On the Efficiency of Interim Analyses Applied to Nonclinical Studies
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On the Efficiency of Interim Analyses Applied to Nonclinical Studies SFdS, Groupe Biopharmacie et Santé, Good Preclinical Statistical Practices Working Party L. Maïofiss-Dullin Institut de Recherches Servier N. Boussac-Marlière Sanofi-Aventis B. Geffray Pfizer Global R&D C. Haimez Institut de Recherches Servier S. Harriong Institut de Recherches Servier S. Hitier Sanofi-Aventis V. Onado Sanofi-Aventis
Key Words Adaptive interim analysis; Nonclinical studies; simulations; sample size Correspondence Address L. Maïofiss-Dullin, IdRS, 11 rue des Moulineaux 92150 Suresnes, France (e-mail: [email protected]).
Interim analyses, commonly applied in clinical trials, are rarely used in the nonclinical field, although they are potentially useful for ethical and practical reasons. The purpose of our work was to evaluate the benefits of such analyses in the discovery area, with the aim of reducing sample size. Following a literature review, we focused on the Bauer and Köhne method (Biometrics 1994) because it allows early stopping in favor of H0 and H1 in either one- or two-sided situations. Moreover, as the methodology is based on Fisher combination of P values, it can be applied to any selected statistical test. Early stopping requires the selection of two interdependent parameters: α0 and α1 (thresholds for early stop in favor of H0 and H1, respectively) while controlling the type I error rate. The efficiency of the several-stage design compared to the single-analysis design was investigated us-
INTRODUCTION Interim analyses are commonly applied in clinical trials, for which their advantages/drawbacks are documented. However, they are rarely used in the nonclinical area, although they are potentially useful for ethical and practical reasons. This article presents the results of the investigation of the potential benefits of interim analyses in the Discovery area, with the aim of reducing sample size. An interim analysis design includes several stages for the study conduct and for its analysis. Each analysis is planned following a decision rule controlling the overall type I error rate. Based on the results of the statistical analysis at the end of a given step, it is possible either to stop the study early in favor of H0 or in favor of H1 or to continue the study. In the latter case, the global sample size can be reestimated; it is also possible to change the design (adaptive design methods). The context of the nonclinical field is first described; the following section presents the
ing simulations based on realistic characteristics of the preclinical area: small sample sizes, low expected proportions of active compounds in screening process, and large effect sizes. The assessment criteria were (1) the sample size gain compared to the single-analysis design at fixed power and (2) the proportions of early stops in favor of H0 and/or H1. In addition, the simulations allowed the determination of optimal values for α0 and α1. Results obtained in one- and two-sided situations in the case of a two-group comparis
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