Comment.

The article comments on the paper by Murray Aitkin, Nan Laird and Brian Francis which presents an analyses of survival of patients in the Stanford Heart Transplantation Program, published in the June 1983 issue of the "Journal of the American Statistical Association." The author always approaches an article that purports to consider an analysis of a classical data set with a great deal of interest. He wonders: "Will the authors be able to discover new and useful results that others have been unable to detect in the past?" Of course, authors take a chance using a popular data set to demonstrate their new methodology. The problem is that perhaps the data set really does not have too much going for it in the first place even though the authors' new approach to analysis may have merit. Such may be the case here. When an experimenter visits with a statistical consultant concerning a proper experimental design to investigate the effectiveness of a new treatment, the consultant is called on to propose valid methods for demonstrating the repeatability of the effect of the new treatment over the standard one. Students in beginning courses in experimental design often have trouble understanding the concept and importance of replication. As statisticians they need to be sure that they do not overlook this concept as it applies to verification of a new statistical methodology. In this article, of course, the authors have chosen to emphasize the analysis of a classic data set rather than the evaluation of a new methodology, a task that can not be done on one data set.