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To register for the training courses, visit the JSM 2007 web site.

This full-day course will be taught by Christy Chuang-Stein (Pfizer), Alex Dmitrienko (Eli Lilly and Company) and Geert Molenberghs (Universiteit Hasselt) from 8:30 to 5:00 on Monday, July 30.

The course covers four important topics that commonly face statisticians and research scientists conducting clinical research. The 4 topics are analysis of stratified data, multiple comparisons and multiple endpoints, interim analysis and interim data monitoring, and analysis of incomplete data.

The course offers a well-balanced mix of theory and applications. It presents practical advice from experts and discusses regulatory considerations. The discussed statistical methods will be implemented using the SAS software. Clinical trial examples will be used to illustrate the statistical methods.

The course is designed for statisticians working in the pharmaceutical or biotechnology industries as well as contract research organizations. It is equally beneficial to statisticians working in institutions that deliver health care and government branches that conduct health-care related research. The attendees are required to have basic knowledge of clinical trials. Familiarity with drug development is highly desirable, but not necessary.

The course will be taught in four 1- or 2-hour sections corresponding to the four topics described in the abstract. A more detailed description of each topic is as follows.

1. Analysis of stratified data (analysis of clinical outcomes in the presence of influential covariates as well as statistical methods for studying treatment-by-stratum interactions).

2. Multiple comparisons and multiple endpoints (statistical methods for handling multiplicity issues in clinical trials, including closed, fixed-sequence multiple tests and gatekeeping methods).

3. Interim data monitoring (popular approaches for designing and monitoring group sequential trials, stochastic curtailment methods based on frequentist and Bayesian tests).

4. Analysis of incomplete data (statistical methods for the analysis of incomplete longitudinal continuous and categorical data, including complete case analysis, last observation carried forward, likelihood-based methods and multiple imputation).

At the beginning of each section the instructor will introduce relevant statistical methodology, compare existing statistical approaches and present practical advices. The introduced statistical methods will then be illustrated using examples from clinical trials. The instructor will discuss regulatory considerations and cover software implementation of the described statistical approaches.

The course will be aimed at clinical statisticians and research scientists working in the pharmaceutical or biotechnology industries as well as those working for contract research organizations that support the former. The course will also be beneficial to statisticians and research scientists working in institutions that deliver health care and government branches that conduct health-care related research. The attendees are required to have basic knowledge of clinical trials. Familiarity with drug development is highly desirable, but not necessary.