
BETABOUNDARY=BINDING  NONBINDING

specifies whether the boundary is used in the computation of the Type I error level . The BETABOUNDARY=BINDING option computes the Type I error probability with the (acceptance) boundary, and the BETABOUNDARY=NONBINDING suboption computes the Type I error probability without the boundary (Zhu, Ni, and Yao, 2011, pp. 132–133). For a detailed description of nonbinding acceptance boundary, see the section "Acceptance () Boundary" in Chapter 89: The SEQDESIGN Procedure. The default is BETABOUNDARY=BINDING.

BETAOVERLAP=ADJUST  NOADJUST
OVERLAP=ADJUST  NOADJUST

specifies whether to check for overlapping of the lower and upper boundaries for the two corresponding onesided tests at the current and subsequent interim stages. This option applies to
twosided designs with early stopping to accept , or to either accept or reject . This type of overlapping might result from a small spending at an interim stage. When you specify BETAOVERLAP=ADJUST, the procedure checks for this type of overlapping at the
current and subsequent interim stages. If such overlapping is found, the boundaries for the twosided design at that stage are set to missing, and the spending values at subsequent stages are adjusted, as described in the section Boundary Adjustments for Overlapping Lower and Upper beta Boundaries.
You can specify BETAOVERLAP=NOADJUST to request that no adjustment be made. The default is BETAOVERLAP=ADJUST.

BOUNDARY=SASdataset

names the required SAS data set that contains the design boundary information. At stage 1, the data set is usually created from the "Boundary Information" table created by the SEQDESIGN procedure. At each
subsequent stage, the data set is usually created from the "Test Information" table created by the SEQTEST procedure at the
previous stage. The data set includes the variables _Scale_
for the boundary scale, _Stop_
for the stopping criterion, and _ALT_
for the type of alternative hypothesis. It also includes _Stage_
for the stage number, Info_Prop
for the information proportion, and a set of the boundary variables from Bound_LA
, Bound_LB
, Bound_UB
, and Bound_UA
for boundary values at each stage.
The data set might also include _Info_
for the actual information level, NObs
for the number of observation, and Events
for the number of events required at each stage.

BOUNDARYKEY=ALPHA  BETA  BOTH

specifies the boundary key to be maintained in the boundary adjustments. The BOUNDARYKEY=ALPHA option maintains the Type I level and derives the Type II error probability, and the BOUNDARYKEY=BETA option maintains the Type II level and derives the Type I error probability. The BOUNDARYKEY=BOTH option maintains both and levels simultaneously by deriving a new maximum information. The default is BOUNDARYKEY=ALPHA.

BOUNDARYSCALE=MLE  SCORE  STDZ  PVALUE
BSCALE=MLE  SCORE  STDZ  PVALUE

specifies the boundary scale to be displayed in the output boundary table and plot. The BOUNDARYSCALE=MLE, BOUNDARYSCALE=SCORE, BOUNDARYSCALE=STDZ, and
BOUNDARYSCALE=PVALUE options correspond to the boundary with the maximum likelihood estimator scale, score statistic scale,
standardized normal Z scale, and pvalue scale, respectively. The default is BOUNDARYSCALE=STDZ.
With the BOUNDARYSCALE=MLE or BOUNDARYSCALE=SCORE option, either the MAXINFO= option must be specified or the _Info_
variable must be in the BOUNDARY= data set to provide the necessary information level at each stage to derive the boundary
values. Usually, these values are obtained from analysis output in SAS procedures.
Note that for a twosided design, the pvalue scale displays the onesided fixedsample pvalue under the null hypothesis with a lower alternative hypothesis.

CIALPHA= <( <LOWER=> <UPPER=> )>

specifies the significance levels for the confidence interval, where , , and . The default is CIALPHA= 0.05.
For a lower confidence interval (CITYPE=LOWER), the CIALPHA= option produces a lower confidence interval. For an upper confidence interval (CITYPE=UPPER), the CIALPHA= option produces a upper confidence interval. The LOWER= and UPPER= suboptions are applicable only for a twosided confidence interval (CITYPE=TWOSIDED).
The LOWER= suboption specifies the lower significance level and the upper significance level . The UPPER= suboption specifies the upper significance level and the lower significance level . If both LOWER= and UPPER= suboptions are not specified, . The significance levels and are then used for the lower confidence limit and upper confidence limit, respectively.

CITYPE=LOWER  UPPER  TWOSIDED

specifies the type of confidence interval. The CITYPE=LOWER, CITYPE=UPPER, and CITYPE=TWOSIDED options correspond to the lower confidence interval, upper confidence
interval, and twosided confidence interval, respectively. The default is CITYPE=LOWER for the design with an upper alternative,
CITYPE=UPPER for the design with a lower alternative, and CITYPE=TWOSIDED for the design with a twosided alternative.

DATA <( TESTVAR=variable  INFOVAR=variable )>=SASdataset

names the SAS data set that contains the test statistic and its associated information level for the stage. The SASdataset includes the stage variable _Stage_
. You can also specify the following options within parentheses after the DATA keyword:

TESTVAR=variable

identifies the test variable in the SASdataset. If you specify this option, the data set also includes the scale variable _Scale_
for the test statistic. Usually, these test variable values are obtained from analysis output in SAS procedures.

INFOVAR=variable

identifies the information variable in the SASdataset to either identify or derive the information levels. If you specify this option, the following information variables apply:

_Info_
uses _Info_
for the information levels.

Events
uses Events
for the numbers of events to derive the information levels.

NObs
uses NObs
for the numbers of observations to derive the information levels.

None
uses the information levels in the BOUNDARY= data set.
If you do not specify the INFOVAR= option, PROC SEQTEST uses _Info_
(if it exists) in the SASdataset for the information levels. Otherwise, the procedure uses NObs
to derive information levels from the numbers of observation or uses Events
to derive information levels from the numbers of events. If these variables are not in the SASdataset, the information levels in the BOUNDARY= data set are used. See Example 90.4 for an illustration of the INFOVAR=NObs option.

ERRSPENDADJ=method
ERRSPENDADJ(boundary)=method
BOUNDARYADJ=method
BOUNDARYADJ(boundary)=method

specifies methods to compute the error spending values at the current and future interim stages for the boundaries. This option is applicable only if the observed information level at the current
stage does not match the value provided in the BOUNDARY= data set. These error spending values are then used to derive the
updated boundary values. The default is ERRSPENDADJ=ERRLINE. Note that the information levels at future interim stages are
determined by the INFOADJ= option.
The following options specify available error spending methods for boundary adjustment:

NONE

specifies that the cumulative error spending at each interim stage not be changed, even if the corresponding information level
has been changed.

ERRLINE

specifies the linear interpolation method for the adjustment.

ERRFUNCGAMMA < ( GAMMA= ) >

specifies the gamma function method for the adjustment. The GAMMA= suboption specifies the parameter in the function, where . The default is GAMMA=–2.

ERRFUNCOBF

specifies the approximate O’BrienFleming cumulative error spending function for the adjustment.

ERRFUNCPOC

specifies the approximate Pocock cumulative error spending function for the adjustment.

ERRFUNCPOW < ( RHO= ) >

specifies the power function method for the adjustment. The RHO= suboption specifies the power parameter in the function, where . The default is RHO=2.
See the section Boundary Adjustments for Information Levels for a detailed description of the available error spending methods for boundary adjustment in the SEQTEST procedure.
If an error spending method for boundary adjustments is used for all boundaries in a group sequential test, you can use the
ERRSPENDADJ=method option to specify the method. Otherwise, you can use the following ERRSPENDADJ(boundary)=method options to specify different methods for the boundaries.

ERRSPENDADJ(ALPHA)=method
ERRSPENDADJ(REJECT)=method
BOUNDARYADJ(ALPHA)=method
BOUNDARYADJ(REJECT)=method

specifies the adjustment method for the (rejection) boundary of a onesided design or the lower and upper boundaries of a twosided design.

ERRSPENDADJ(LOWERALPHA)=method
ERRSPENDADJ(LOWERREJECT)=method
BOUNDARYADJ(LOWERALPHA)=method
BOUNDARYADJ(LOWERREJECT)=method

specifies the adjustment method for the lower boundary of a twosided design.

ERRSPENDADJ(UPPERALPHA)=method
ERRSPENDADJ(UPPERREJECT)=method
BOUNDARYADJ(UPPERALPHA)=method
BOUNDARYADJ(UPPERREJECT)=method

specifies the adjustment method for the upper boundary of a twosided design.

ERRSPENDADJ(BETA)=method
ERRSPENDADJ(ACCEPT)=method
BOUNDARYADJ(BETA)=method
BOUNDARYADJ(ACCEPT)=method

specifies the adjustment method for the (acceptance) boundary of a onesided design or the lower and upper boundaries of a twosided design.

ERRSPENDADJ(LOWERBETA)=method
ERRSPENDADJ(LOWERACCEPT)=method
BOUNDARYADJ(LOWERBETA)=method
BOUNDARYADJ(LOWERACCEPT)=method

specifies the adjustment method for the lower boundary of a twosided design.

ERRSPENDADJ(UPPERBETA)=method
ERRSPENDADJ(UPPERACCEPT)=method
BOUNDARYADJ(UPPERBETA)=method
BOUNDARYADJ(UPPERACCEPT)=method

specifies the adjustment method for the upper boundary of a twosided design.

ERRSPENDMIN=numbers
ERRSPENDMIN(boundary)=numbers

specifies the minimum error spending values at the current observed and future interim stages for the boundaries specified in the BOUNDARYKEY= option. The default
is ERRSPENDMIN=0.
If a set of numbers is used for each boundary in the design, you can use the ERRSPENDMIN=numbers option. Otherwise, you can use the following ERRSPENDMIN(boundary)=numbers options to specify different sets of minimum error spending values for the boundaries. For a boundary, the error spending
value at stage 1 is identical to its nominal pvalue.

ERRSPENDMIN(ALPHA)=numbers
ERRSPENDMIN(REJECT)=numbers

specifies the minimum error spending values for the boundary of a onesided design or the lower and upper boundaries of a twosided design.

ERRSPENDMIN(LOWERALPHA)=numbers
ERRSPENDMIN(LOWERREJECT)=numbers

specifies the minimum error spending values for the lower boundary of a twosided design.

ERRSPENDMIN(UPPERALPHA)=numbers
ERRSPENDMIN(UPPERREJECT)=numbers

specifies the minimum error spending values for the upper boundary of a twosided design.

ERRSPENDMIN(BETA)=numbers
ERRSPENDMIN(ACCEPT)=numbers

specifies the minimum error spending values for the boundary of a onesided design or the lower and upper boundaries of a twosided design.

ERRSPENDMIN(LOWERBETA)=numbers
ERRSPENDMIN(LOWERACCEPT)=numbers

specifies the minimum error spending values for the lower boundary of a twosided design.

ERRSPENDMIN(UPPERBETA)=numbers
ERRSPENDMIN(UPPERACCEPT)=numbers

specifies the minimum error spending values for the upper boundary of a twosided design.

INFOADJ=NONE  PROP

specifies whether information levels at future interim stages are to be adjusted. If you specify INFOADJ=NONE, no adjustment is made, and the information levels are preserved at the levels provided in the
BOUNDARY= data set. If you specify INFOADJ=PROP (which is the default), the information levels are adjusted proportionally
from the levels provided in the BOUNDARY= data set. The section Information Level Adjustments at Future Stages describes how the adjustments are computed.
Note that if you specify BOUNDARYKEY=BOTH, the INFOADJ=NONE option is not applicable, and the INFOADJ=PROP option is used
to adjusted the information levels at future stages proportionally from the levels provided in the BOUNDARY= data set to maintain
both and levels.

NSTAGES=number

specifies the number of stages for the clinical trial. The default is the number derived from the BOUNDARY= data set.
The specified NSTAGES= number might or might not be the same as the number derived in the BOUNDARY= data set. You can use
the NSTAGES= option to set the next stage as the final stage to compute the conditional power, as described in the section
Conditional Power Approach.

ORDER=LR  MLE  STAGEWISE

specifies the ordering of the sample space , where k is the stage number and z is the observed standardized Z statistic. The ordering is used to derive the pvalues for the observed statistic and to create unbiased median estimate and confidence limits from the statistic. The ORDER=LR option specifies
the LR ordering that compares the distances between observed standardized Z statistics and their corresponding hypothetical values, the ORDER=MLE option specifies the MLE ordering that compares values
in the MLE scale, and the ORDER=STAGEWISE specifies the stagewise ordering that uses counterclockwise ordering around the
continuation region. The default is ORDER=STAGEWISE. See the section Available Sample Space Orderings in a Sequential Test for a detailed description of these sample space orderings.

PARMS <( TESTVAR=variable  INFOVAR=variable )>=SASdataset

names the SAS data set that contains the parameter estimate and its associated standard error for the stage. The SASdataset includes the stage variable _Stage_
. You can also specify the following options within parentheses after the PARMS keyword:

TESTVAR=variable

identifies the test variable in the SASdataset. If you specify this option, the SASdataset also includes the Parameter
, Effect
, Variable
, or Parm
variable that contains the variable. In addition, the SASdataset also includes the test statistic Estimate
, the standard error of the estimate StdErr
, and the test statistic scale variable _Scale_
. Usually, these test variable values are obtained from analysis output in SAS procedures.

INFOVAR=variable

identifies the information variable in the SASdataset to either identify or derive the information levels. If you specify this option, the following information variables apply:

Events
uses Events
for the numbers of events to derive the information levels.

NObs
uses NObs
for the numbers of observations to derive the information levels.

None
uses the information levels in the BOUNDARY= data set.

StdErr
uses StdErr
to derive the information levels.
If you do not specify the INFOVAR= option, PROC SEQTEST uses StdErr
(if it exists) in the SASdataset to derive the information levels. Otherwise, the procedure uses NObs
to derive information levels from the numbers of observation or uses Events
to derive information levels from the numbers of events. If these variables are not in the SASdataset, the information levels in the BOUNDARY= data set are used.