Robert T. O'Neill

Statistical analyses of adverse event data from clinical trials. Special emphasis on serious events.

The purpose of this paper is to describe some methods for analyzing and summarizing adverse event rates from clinical trials, emphasizing, in particular, serious adverse drug events and their time of occurrence, and the impact of differential subject exposure and pretreatment status on the estimation of rates.

Biostatistical considerations in pharmacovigilance and pharmacoepidemiology: linking quantitative risk assessment in pre-market licensure application safety data, post-market alert reports and formal epidemiological studies.

This paper deals with a conceptual discussion of a variety of statistical concepts, methods and strategies that are relevant to the quantitative assessment of risk derived from safety data collected during the pre- and post-marketing phase of a new drugs life cycle. A call is made for the use of more standard approaches to the analysis of safety data that are statistically and epidemiologically rigorous and for attempts to link the strategies for pre-market safety assessment with strategies for post-market safety evaluation. This link may be facilitated by recognizing the limitations and complementary roles played by pre- and post-market safety data collection schemes and by linking the quantitative analyses utilized for either exploratory or confirmatory purposes of risk assessment in each phase of safety data collection. Examples are provided of studies specifically designed to evaluate risk in a post approval setting and several available guidelines intended to improve the quality of these studies are discussed.

Industry, government, and academic panel discussion on multiple comparisons in a "real" phase three clinical trial.

Abstract A Food and Drug Administration (FDA)/Industry/Academic Panel Discussion on multiplicity aspects of a real Phase III clinical trial was held at the Third International Conference on Multiple Comparisons, August 6, 2002, in Bethesda, Maryland. The goal was to develop some consensus among industry, government, and academic statisticians concerning requirements and methods for multiplicity management in typical clinical trials. The session was tape-recorded; this article mostly comes from an edited transcript.

Statistical issues: a roundtable discussion

Dr Alderson: I am Norris Alderson. I am Associate Commissioner for Science at FDA, and I had the privilege of working with a Planning Committee to arrange for this workshop. I was the only non-statistician in the group, I must tell you, and this was really an experience for me. I want to introduce this panel because I think it is unique from the perspective that it has representatives from FDA, industry, and academia. They are Dr Susan Ellenberg from CBER, Dr Jay Siegel from Centocor, Professor Don Rubin from Harvard University, Dr Gregory Campbell from CBER, Dr Stacy Lindborg from Eli Lilly, Dr Robert O’Neill from CDER, and Professor Ralph D’Agostino, Boston University. Our task when we set up this panel was to give this group the opportunity to summarize, from their perspective, what they have heard, and also think about what is next. Speaking on behalf of the Planning Committee, we are interested in questions, as well as comments from the audience, as related to the use of Bayesian methods as a tool, particularly in the context of the critical path initiative, reduce the time for review and approval of new public health products.

Statistical Considerations in Geriatric Drug Testing

Clinical studies that involve the elderly must incorporate some unique statistical design and analysis considerations. Analysis of data from these studies as well as the collection of data require special efforts to assure that variations have been recognized and taken into account. Potential study designs and data presentations are discussed.

Competing risk analysis of life table data: Application to lifetime risk computation

In this pedagogic note we propose to assess the safety of treatment in a clinical trial, or the effect of risk exposure in a chronic animal study, in terms of two lifetime risks. These risks are computable from life table type data and take into account the effects of competing risks. We first describe their computational procedures in detail to demonstrate the need for their implementation in a computer program. We then illustrate their practical application through use of the data obtained from an actual clinical study.

Multiplicity in confirmatory clinical trials: A case study with discussion from a JSM panel

An invited panel session was conducted in the 2012 Joint Statistical Meetings, San Diego, California, USA, to stimulate the discussion on multiplicity issues in confirmatory clinical trials for drug development. A total of 11 expert panel members were invited and 9 participated. Prior to the session, a case study was previously provided to the panel members to facilitate the discussion, focusing on the key components of the study design and multiplicity. The Phase 3 development program for this new experimental treatment was based on a single randomized controlled trial alone. Each panelist was asked to clarify if he or she responded as if he or she were a pharmaceutical drug sponsor, an academic panelist or a health regulatory scientist.

Case–Control Study, Sequential

Recruitment of cases and controls for a case–control study can take an appreciable amount of time. There is potential for shortening the duration of a case–control study and reducing the numbers of required cases and controls by analyzing the accruing data sequentially and perhaps by stopping accrual early if a definitive early decision can be reached. We discuss the Wald sequential probability ratio test and group sequential tests for 1:1 matched case–control designs and tabulate the average reductions in sample sizes obtained by sequential procedures, compared with fixed sample size designs. Keywords: case–control design; Wald sequential probability ratio test; group sequential test; sample size; sequential case–control study