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• W2182123265 abstract "Analyses of clinical adverse event (AE) data are routinely summarized using between-group p-values for every AE within each of several body systems. If the p-values are reported and/or interpreted without multiplicity considerations, there is a potential for too many false positive findings. This can needlessly muddy the safety profile of an otherwise safe drug or vaccine. Accordingly, we offer an innovative proposal for tackling multiplicity, one that provides a reasonable balance between no adjustment and over adjustment, and is easy to automate. Our proposal involves a two-step use of adjusted p-values based on the false discovery rate (FDR) methodology of Benjamini and Hochberg (1995). Real data from three moderate to large vaccine trials are used to illustrate our proposed “Double FDR” approach and to reinforce the potential impact of failing to account for multiplicity. This work was in collaboration with the late Prof. John Tukey. CONTACT INFORMATION Devan V. Mehrotra, Ph.D. Merck Research Laboratories Clinical Biostatistics, UN-A102 785 Jolly Road, Building C Blue Bell, PA 19422 Tel: 484-344-2621 Fax: 484-344-7105 E-mail: devan_mehrotra@merck.com INTRODUCTION The evaluation of safety is an important part of clinical trials of pharmaceutical and biological products. Safety endpoints, or adverse experiences (AEs) typically can be categorized as three types. Tier 1 adverse experiences are those associated with specific hypotheses being tested formally in the clinical trial. Tier 2 AEs would be the set encountered as part of the overall patient safety reporting in the trial. There are usually no hypotheses associated with this set of AEs but the data are collected and reported in a comprehensive manner to allow an overall evaluation of safety. A pvalue or confidence interval estimate of a risk difference or relative risk is reported for each AE encountered in a study. Tier 3 AEs are rare spontaneous reports of serious events that require specific clinical evaluation. The focus of this paper is on the Tier 2 AEs and the issues of multiplicity that need to be considered when interpreting safety data reported in clinical trials. We start with a motivating example using data from a vaccine study and discuss the multiplicity issues that arise with Tier 2 AEs. We then give a brief overview of Familywise Error Rates and False Discovery Rates and propose a method for flagging AEs using objective statistical criteria. The methods are summarized using three vaccine studies. Finally, we offer some concluding remarks. MOTIVATING EXAMPLE A safety and immunogenicity trial of a candidate quadrivalent vaccine containing measles, mumps, rubella, and varicella (MMRV) was conducted in 296 health toddlers, 12 to 18 months of age. Participants were randomly assigned to receive MMRV on day 0 (Group 1) or MMR on day 0 followed by V on day 42 (Group 2). All toddlers received PedvaxHIB on day 0. Safety follow-up used standard AE reporting and the primary question was for local and systemic reactions for the varicella component. This involved a comparison of AEs between Group 1, days 0-42, and Group 2, days 4284. The clinical AE counts for all the Tier 2 AEs are summarized in Table 1 at the end of this paper for the N1 = 148 toddlers and the N2 = 132 toddlers who received a varicella injection. The difference in proportions of toddlers experiencing AEs and a twosided p-value using Fisher’s Exact Test also is provided. Note that 40 different AEs involving 8 different body systems were encountered in this study. Four AEs gave p-values <0.05. MULTIPLICITY ISSUES This example motivates the question of how to report and interpret Tier 2 AEs in clinical trial settings. Clearly, the practice of computing a p-value for each AE has the potential for an excessive number of false positive safety findings if multiplicity is ignored. This can muddy the interpretation of the safety profile of the vaccine/drug. Alternatively, using a Bonferroni type of multiplicity adjustment may mask a true effect. The challenge is to develop a procedure for tackling multiplicity that can provide a proper balance between “no adjustment” and “too much adjustment.” It would be important that the procedure be easy to implement within existing software. In this paper we investigate the use of the False Discovery Rate (Benjamini and Hochberg, Proceedings of the Annual Meeting of the American Statistical Association, August 5-9, 2001" @default.
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• W2182123265 title "MULTIPLICITY CONSIDERATIONS IN CLINICAL SAFETY ANALYSES" @default.
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