contains the resulting boundary information for the subsequent sequential tests.
The “Design Information” table in Output 84.2.1 displays design specifications and derived statistics. Since the alternative reference is specified, the maximum information is derived.
Output 84.2.1: Design Information
Design Information  

Statistic Distribution  Normal 
Boundary Scale  Standardized Z 
Alternative Hypothesis  TwoSided 
Early Stop  Reject Null 
Method  Error Spending 
Boundary Key  Both 
Alternative Reference  0.1 
Number of Stages  3 
Alpha  0.05 
Beta  0.1 
Power  0.9 
Max Information (Percent of Fixed Sample)  101.8276 
Max Information  1069.948 
Null Ref ASN (Percent of Fixed Sample)  101.2587 
Alt Ref ASN (Percent of Fixed Sample)  77.81586 
The “Boundary Information” table in Output 84.2.2 displays information level, alternative reference, and boundary values at each stage.
Output 84.2.2: Boundary Information
Boundary Information (Standardized Z Scale) Null Reference = 0 


_Stage_  Alternative  Boundary Values  
Information Level  Reference  Lower  Upper  
Proportion  Actual  N  Lower  Upper  Alpha  Alpha  
1  0.5000  534.9738  46.43869  2.31295  2.31295  2.96259  2.96259 
2  0.7500  802.4606  69.65804  2.83277  2.83277  2.35902  2.35902 
3  1.0000  1069.948  92.87739  3.27101  3.27101  2.01409  2.01409 
With ODS Graphics enabled, a detailed boundary plot with the rejection and acceptance regions is displayed, as shown in Output 84.2.3. The boundary plot also displays the information level and critical value for the corresponding fixedsample design. This design has characteristics of an O’BrienFleming design; the probability for early stopping is low, and the maximum information and critical values at the final stage are similar to those of the corresponding fixedsample design.
Output 84.2.3: Boundary Plot
With the MODEL=REG option in the SAMPLESIZE statement, the “Sample Size Summary” table in Output 84.2.4 displays the parameters for the sample size computation.
Output 84.2.4: Required Sample Size Summary
Sample Size Summary  

Test  Reg Parameter 
Parameter  0.1 
Variance  5 
X Variance  64 
R Square (X)  0.1 
Max Sample Size  92.87739 
Expected Sample Size (Null Ref)  92.35845 
Expected Sample Size (Alt Ref)  70.97617 
The “Sample Sizes” table in Output 84.2.5 displays the required sample sizes for the group sequential clinical trial.
Output 84.2.5: Required Sample Sizes
Sample Sizes (N) Z Test for Regression Parameter 


_Stage_  Fractional N  Ceiling N  
N  Information  N  Information  
1  46.44  535.0  47  541.4 
2  69.66  802.5  70  806.4 
3  92.88  1069.9  93  1071.4 
Thus, 47, 70, and 93 individuals are needed in stages 1, 2, and 3, respectively. Since the sample sizes are derived from estimated values of , , and , the actual information levels might not achieve the target information levels. Thus, instead of specifying sample sizes in the protocol, you can specify the maximum information levels. Then if an actual information level is much less than the target level, you can increase the sample sizes for the remaining stages to achieve the desired information levels and power.
Suppose that 47 individuals are available at stage 1. Output 84.2.6 lists the first 10 observations of the trial data.
Output 84.2.6: Clinical Trial Data
First 10 Obs in the Trial Data 
Obs  Oxygen  Age  Weight  RunTime  RunPulse  MaxPulse 

1  54.5521  44  87.7676  11.6949  178.435  181.607 
2  52.2821  40  75.4853  9.8872  184.433  183.667 
3  62.1871  44  89.0638  8.7950  155.540  167.108 
4  65.3269  42  67.7310  8.4577  162.926  173.877 
5  59.9809  37  93.1902  9.3228  179.033  180.144 
6  52.5588  47  75.9044  12.0385  177.753  175.033 
7  51.7838  40  73.5422  11.6607  175.838  178.140 
8  57.0024  43  81.2861  11.2219  160.963  171.770 
9  48.0775  44  85.2290  13.1789  173.722  176.548 
10  68.3357  38  80.2490  8.5066  171.824  184.011 
The following statements use the REG procedure to estimate the slope and its associated standard error at stage 1:
proc reg data=Fit_1; model Oxygen=Age Weight RunTime RunPulse MaxPulse; ods output ParameterEstimates=Parms_Fit1; run;
The following statements create and display (in Output 84.2.7) the input data set that contains slope and its associated standard error for the SEQTEST procedure:
data Parms_Fit1; set Parms_Fit1; if Variable='Weight'; _Scale_='MLE'; _Stage_= 1; keep _Scale_ _Stage_ Variable Estimate StdErr; run; proc print data=Parms_Fit1; title 'Statistics Computed at Stage 1'; run;
Output 84.2.7: Statistics Computed at Stage 1
Statistics Computed at Stage 1 
Obs  Variable  Estimate  StdErr  _Scale_  _Stage_ 

1  Weight  0.03772  0.04345  MLE  1 
The following statements invoke the SEQTEST procedure to test for early stopping at stage 1:
ods graphics on; proc seqtest Boundary=Bnd_Fit Parms(Testvar=Weight)=Parms_Fit1 infoadj=prop errspendadj=errfuncobf order=lr stopprob ; ods output Test=Test_Fit1; run; ods graphics off;
The BOUNDARY= option specifies the input data set that provides the boundary information for the trial at stage 1 (this data
set was generated in the SEQDESIGN procedure). Recall that these boundary values were derived for the information levels specified
with the INFO=CUM(2 3 4) option in the SEQDESIGN procedure. The PARMS=PARMS_FIT1 option specifies the input data set PARMS_FIT1
that contains the test statistic and its associated standard error at stage 1, and the TESTVAR=WEIGHT option identifies the
test variable WEIGHT
in the data set.
If the computed information level for stage 1 is not the same as the value provided in the BOUNDARY= data set, the INFOADJ=PROP option (which is the default) proportionally adjusts the information levels at future interim stages from the levels provided in the BOUNDARY= data set. The ORDER=LR option uses the LR ordering to derive the pvalue, the unbiased median estimate, and the confidence limits for the regression slope estimate. The ERRSPENDADJ=ERRFUNCOBF option adjusts the boundaries with the updated error spending values generated from an O’BrienFlemingtype cumulative error spending function.
The ODS OUTPUT statement with the TEST=TEST_FIT1 option creates an output data set named TEST_FIT1
which contains the updated boundary information for the test at stage 1. The adjustment is needed because the observed information
level is different from the information level in the BOUNDARY= data set. The data set TEST_FIT1
also provides the boundary information that is needed for the group sequential test at the next stage.
The “Design Information” table in Output 84.2.8 displays the design specifications. By default (or equivalently if you specify BOUNDARYKEY=ALPHA), the boundary values are modified for the new information levels to maintain the Type I level. The maximum information remains the same as in the BOUNDARY= data set, but the derived Type II error probability and power are different because of the new information level.
Output 84.2.8: Design Information
Design Information  

BOUNDARY Data Set  WORK.BND_FIT 
Data Set  WORK.PARMS_FIT1 
Statistic Distribution  Normal 
Boundary Scale  Standardized Z 
Alternative Hypothesis  TwoSided 
Early Stop  Reject Null 
Number of Stages  3 
Alpha  0.05 
Beta  0.09994 
Power  0.90006 
Max Information (Percent of Fixed Sample)  101.8057 
Max Information  1069.94751 
Null Ref ASN (Percent of Fixed Sample)  101.2416 
Alt Ref ASN (Percent of Fixed Sample)  77.87607 
With the STOPPROB option, the “Expected Cumulative Stopping Probabilities” table in Output 84.2.9 displays the expected stopping stage and cumulative stopping probability to reject the null hypothesis at each stage under various hypothetical references , where is the alternative reference and by default. You can specify other values for with the CREF= option.
Output 84.2.9: Stopping Probabilities
Expected Cumulative Stopping Probabilities Reference = CRef * (Alt Reference) 


CRef  Expected Stopping Stage 
Source  Stopping Probabilities  
Stage_1  Stage_2  Stage_3  
0.0000  2.978  Reject Null  0.00289  0.01906  0.05000 
0.5000  2.792  Reject Null  0.03373  0.17443  0.36566 
1.0000  2.069  Reject Null  0.24884  0.68206  0.90006 
1.5000  1.348  Reject Null  0.68172  0.97032  0.99820 
The “Test Information” table in Output 84.2.10 displays the boundary values for the test statistic. By default (or equivalently if you specify BOUNDARYSCALE=STDZ), these statistics are displayed with the standardized Z scale. The information level at stage 1 is derived from the standard error in the PARMS= data set,

Output 84.2.10: Sequential Tests
Test Information (Standardized Z Scale) Null Reference = 0 


_Stage_  Alternative  Boundary Values  Test  
Information Level  Reference  Lower  Upper  Weight  
Proportion  Actual  Lower  Upper  Alpha  Alpha  Estimate  Action  
1  0.4950  529.6232  2.30135  2.30135  2.97951  2.97951  0.86798  Continue 
2  0.7475  799.7853  2.82805  2.82805  2.36291  2.36291  .  
3  1.0000  1069.948  3.27101  3.27101  2.01336  2.01336  . 
With the INFOADJ=PROP option (which is the default), the information level at stage 2 is derived proportionally from the observed information at stage 1 and the information levels in the BOUNDARY= data set. See the section Boundary Adjustments for Information Levels for details about how the adjusted information levels are computed.
At stage 1, the standardized Z statistic 0.86798 is between the lower boundary –2.97951 and the upper boundary 2.97951, so the trial continues to the next stage.
With ODS Graphics enabled, a boundary plot with test statistics is displayed, as shown in Output 84.2.11. As expected, the test statistic is in the continuation region between the lower and upper boundaries.
Output 84.2.11: Sequential Test Plot
The following statements use the REG procedure to estimate the slope and its associated standard error at stage 2:
proc reg data=Fit_2; model Oxygen=Age Weight RunTime RunPulse MaxPulse; ods output ParameterEstimates=Parms_Fit2; run;
Note that the data set Fit_2
contains both the data from stage 1 and the data from stage 2.
The following statements create and display (in Output 84.2.12) the input data set that contains slope and its associated standard error at stage 2 for the SEQTEST procedure:
data Parms_Fit2; set Parms_Fit2; if Variable='Weight'; _Scale_='MLE'; _Stage_= 2; keep _Scale_ _Stage_ Variable Estimate StdErr; run; proc print data=Parms_Fit2; title 'Statistics Computed at Stage 2'; run;
Output 84.2.12: Statistics Computed at Stage 2
Statistics Computed at Stage 2 
Obs  Variable  Estimate  StdErr  _Scale_  _Stage_ 

1  Weight  0.02932  0.03520  MLE  2 
The following statements invoke the SEQTEST procedure to test for early stopping at stage 2:
proc seqtest Boundary=Test_Fit1 Parms(Testvar=Weight)=Parms_Fit2 infoadj=prop errspendadj=errfuncobf order=lr ; ods output Test=Test_Fit2; run;
The BOUNDARY= option specifies the input data set that provides the boundary information for the trial at stage 2, which was generated by the SEQTEST procedure at the previous stage. The PARMS= option specifies the input data set that contains the test statistic and its associated standard error at stage 2, and the TESTVAR= option identifies the test variable in the data set.
The ODS OUTPUT statement with the TEST=TEST_FIT2 option creates an output data set named TEST_FIT2
which contains the updated boundary information for the test at stage 2. The data set also provides the boundary information
that is needed for the group sequential test at the next stage.
Since the data set PARMS_FIT2
does not contain the test information at stage 1, the information level at stage 1 in the TEST_FIT1
data set is used to generate boundary values for the test at stage 2.
Following the process at stage 1, the slope estimate is also between its corresponding lower and upper boundary values, so the trial continues to the next stage.
The following statements use the REG procedure to estimate the slope and its associated standard error at the final stage:
proc reg data=Fit_3; model Oxygen=Age Weight RunTime RunPulse MaxPulse; ods output ParameterEstimates=Parms_Fit3; run;
The following statements create the input data set that contains slope and its associated standard error at stage 3 for the SEQTEST procedure:
data Parms_Fit3; set Parms_Fit3; if Variable='Weight'; _Scale_='MLE'; _Stage_= 3; keep _Scale_ _Stage_ Variable Estimate StdErr; run;
The following statements print (in Output 84.2.13) the test statistics at stage 3:
proc print data=Parms_Fit3; title 'Statistics Computed at Stage 3'; run;
Output 84.2.13: Statistics Computed at Stage 3
Statistics Computed at Stage 3 
Obs  Variable  Estimate  StdErr  _Scale_  _Stage_ 

1  Weight  0.02189  0.03028  MLE  3 
The following statements invoke the SEQTEST procedure to test the hypothesis:
ods graphics on; proc seqtest Boundary=Test_Fit2 Parms(testvar=Weight)=Parms_Fit3 errspendadj=errfuncobf order=lr pss plots=(asn power) ; ods output Test=Test_Fit3; run; ods graphics off;
The BOUNDARY= option specifies the input data set that provides the boundary information for the trial at stage 3, which was generated by the SEQTEST procedure at the previous stage. The PARMS= option specifies the input data set that contains the test statistic and its associated standard error at stage 3, and the TESTVAR= option identifies the test variable in the data set.
The ODS OUTPUT statement with the TEST=TEST_FIT3 option creates an output data set named TEST_FIT3
which contains the updated boundary information for the test at stage 3.
The “Design Information” table in Output 84.2.14 displays design specifications. By default (or equivalently if you specify BOUNDARYKEY=ALPHA), the boundary values are modified for the new information levels to maintain the Type I level.
Output 84.2.14: Design Information
Design Information  

BOUNDARY Data Set  WORK.TEST_FIT2 
Data Set  WORK.PARMS_FIT3 
Statistic Distribution  Normal 
Boundary Scale  Standardized Z 
Alternative Hypothesis  TwoSided 
Early Stop  Reject Null 
Number of Stages  3 
Alpha  0.05 
Beta  0.09514 
Power  0.90486 
Max Information (Percent of Fixed Sample)  102.0102 
Max Information  1090.63724 
Null Ref ASN (Percent of Fixed Sample)  101.4122 
Alt Ref ASN (Percent of Fixed Sample)  77.22139 
The maximum information is derived from the standard error associated with the slope estimate at the final stage and is larger than the target level. The derived Type II error probability and power are different because of the new information levels.
With the PSS option, the “Power and Expected Sample Sizes” table in Output 84.2.15 displays powers and expected mean sample sizes under various hypothetical references , where is the alternative reference and by default. You can specify the values with the CREF= option.
Output 84.2.15: Power and Expected Sample Size Information
Powers and Expected Sample Sizes Reference = CRef * (Alt Reference) 


CRef  Power  Sample Size 
Percent FixedSample 

0.0000  0.02500  101.4122 
0.5000  0.37046  96.3754 
1.0000  0.90486  77.2214 
1.5000  0.99844  58.5301 
With the PLOTS=ASN option, the procedure displays a plot of expected sample sizes under various hypothetical references, as shown in Output 84.2.16. By default, expected sample sizes under the hypotheses , , are displayed, where is the alternative reference.
Output 84.2.16: ASN Plot
With the PLOTS=POWER option, the procedure displays a plot of powers under various hypothetical references, as shown in Output 84.2.17. By default, powers under hypothetical references are displayed, where by default. You can specify values with the CREF= option. The values are displayed on the horizontal axis.
Output 84.2.17: Power Plot
Under the null hypothesis, , the power is 0.025, which is the upper Type I error probability. Under the alternative hypothesis, , the power is 0.90486, which is one minus the Type II error probability.
The “Test Information” table in Output 84.2.18 displays the boundary values for the test statistic with the default standardized Z scale. The information level at the current stage is derived from the standard error for the current stage in the PARMS=
data set. At stage 3, the standardized slope estimate 0.72284 is still between the lower and upper boundary values. Since it is the final stage, the trial stops to accept the null hypothesis that the variable Weight
has no effect on the oxygen intake rate after adjusting for other covariates.
Output 84.2.18: Sequential Tests
Test Information (Standardized Z Scale) Null Reference = 0 


_Stage_  Alternative  Boundary Values  Test  
Information Level  Reference  Lower  Upper  Weight  
Proportion  Actual  Lower  Upper  Alpha  Alpha  Estimate  Action  
1  0.4856  529.6232  2.30135  2.30135  2.97951  2.97951  0.86798  Continue 
2  0.7401  807.1954  2.84112  2.84112  2.34945  2.34945  0.83305  Continue 
3  1.0000  1090.637  3.30248  3.30248  2.01885  2.01885  0.72284  Accept Null 
Since the data set FIT_3
contains the test information only at stage 3, the information levels at previous stages in the TEST_FIT2
data set are used to generate boundary values for the test.
With ODS Graphics enabled, a boundary plot with test statistics is displayed, as shown in Output 84.2.19. As expected, the test statistic is in the acceptance region between the lower and upper boundaries at the final stage.
Output 84.2.19: Sequential Test Plot
After a trial is stopped, the “Parameter Estimates” table in Output 84.2.20 displays the stopping stage, parameter estimate, unbiased median estimate, confidence limits, and the pvalue under the null hypothesis .
Output 84.2.20: Parameter Estimates
Parameter Estimates LR Ordering 


Parameter  Stopping Stage 
MLE  pValue for H0:Parm=0 
Median Estimate 
95% Confidence Limits  
Weight  3  0.021888  0.4699  0.021884  0.03747  0.08123 
As expected, the pvalue 0.4699 is not significant at the level, and the confidence interval does contain the value zero. The pvalue, unbiased median estimate, and confidence limits depend on the ordering of the sample space , where k is the stage number and z is the standardized Z statistic. With the specified LR ordering, the pvalues are computed with the ordering if . See the section Available Sample Space Orderings in a Sequential Test for a detailed description of the LR ordering.