This example uses the SAS data set `Arthritis`

created in Example 83.1. The data set contains the variable `Treatment`

, which denotes the treatment received by a patient, and the variable `Response`

, which contains the response status of the patient. The variable `Freq`

contains the frequency of the observation, which is the number of patients with the `Treatment`

and `Response`

combination.

The following statements request empirical distribution function (EDF) statistics, which test whether the distribution of
a variable is the same across different groups. The EDF option requests the EDF analysis. The variable `Treatment`

is the CLASS variable, and the variable `Response`

specified in the VAR statement is the analysis variable. The FREQ statement names `Freq`

as the frequency variable.

The PLOTS= option requests an EDF plot for `Response`

classified by `Treatment`

. ODS Graphics must be enabled before producing plots.

ods graphics on; proc npar1way edf plots=edfplot data=Arthritis; class Treatment; var Response; freq Freq; run; ods graphics off;

Output 83.2.1 shows EDF statistics that compare the two levels of `Treatment`

, Active and Placebo. The asymptotic p-value for the Kolmogorov-Smirnov test is 0.0164. This supports rejection of the null hypothesis that the distributions are
the same for the two samples.

Output 83.2.2 shows the EDF plot for `Response`

classified by `Treatment`

.

Output 83.2.1: Empirical Distribution Function Statistics

The NPAR1WAY Procedure

Kolmogorov-Smirnov Test for Variable Response Classified by Variable Treatment |
|||
---|---|---|---|

Treatment | N | EDF at Maximum |
Deviation from Mean at Maximum |

Active | 27 | 0.407407 | -1.141653 |

Placebo | 32 | 0.812500 | 1.048675 |

Total | 59 | 0.627119 | |

Maximum Deviation Occurred at Observation 3 | |||

Value of Response at Maximum = 3.0 |

Output 83.2.2: Empirical Distribution Function Plot