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Fall 2008 training series
Fall 2008 training series includes webinars on the analysis of stratified data in clinical trials, bioequivalence problems and sample-size analysis.
Webinar 1: Stratified analyses: Tips for improving power
Presented by Dr. Devan V. Mehrotra (Merck Research Laboratories) on October 21, 2008 (noon-2:00 Eastern time).
The Mantel-Haenszel test and the van Elteren test, both implemented in SAS PROC FREQ, are widely used for stratified analyses of binary and ranked data, respectively. Both methods have good power properties, but only under certain restrictive assumptions; when the assumptions are violated, there can be a notable loss in power. In this tutorial, we will describe some alternatives to these popular methods, including the "minimum risk" weighting strategy for stratified binary data and an "adaptive" testing strategy for stratified rank-based analyses. We will use simulations to provide guidance on which methods might be more appropriate under given conditions. Numerical examples will be used throughout for illustration, and to reinforce the key points.
Handouts
Questions and answers
Responses to the questions submitted during the October 21 webinar.
Webinar 2: Bioequivalence
Presented by Drs. Scott Patterson (Wyeth) and Byron Jones (Pfizer) on December 10, 2008 (10:00-noon Eastern time).
This introductory course will focus on the design and analysis of bioequivalence studies for orally administered drug products. It provides a detailed overview of the most well-established method of demonstrating bioequivalence. The following topic will be covered: 1. Drug development, clinical pharmacology and statistics. 2. History and international bioequivalence regulations. 3. 2x2 cross-over designs and average Bioequivalence with examples. The course offers a well-balanced mix of theory and applications, including regulatory considerations. Examples from real trials are used throughout the discussion to illustrate the statistical approaches discussed in the course.
Text book
Patterson S, Jones B. (2006). Bioequivalence and Statistics in Clinical Pharmacology (Chapters 1-2). Chapman and Hall, CRC Press, London.
Software implementation
Software implementation of the described statistical methods may be found on the book's web site.
Handouts
Questions and answers
Responses to the questions submitted during the December 10 webinar.
Webinar 3: Classical sample-size analysis for hypothesis testing (Part I)
Presented by Prof. Ralph O'Brien (Case Western) on January 29, 2009 (noon-2:00 Eastern time).
This session will cover the key concepts, all by discussing a straightforward, realistic, two-arm clinical trial being planned to test a fictional lactate-lowering drug (QCA) that promises to reduce mortality in children with severe malaria. We will first discuss that if QCA has no effect, the distribution of the p-value is uniform between 0.00 to 1.00, regardless of how large the total sample size (N) is, a concept misunderstood by many who think that p-values shift towards 1.00 when the null hypothesis is true. The graphs let us see that under the null hypothesis, Prob[p <= alpha] = alpha, the Type I error rate. Then I show how the p-value shifts more towards 0.00 when QCA is conjectured to be more effective and how this leftward shift increases when N increases and when N is allocated in a nearly balanced manner between the two arms. This lets us see graphically the Type II error rate, Prob[p > alpha] = beta, and the power = Prob[p <= alpha] = 1 - beta. Time will be devoted to discussing how one formulates the scenarios for a typical sample-size analysis and how one presents the results of this exercise in a statistical considerations section in a research proposal. We will cover some of the messy issues that arise, such as: Are sample-size analyses relevant for "pilot" studies? How do we deal with the built-in trade-off between alpha and beta. In other words, what are a prudent a and an acceptable power? How does using a one-sided versus two-sided hypotheses affect these matters? What is "statistical gaming" in sample-size analysis?
Text book
O'Brien R, Castelloe J. (2007). Sample-size analysis for traditional hypothesis testing: Concepts and issues. Pharmaceutical Statistics Using SAS: A Practical Guide. Dmitrienko A, Chuang-Stein C, D'Agostino R. (editors). SAS Press: Cary, NC.
Webinar 4: Classical sample-size analysis for hypothesis testing (Part II)
Presented by Prof. Ralph O'Brien (Case Western) on February 12, 2009 (noon-2:00 Eastern time).
This session will quickly review the essentials of the first session and then continue with the malaria example to explore more vital questions that classical sample-size analyses fails to address. That is, if the planned study yields a significant p-value, what is the chance this is a Type I error? Likewise, if the study turns out non-significant, what is the chance this is a Type II error? By using judgments about the probability that the null hypothesis is false, we apply Bayes Theorem (taught with simple calculations in a table, no formulas) to assess these "crucial" Type I and II error rates, and we show (using a simple a Excel program) that they can differ greatly from their classical counterparts. Importantly, both crucial error rates are reduced by increasing the statistical power. Studies with small N that propose to test speculative hypotheses are prone to large crucial error rates. The final phase of the session deals with an actual early trial of a highly novel treatment for atherosclerosis in which a 0.02 p-value was deemed to be "the first convincing demonstration" of efficacy. What the investigators failed to understand, however, is that their crucial Type I error rate may have been well over 85%. We will end by going though a mock study planning exercise to design the follow-up study.
Text book
O'Brien R, Castelloe J. (2007). Sample-size analysis for traditional hypothesis testing: Concepts and issues. Pharmaceutical Statistics Using SAS: A Practical Guide. Dmitrienko A, Chuang-Stein C, D'Agostino R. (editors). SAS Press: Cary, NC.
Registration
Registration fee is $44 (Member of the Biopharmaceutical Section), $59 (ASA Member) and $74 (Nonmember). To register for individual webinars, visit the Biopharmaceutical Section's web page.
 
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