The SEQTEST Procedure
- Overview
- Getting Started
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Syntax
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Details
Input Data Sets Boundary Variables Information Level Adjustments at Future Stages Boundary Adjustments for Information Levels Boundary Adjustments for Minimum Error Spending Boundary Adjustments for Overlapping Lower and Upper beta Boundaries Stochastic Curtailment Repeated Confidence Intervals Analysis after a Sequential Test Available Sample Space Orderings in a Sequential Test Applicable Tests and Sample Size Computation Table Output ODS Table Names Graphics Output ODS Graphics Acknowledgments
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Examples
Testing the Difference between Two Proportions Testing an Effect in a Regression Model Testing an Effect with Early Stopping to Accept H0 Testing a Binomial Proportion Comparing Two Proportions with a Log Odds Ratio Test Comparing Two Survival Distributions with a Log-Rank Test Testing an Effect in a Proportional Hazards Regression Model Testing an Effect in a Logistic Regression Model
- References
Examples: SEQTEST Procedure
The following examples perform group sequential tests with various designs and test statistics.
Four statistic scales are available for the input boundary values, the input test statistic, and the displayed test information in the SEQTEST procedure. These are the maximum likelihood estimator scale, score statistic scale, standardized normal 
scale, and 
-value scale. There is a unique one-to-one transformation between any two of the scales, and you can use different scales for the input boundary values and input test statistic. These boundary values and test statistic are displayed with the scale specified in the BOUNDARYSCALE= option in the SEQTEST procedure.
- Testing the Difference between Two Proportions
- Testing an Effect in a Regression Model
- Testing an Effect with Early Stopping to Accept H0
- Testing a Binomial Proportion
- Comparing Two Proportions with a Log Odds Ratio Test
- Comparing Two Survival Distributions with a Log-Rank Test
- Testing an Effect in a Proportional Hazards Regression Model
- Testing an Effect in a Logistic Regression Model