Subsections:

The OUT= data set contains contrast names (`_test_`

), variable names (`_var_`

), the contrast label (`_contrast_`

), raw p-values (`raw_p`

or the value specified in the INPVALUES=
option), and all requested adjusted p-values (`bon_p`

, `sid_p`

, `boot_p`

, `perm_p`

, `stpbon_p`

, `stpsid_p`

, `stpbootp`

, `stppermp`

, `hom_p`

, `hoc_p`

, `fic_p`

, `stouffer_p`

, `aholm_p`

, `ahoc_p`

, `fdr_p`

, `dfdr_p`

, `fdrbootp`

, `ufdbootp`

, `fdrpermp`

, `ufdpermp`

, `afdr_p`

, or `pfdr_p`

).

If a resampling-based adjusted p-value is requested, then the simulation standard error is included as either `sim_se`

, `stpsimse`

, `fdrsimse`

, or `ufdsimse`

, depending on whether single-step, step-down, or FDR adjustments are requested. The simulation standard errors are used to
bound the true resampling-based adjusted p-value. For example, if the resampling-based estimate is 0.0312 and the simulation standard error is 0.00123, then a 95% confidence
interval for the true adjusted p-value is , or 0.0288 to 0.0336.

Intermediate statistics used to calculate the p-values are also written to the OUT= data set. The statistics are separated by the `_strat_`

level. When `_strat_`

is reported as missing, the statistics refer to the pooled analysis over all `_strat_`

levels. The p-values are provided only for the pooled analyses and are therefore reported as missing for the strata-specific statistics.

For the Peto
test, an additional variable, `_tstrat_`

, is included to indicate whether the stratum is an incidental occurrence stratum (`_tstrat_`

=0) or a fatal occurrence stratum (`_tstrat_`

=1).

The statistic `_value_`

is the per-strata contribution to the numerator of the overall test statistic. In the case of the MEAN
test, this is the contrast function of the sample means multiplied by the total number of observations within the stratum.
For the FT
test, `_value_`

is the contrast function of the double-arcsine transformed proportions, again multiplied by the total number of observations
within the stratum. For the CA
and Peto
tests, `_value_`

is the observed value of the trend statistic within that stratum.

When either PETO
or CA
is requested, the variable `_exp_`

is included; this variable contains the expected value of the trend statistic for the given stratum.

The statistic `_se_`

is the square root of the variance of the per-strata `_value_`

statistic for any of the tests.

For MEAN
tests, the variable `_nval_`

is included. When reported with an individual stratum level (that is, when the `_strat_`

value is nonmissing), the value `_nval_`

refers to the within-stratum sample size. For the combined analysis (that is, the value of the `_strat_`

is missing), the value `_nval_`

contains degrees of freedom of the t distribution used to compute the unadjusted p-value.

When the FISHER
test is requested, the OUT= data set contains the variables `_xval_`

, `_mval_`

, `_yval_`

, and `_nval_`

, which define observations and sample sizes in the two groups defined by the CONTRAST
statement.

For example, the OUT= data set from the drug example in the section Getting Started: MULTTEST Procedure is displayed in Figure 79.5.

Figure 79.5: Output Data for the MULTTEST Procedure

Obs | _test_ | _var_ | _contrast_ | _value_ | _exp_ | _se_ | raw_p | boot_p | sim_se |
---|---|---|---|---|---|---|---|---|---|

1 | CA | SideEff1 | Trend | 8 | 5 | 1.54303 | 0.05187 | 0.33880 | .003346749 |

2 | CA | SideEff2 | Trend | 7 | 5 | 1.54303 | 0.19492 | 0.84030 | .002590327 |

3 | CA | SideEff3 | Trend | 10 | 7 | 1.63299 | 0.06619 | 0.51895 | .003532994 |

4 | CA | SideEff4 | Trend | 10 | 6 | 1.60357 | 0.01262 | 0.08840 | .002007305 |

5 | CA | SideEff5 | Trend | 7 | 4 | 1.44749 | 0.03821 | 0.24080 | .003023370 |

6 | CA | SideEff6 | Trend | 9 | 6 | 1.60357 | 0.06137 | 0.43825 | .003508468 |

7 | CA | SideEff7 | Trend | 9 | 5 | 1.54303 | 0.00953 | 0.05135 | .001560660 |

8 | CA | SideEff8 | Trend | 8 | 5 | 1.54303 | 0.05187 | 0.33880 | .003346749 |

9 | CA | SideEff9 | Trend | 7 | 5 | 1.54303 | 0.19492 | 0.84030 | .002590327 |

10 | CA | SideEff10 | Trend | 8 | 6 | 1.60357 | 0.21232 | 0.90300 | .002092737 |

The OUTPERM= data set contains contrast names (`_contrast_`

), variable names (`_var_`

), and the associated permutation distributions (`_value_`

and `upper_p`

). PROC MULTTEST computes the permutation distributions when you use the PERMUTATION=
option with the CA
or Peto test. The `_value_`

variable represents the support of the distributions, and `upper_p`

represents their cumulative upper-tail probabilities. The size of this data set depends on the number of variables and the
support of their permutation distributions.

For information about how this distribution is computed, see the section Exact Permutation Test. For an illustration, see Example 79.1.

The OUTSAMP= data set contains the data sets used in the resampling analysis, if such an analysis is requested. The variable
`_sample_`

indicates the number of the resampled data set. This variable ranges from 1 to the value of the NSAMPLE=
option. For each value of the `_sample_`

variable, an entire resampled data set is included, with `_stratum_`

, `_class_`

, and all other variables in the original data set. The values of the original variables are mean-centered for the mean test,
if requested. The variable `_obs_`

indicates the observation’s position in the original data set.

Each new data set is randomly drawn from the original data set, either with (bootstrap) or without (permutation) replacement. The size of this data set is, thus, the number of observations in the original data set times the number of samples.