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It is often necessary to assess the agreement on multi-category ratings by multiple raters in various studies in many fields. Kappa is one of the most popular indicators of interrater agreement for categorical data. SAS® procedure PROC FREQ can provide kappa statistic for two raters. A SAS® macro MAGREE computes kappa for multiple raters with multi-categorical ratings. Both have limited applications. The author wrote a macro which implements the Fleiss (1981) methodology measuring the agreement when both the number of raters and the number of categories of the rating are greater than two. This macro can handle missing data as well as data that are not square, which are the two major limits of PROC FREQ and macro MAGREE. It is very easy to use. Anyone who has minimum SAS® programming skill and basic understanding of the kappa statistic can use the macro with ease. All the users need to do is to start the macro and input some parameters. SAS® products involved are Base SAS® and SAS® MACRO. This macro is tested on Windows SAS®8 and above.

The purpose of this paper is to introduce new users to the DATASETS procedure. Advanced topics such as working with indexes, generational datasets, and repairing damaged datasets are not included in this paper.

Time-to-event data have long been important in many applied fields. Many models and analysis methods have been developed for this type of data, in which each sample unit experiences at most a single end-of-life event. In contrast, many applications involve repeated events, where a subject or sample unit may experience any number of events over a lifetime. There is a growing interest in the analysis of recurrent events data, also called repeated events data and recurrence data. This type of data arises in many fields. For example, the repair history of manufactured items can be modeled as recurrent events. In medical studies, the times of recurrent disease episodes in patients can also be modeled as recurrent events. This paper describes methods for the analysis of recurrent events data. Nonparametric methods involving extensive use of graphics for the analysis of such data are discussed in a new book by Nelson (2003). These methods are illustrated using the SAS/QC RELIABILITY procedure. The use of the SAS/STAT GENMOD and PHREG procedures to fit regression models to recurrent events data is also illustrated. Examples are presented from the fields of medical studies and product reliability.

A very simple and powerful, yet relatively unknown, programming technique is the use of a wildcard character in variable lists. The wildcard is the colon. This paper shows how to use the technique to reduce programming time, errors and drudgery.

SAS provides a macro GLIMMIX, which can be used for modelling of discrete spatial variation in epidemiological studies, where data are aggregated into small areas such as municipalities or postcode sectors. The purpose of these models is primary to examine to what extent unmeasured spatially correlated variables can explain the outcome of interest. Some necessary additional code is proposed for this macro implementing some of the most used models for analysing and exploring spatial variation, in for example Poisson and logistic regression: Gaussian intrinsic conditional autoregression and spatial multiple memberships models originated from multilevel models. The code is illustrated by analysing the well-known Scottish lip cancer dataset with GLIMMIX and the results are compared with a Markov chain Monte Carlo approach. The code gives epidemiologists and bio-statisticians an immediate tool for analysing discrete spatial models in a familiar statistical software package.