
ADAPTIVEHOCHBERG
AHOC

requests adjusted pvalues by using the Hochberg and Benjamini (1990) adaptive stepup Bonferroni method. See the section Adaptive Adjustments for more details.

ADAPTIVEHOLM
AHOLM

requests adjusted pvalues by using the Hochberg and Benjamini (1990) adaptive stepdown Bonferroni method. See the section Adaptive Adjustments for more details.

ADAPTIVEFDR<(UNRESTRICT)>
AFDR<(UNRESTRICT)>

requests adjusted pvalues by using the Benjamini and Hochberg (2000) adaptive linear stepup method (AFDR). The UNRESTRICT option estimates the AFDR as defined in Benjamini and Hochberg (2000), which allows the adjustment to reduce the raw pvalue. By default, the AFDR is constrained to be greater than or equal to the raw pvalue. See the section Adaptive False Discovery Rate for more details.

BONFERRONI
BON

specifies that the Bonferroni adjustments (number of tests pvalue) be computed for each test. These adjustments can be extremely conservative and should be viewed with caution. When
exact tests are specified via the PERMUTATION= option in the TEST statement, the actual permutation distributions are used, resulting in a much less conservative version of this procedure
(Westfall and Wolfinger, 1997). See the section Bonferroni for more details.

BOOTSTRAP
BOOT

specifies that the pvalues be adjusted by using the bootstrap method to resample vectors (Westfall and Young, 1993). Resampling is performed with replacement and independently within levels of the STRATA variable. Continuous variables are meancentered by default prior to resampling; specify the NOCENTER option to change this. See the section Bootstrap for more details. The BOOTSTRAP option is not allowed with the Peto test.
If the PERMUTATION= suboption is used with the CA test in the TEST statement, the exact permutation distribution is recomputed for each bootstrap sample.
Caution: This can be very timeconsuming. It is preferable to use permutation resampling when permutation base tests are used.

CENTER

requests that continuous variables be meancentered prior to resampling. The default action is to meancenter for bootstrap
resampling and not to meancenter for permutation resampling.

DATA=SASdataset

names the input SAS data set to be used by PROC MULTTEST. The default is to use the most recently created data set. The DATA=
and INPVALUES= options cannot both be specified.

DEPENDENTFDR
DFDR

requests adjusted pvalues by using the method of Benjamini and Yekateuli (2001). See the section Dependent False Discovery Rate for more details.

EPSILON=number

specifies the amount by which two pvalues must differ to be declared unequal. The value number must be between 0 and 1; the default value is 1000 times the machine epsilon, which is approximately 1E–12. For SAS 9.1 and
earlier releases the default value was 1E–8. See Westfall and Young (1993, pp. 165–166) for more information.

FDR
LSU

requests adjusted pvalues by using the linear stepup method of Benjamini and Hochberg (1995). These pvalues do not control the familywise error rate, but they do control the false discovery rate in some cases. See the section
False Discovery Rate Controlling Adjustments for more details.

FDRBOOT<(
)>

A bootstrapresampling false discovery rate controlling method due to Yekateuli and Benjamini (1999). This method uses the same resampling algorithm as the BOOTSTRAP option. Every resample is saved in order to compute a quantile of the resampled pvalues; therefore, this method can use a lot of memory. The parameter designates that a 100 quantile is used in the computations for determining the adjustments; by default, . See the section False Discovery Rate Resampling Adjustments for details.

FDRPERM<(
)>

A permutationresampling false discovery rate controlling method due to Yekateuli and Benjamini (1999). This method uses the same resampling algorithm as the PERMUTATION option. Every resample is saved in order to compute a quantile of the resampled pvalues; therefore, this method can use a lot of memory. The parameter designates that a 100 quantile is used in the computations for determining the adjustments; by default, . See the section False Discovery Rate Resampling Adjustments for details.

FISHER_C
FIC

requests adjusted pvalues by using Fisher’s combination method. See the section Fisher Combination for more details.

HOCHBERG
HOC

requests adjusted pvalues by using the stepup Bonferroni method due to Hochberg (1988). See the section Hochberg for more details.

HOMMEL
HOM

requests adjusted pvalues by using the method of Hommel (1988). See the section Hommel for more details.

HOLM

is an alias for the STEPBON adjustment.

INPVALUES<(pvaluename)>=SASdataset

names an input SAS data set that includes a variable containing raw pvalues. The MULTTEST procedure adjusts the collection of raw pvalues for multiplicity. Resamplingbased adjustments are not permitted with this type of data input. The CLASS, CONTRAST, FREQ, STRATA, and TEST statements are ignored when an INPVALUES= data set is specified. The INPVALUES= and DATA= options cannot both be specified. The pvaluename enables you to specify the name of the pvalue column from your data set. By default, pvaluename=’raw_p’. The INPVALUES= data set can contain variables in addition to the raw pvalues variable; see Example 61.5 for an example.

LIPTAK

is an alias for the STOUFFER adjustment.

NOCENTER

requests that continuous variables not be meancentered prior to resampling. The default action is to meancenter for bootstrap
resampling and not to meancenter for permutation resampling.

NOPRINT

suppresses the normal display of results. Note that this option temporarily disables the Output Delivery System (ODS); see
Chapter 20: Using the Output Delivery System, for more information.

NOPVALUE

suppresses the display of the “pValues” table of raw and adjusted pvalues. This option is most useful when you are adjusting many tests and need to create only an OUT= data set or display graphics.

NOTABLES

suppresses display of the “Discrete Variable Tabulations” and “Continuous Variable Tabulations” tables.

NOZEROS

suppresses display of tables having zero occurrences for all CLASS levels.

NSAMPLE=number
N=number

specifies the number of resamples for use with the resampling methods. The value number must be a positive integer; by default, 20,000 resamples are used. Large values of number (20,000 or more) are usually recommended for accuracy, but long execution times can result, particularly with large data
sets.

NTRUENULL=keyword  value
M0=keyword  value

Controls the method used to estimate the number of true NULL hypotheses () for the adaptive methods. This option is ignored unless one of the adaptive methods is specified. By default, PROC MULTTEST
uses the DECREASESLOPE method for the ADAPTIVEHOLM and ADAPTIVEHOCHBERG adjustments, and the LOWESTSLOPE method for ADAPTIVEFDR adjustment. For the PFDR adjustment, the SPLINE method is attempted first. If the estimate is nonpositive or if the slope of the spline at the last is greater than 0.1 times the range of the fitted spline values, then the BOOTSTRAP method is used.
You can specify a positive integer as the value, or you can specify one of the keywords in the following list. Alternatively, you can specify the proportion of true NULL hypotheses by using the PTRUENULL= option. Suppose you have m tests with ordered pvalues , and define .

BOOTSTRAP<(bootstrapoptions)>

uses the bootstrap method of Storey and Tibshirani (2003). Compute the proportion of true null hypotheses for , where is the number of pvalues less than or equal to , and f = 1 for the finitesample case; otherwise f = 0. For each , bootstrap on the pvalues to form B bootstrap versions , and choose the that yields the minimum . The available bootstrapoptions are as follows:

FINITE

the finitesample case of the PFDR option.

NBOOT=B

bootstrap resamples of the raw pvalues for the computations. NBOOT=10,000 by default; B must be a positive integer.

NLAMBDA=n

“optimal” is the value in that minimizes the MSE. NLAMBD=20 by default; n must be an integer greater than 1.

DECREASESLOPE

Schweder and Spjøtvoll (1982) as modified by Hochberg and Benjamini (1990). Let be the slope of the least squares line fit to and through the origin, for . Find the first such that . Then .

KSTEST<(
)>

uses the KolmogorovSmirnov uniformity test method of Turkheimer, Smith, and Schmidt (2001). Let , and the KolmogorovSmirnov statistic . If D is greater than the uppertail probability (Press et al., 1992), then ; otherwise, let . Repeat until . Next compute the slope b of the weighted least squares regression line on the k smallest by using weights . Then .

LEASTSQUARES

uses a linear least squares method to search for the correct cutpoint. For each compute the SSE of the least squares line through the origin fitting , let be the slope of this line, and add the SSE of the unconstrained least squares line through the rest of the qs. For i = 0 compute the SSE for the unconstrained line. The argument i that minimizes the SSE is the cutpoint: if i = 0 then ; if then ; otherwise .

LOWESTSLOPE

uses the lowest slope method of Benjamini and Hochberg (2000). Find the first such that decreases. Then .

MEANDIFF

uses the mean of differences method of Hsueh, Chen, and Kodell (2003). Let and estimate . Start from and proceed downward until the first time occurs.

SPLINE<(splineoptions)>

uses the cubic spline method of Storey and Tibshirani (2003). For each compute . Let be the natural cubic spline with 3 degrees of freedom of versus . Estimate by taking the spline value at the last : , so that . The available splineoptions are as follows:

DF=df

sets the degrees of freedom of the spline, where df is a nonnegative integer. The default is DF=3.

DFCONV=number

specifies the absolute change in spline degrees of freedom value for concluding convergence. If (or if the SPCONV= criterion is satisfied), then convergence is declared. number must be between 0 and 1; by default, number is 1000 times the square root of machine epsilon, which is about 1E–5.

FINITE

computations for the finitesample case of the PFDR option.

MAXITER=n

specifies the maximum number of goldensearch iterations used to find a spline with DF= degrees of freedom. By default, MAXITER=100; number must be a nonnegative integer.

NLAMBDA=n

for for the spline fit. By default, NLAMBDA=20; number must be an integer greater than 1.

SPCONV=number

specifies the absolute change in smoothing parameter value for concluding convergence of the spline. If (or if the DFCONV= criterion is satisfied), then convergence is declared. By default, number equals the square root of the machine epsilon, which is about 1E–8.
In all cases is constrained to lie between 0 and m; if the computed , then the adaptive adjustments do not produce results. If you specify , then it is reduced to m. Values of are displayed in the “Estimated Number of True Null Hypotheses” table.

ORDER=DATA  FORMATTED  FREQ  INTERNAL

specifies the sort order for the levels of the classification variables (which are specified in the CLASS statement). This option applies to the levels for all classification variables, except when you use the (default) ORDER=FORMATTED
option with numeric classification variables that have no explicit format. With this option, the levels of such variables
are ordered by their internal value.
The ORDER= option can take the following values:
Value of ORDER=

Levels Sorted By

DATA

Order of appearance in the input data set

FORMATTED

External formatted value, except for numeric variables with no explicit format, which are sorted by their unformatted (internal)
value

FREQ

Descending frequency count; levels with the most observations come first in the order

INTERNAL

Unformatted value

By default, ORDER=FORMATTED. For ORDER=FORMATTED and ORDER=INTERNAL, the sort order is machinedependent. For more information
about sort order, see the chapter on the SORT procedure in the
Base SAS Procedures Guide and the discussion of BYgroup processing in
SAS Language Reference: Concepts.

OUT=SASdataset

names the output SAS data set containing variable names, contrast names, intermediate calculations, and all associated pvalues. See OUT= Data Set for more information.

OUTPERM=SASdataset

names the output SAS data set containing entire permutation distributions (uppertail probabilities) for all tests when the
PERMUTATION= option is specified. See OUTPERM= Data Set for more information.
Caution: This data set can be very large.

OUTSAMP=SASdataset

names the output SAS data set containing information from the resampled data sets when resampling is performed. See OUTSAMP= Data Set for more information.
Caution: This data set can be very large.

PDATA=SASdataset

is an alias for the INPVALUES= option.

PERMUTATION
PERM

computes adjusted pvalues in identical fashion as the BOOTSTRAP option, with the exception that PROC MULTTEST resamples without replacement rather than with replacement. Resampling is performed
independently within levels of the STRATA variable. Continuous variables are not meancentered prior to resampling; specify the CENTER to change this. See the section Bootstrap for more details. The PERMUTATION option is not allowed with the Peto test.

PFDR<(options )>

computes the “qvalues” of Storey (2002) and Storey, Taylor, and Siegmund (2004). PROC MULTTEST treats these “qvalues” as adjusted pvalues. The computations depend on selecting a parameter and an estimation method for the false discovery rate; see the section Positive False Discovery Rate for computational details. The available options for choosing the method are as follows:

FINITE

estimates the false discovery rate with or for the finitesample case with independent null pvalues.

POSITIVE

estimates the false discovery rate with instead of the default .

UNRESTRICT

estimates the false discovery rate as defined in Storey (2002), which allows the adjustment to reduce the raw pvalue. By default, the PFDR is constrained to be greater than or equal to the raw pvalue.
The available options for controlling the search are the bootstrapoptions, the splineoptions, and the following options:

LAMBDA=number

specifies a and does not perform the bootstrap or spline searches for an “optimal” .

MAXLAMBDA=number

stops the NLAMBDA= search sequence for the bootstrap and spline searches when this number is reached. The number must be in . This option is ignored if the LAMBDA= option is specified.

PLOTS<(globalplotoptions)>=plotrequest<(options)>
PLOTS<(globalplotoptions)>=(plotrequest<(options)><
plotrequest<(options)>> )

controls the plots produced through ODS Graphics. If you specify only one plotrequest, you can omit the parentheses. For example, the following statements are valid specifications of the PLOTS= option:
plots = all
plots = (rawprob adjusted)
plots(sigonly) = (rawprob adjusted(unpack))
ODS Graphics must be enabled before plots can be requested. For example:
ods graphics on;
proc multtest plots=adjusted inpvalues=a pfdr;
run;
ods graphics off;
For more information about enabling and disabling ODS Graphics, see the section Enabling and Disabling ODS Graphics in Chapter 21: Statistical Graphics Using ODS.
By default, no graphs are created; you must specify the PLOTS= option to make graphs. You need at least two tests to produce
a graph. If you are not using an INPVALUES= data set, then each test is given a name constructed as “variablename contrastlabel”. If you specify a MEAN test in the TEST statement, the ttest names are prefixed with “Mean:”. See Example 61.6 for examples of the ODS graphical displays.
The following globalplotoptions are available:

UNPACKPANELS  UNPACK

suppresses paneling. By default, the plots produced with the ADJUSTED and RAWPROB options are grouped in a single display, called a panel. Specify UNPACK to display each plot separately.

SIGONLY<=number>

displays only those tests with adjusted pvalues number, where number . By default, number = 0.05.
The following plotrequests are available:

ADJUSTED<(UNPACK)>

displays a 22 panel of adjusted pvalue plots similar to those Storey and Tibshirani (2003) developed for use with the PFDR pvalue adjustment method. The plots of the adjusted pvalues by the raw pvalues and the adjusted pvalues by their rank show the effect of the adjustments. The plot of the proportion of adjusted pvalues each adjusted pvalue and the plot of the expected number of false positives (the proportion significant multiplied by the adjusted pvalue) versus the proportion significant show the effect of choosing different significance levels. The UNPACK option unpanels
the display.

ALL

produces all appropriate plots. You can specify other options with ALL; for example, to display all plots and unpack the RAWPROB plots you can specify plots=(all rawprob(unpack))
.

LAMBDA

displays plots of the MSE and the estimated number of true nulls against the parameter when the NTRUENULL=SPLINE or NTRUENULL=BOOTSTRAP option is in effect.

MANHATTAN<(options)>

displays the Manhattan plot (a plot of –log of the adjusted pvalues versus the tests). You can specify the following options:

GROUP=variable

specifies a variable to group the adjusted pvalues in the display.

LABEL <=OBS>

labels the observations that have adjusted pvalues that are less than the value specified in the VREF= option. By default, labels are created as follows: if an INPVALUES= data set and an ID statement are specified, then the observations are labeled with the ID values; if a DATA= data set is specified, then the
observations are labeled with their constructed test name; otherwise, the observation or test number is displayed.

NOTESTNAME

displays the number of the test instead of the test name on the Xaxis, which is useful when you have many tests.

UNPACK

suppresses paneling. By default, Manhattan plots are created for each requested pvalue adjustment, and the results are grouped in a single display, called a panel. Specify UNPACK to display each plot separately.

VREF=number  NONE

displays a reference line at –log(number). The number must be between 0 and 1. By default, a reference line at –log(0.05) is displayed; it can be suppressed by specifying VREF=0 or VREF=NONE. If the LABEL option is also specified, then observations
above this line are labeled with their ID variables, their observation number, their test name, or their test number.

NONE

suppresses all plots.

PBYTEST<(options)>

displays the adjusted pvalues for each test. The available options are as follows:

NOTESTNAME

displays the number of the test instead of the test name on the axis, which is useful when you have many tests.

VREF=

numberlist displays reference lines at the pvalues specified in the numberlist. The values in the numberlist must be between 0 and 1; otherwise they are ignored. You can specify a single value or a list of values; for example, vref=0.1 0 to 0.05 by 0.01
displays reference lines at each of the values {0.01, 0.02, 0.03, 0.04, 0.05, and 0.1}.

RAWPROB<(UNPACK)>

displays a uniform probability plot of 1 minus the raw pvalues (Schweder and Spjøtvoll, 1982) along with a histogram. If is the number of true null hypotheses among the m tests, the points on the left side of the plot should be approximately linear with slope . This graphic is displayed when an adaptive pvalue adjustment method is requested in order to see if the NTRUENULL= estimate is appropriate. The UNPACK option unpanels the display.

PTRUENULL=keyword  value
PI0=keyword  value

is alias for the NTRUENULL= option, except that you can specify the proportion of true null hypotheses as a value between 0 and 1, instead of specifying the number of true null hypotheses. The available keywords are also the NTRUENULL= options.

RANUNI

requests the random number generator used in releases prior to SAS 9.2. Beginning with SAS 9.2, the random number generator
is the Mersenne Twister, which has better performance when bootstrapping. Changes in the bootstrap or permutationadjusted pvalues from prior releases are due to unimportant sampling differences.

SEED=number
S=number

specifies the initial seed for the random number generator used for resampling. The value for number must be an integer. If you do not specify a seed, or if you specify a value less than or equal to zero, then PROC MULTTEST
uses the time of day from the computer’s clock to generate an initial seed. For more details about seed values, see
SAS Language Reference: Concepts.

SIDAK
SID

computes the Šidák adjustment for each test. These adjustments take the form
where p is the raw pvalue and m is the number of tests. These are slightly less conservative than the Bonferroni adjustments, but they still should be viewed
with caution. When exact tests are specified via the PERMUTATION= option in the TEST statement, the actual permutation distributions are used, resulting in a much less conservative version of this procedure
(Westfall and Wolfinger, 1997). See the section Šidák for more details.

STEPBON
HOLM

requests adjusted pvalues by using the stepdown Bonferroni method of Holm (1979). See the section StepDown Methods for more details.

STEPBOOT

requests that adjusted pvalues be computed by using bootstrap resampling as described under the BOOTSTRAP option, but in stepdown fashion. See the section StepDown Methods for more details.

STEPPERM

requests that adjusted pvalues be computed by using permutation resampling as described under the PERMUTATION option, but in stepdown fashion. See the section StepDown Methods for more details.

STEPSID

requests adjusted pvalues by using the Šidák method as described in the SIDAK option, but in stepdown fashion. See the section StepDown Methods for more details.

STOUFFER
LIPTAK

requests adjusted pvalues by using the StoufferLiptak combination method. See the section StoufferLiptak Combination for more details.