Biopharmaceutical Section
Web-based Training Series

New for Fall 2008 Webinars: Attendees can receive the audio of the presentation via telephone call-in number OR audio streaming over the internet. Now persons attending the webinar outside the U.S. or Canada do not have to use the international toll number.

Registration Fees:
Member of the Biopharmaceutical Section: $44
ASA Member: $59
Nonmember: $74

Webinar 1
Stratified Analyses: Tips for Improving Power
Devan V. Mehrotra (Merck Research Laboratories)
October 21, 2008, Noon-2:00 PM (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

Webinar 2
Bioequivalence
Scott Patterson (Wyeth) and Byron Jones (Pfizer)
December 10, 2008, 10:00-Noon PM (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. Software implementation of the described statistical methods may be found at http://www.crcpress.com/e_products/downloads/download.asp?cat_no=C5300.

Text book
Patterson S, Jones B. (2006). Bioequivalence and Statistics in Clinical Pharmacology (Chapters 1-2). Chapman and Hall, CRC Press, London.

Webinar 3
Classical sample-size analysis for hypothesis testing (Part I)
Ralph O'Brien (Case Western)
January 29, 2009, Noon-2:00 PM (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 a] = a, 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 > a] = ß, and the power = Prob[pa] = 1 - ß. 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 a and ß 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)
Ralph O'Brien (Case Western)
February 12, 2009, Noon-2:00 PM (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.

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About Webinars

A webinar is a seminar which is conducted over the World Wide Web. It is a type of web conferencing. In contrast to a Webcast, which is transmission of information in one direction only, webinars are designed to be interactive between the presenter and audience. A webinar is 'live' in the sense that information is conveyed according to an agenda, with a starting and ending time. In the case of the Biopharmaceutical Section Webinar Series, the presenter speaks over a standard telephone line, pointing out information being presented on screen. The audience can respond via a chat feature. The word 'webinar' is a blend of web and seminar.

System Requirements

Operating System: Windows 2000 to present, Macintosh OSX, Linux Redhat
Browser: Internet Explorer (IE) 5.5+, Firefox 2.0+, Opera 9.0+, Mozilla 1.71+, Safari 3.1
Other: Events with streaming audio or video require Macromedia Flash 8.0+
Hardware: 56Kbps Internet access. Speakers or headphones and cable modem, DSL, ISDN, or equivalent broadband needed to receive audio/video streaming (128K minimum).

*Opera browser will not allow access to the Question and Answer feature of the console.

More information on the Web-based training program can be found at the biopharmaceutical network's web site.

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