In medical research there is a clear trend toward the use of larger, prospective studies of clinically significant outcomes, and there is an equal trend toward dealing with increasing numbers of variables. From such studies come the algorithms for making medical decisions often referred to as evidence based medicine, and statistical analyses comprise a large part of this process. To keep pace with medical research and to even read its literature requires ever increasing understanding of statistical methods, and all too often published papers leave out basic details. To assist in the understanding of these methods, we offer this course. It is intended for general as well as academic pathologists and for residents and fellows in pathology.

Although no prior statistical knowledge and experience is necessary, we assume that the participants want to learn greater detail about common statistical methods and that they already have some experience reading statistical results in the medical literature.

The course will be divided roughly into two parts. In the first, we will deal with the basic concepts of probability, random variables, distribution functions, statistics and tests of hypothesis. This part will also deal with the clinical probabilities of sensitivity, specificity, predictive value and ROC curves, as well as type I and II errors, p values and the coding of clinical random variables. The second part will deal with multivariate models for studying outcomes. For example, this part will present the logistic regression model for binary outcomes and the Cox proportional hazard model for analysis of survival time. Kaplan-Meier survival plots will be discussed as well. Examples of data from the literature will be used for illustration, and some analyses will be done during the course using a portable computer and the S-PLUS statistical software. Much of the detail will be provided in the course syllabus, and we will plan to leave ample time for open discussion during the course.

An in depth course syllabus will be distributed at the time of the course.