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Sample size considerations for Japanese patients in a multi-regional trial based on MHLW guidance

PHARMACEUTICAL STATISTICS 2010年第2期9卷 100-112页

作者： Quan, Hui Zhao, Peng-Liang Zhang, Ji Roessner, Martin Aizawa, Kyo Sanofi Aventis Biostat & Programming Bridgewater NJ 08807 USA

Since the publication of the International Conference on Harmonization E5 guideline, new drug approvals in Japan based on the bridging strategy have been increasing. To further streamline and expedite new drug development in Japan, the Ministry of Health, Labour and Welfare, the Japanese regulatory authority, recently issued the 'Basic Principles on Global Clinical Trials' guidance to promote Japan's participation in multi-regional trials. The guidance, in a Q&A format, provides two methods as examples for recommending the number of Japanese patients in a multi-regional trial. Method l in the guidance is the focus of this paper. We derive formulas for the sample size calculations for normal, binary and survival endpoints. Computations and simulation results are provided to compare different approaches. Trial examples are used to illustrate the applications of the approaches. Copyright (C) 2009 John Wiley & Sons, Ltd.

关键词： bridging study ethnic effect delta method survival endpoint risk reduction

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Considerations for pediatric trial designs and analyses

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JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2018年第3期28卷 413-436页

作者： Cho, Meehyung Qiu, Zhiying Ye, Jenny Quan, Hui Zhao, Peng-Liang Sanofi US Biostat & Programming Bridgewater NJ 08807 USA

There are challenges in designing pediatric trials arising from special ethical issues and the relatively small accessible patient population. The application of conventional phase 3 trial designs to pediatrics is not realistic in some therapeutic areas. To address this issue, we propose various approaches for designing pediatric trials that incorporate data available from adult studies using James-Stein shrinkage estimation, empirical shrinkage estimation, and Bayesian methods. We also apply the concept of consistency used in multi-regional trials to pediatric trials. The performance of these methods is assessed through representative scenarios and an example using actual Type 2 diabetes mellitus (T2DM) trials.

关键词： Bayesian method empirical shrinkage estimation James-Stein shrinkage estimation pediatric sample size weighted combination

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Optimal Sample Sizes and Go/No-Go Decisions for Phase II/III Development Programs Based on Probability of Success

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STATISTICS IN BIOPHARMACEUTICAL RESEARCH 2011年第3期3卷 463-475页

作者： Jiang, Kaihong Sanofi Aventis Biostat & Programming Bridgewater NJ 08807 USA

In the course of late-stage drug development, a phase II/III clinical development program faces a series of sample size planning and decision problems: the simultaneous sample sizes determination (SSD) across phase II and phase III trials at the program planning stage, the go/no-go decisions after phase II data have been observed, and the SSD of phase III trials incorporating phase II data. Conventionally, SSD are carried out on a trial-by-trial basis and independent of one another even though the trials are within the same program;and the go/no-go decisions are made based on the p-values, point estimates, and confidence intervals which may be, at times, ambiguous for decision making. In a combined empirical Bayesian and frequentist framework, this article proposes a probability of success (POS) function to allow an integrated approach to the SSD and go/no-go decision problems for a phase II/III program in which both the phase II and III trials share a common, normally distributed response variable. For the first time, optimal sample sizes can be determined simultaneously for a phase II/III program using the proposed POS function. As the POS function includes the conventional power function as a special case, it enjoys broader applications including SSD of phase II trials and that of phase III trials incorporating phase II data-neither of which can be handled as readily using the conventional power function.

关键词： Bayesian Due diligence Integrated Portfolio management Program-wise planning Simultaneous sample size estimation

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Use of the Average Baseline Versus the Time-Matched Baseline in Parallel Group Thorough QT/QTc Studies

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JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2010年第3期20卷 665-682页

作者： Meng, Zhaoling Quan, Hui Fan, Li Kringle, Robert Sun, Gordon Sanofi Aventis Biostat & Programming Bridgewater NJ 08807 USA

Using historical studies, we compared the impact of using the average baseline or time-matched baseline on diurnal effect correction, treatment effect estimation, and analysis of variance/covariance (ANOVA/ANCOVA) efficiency in a parallel thorough QT/QTc (TQT) study. Under a multivariate normal distribution assumption, we derived conditions for achieving unbiasness and better efficiency when using the average baseline, and confirmed these conditions using historical TQT studies. Furthermore, simulations were conducted under the randomized trial with and without observed imbalanced baseline settings. We conclude that the analyses using average baseline yield better efficiency and unbiased or less biased results under our TQT study conditions.

关键词： ANCOVA ANOVA Bias Diurnal effect correction Efficiency

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Bayesian methods for the analysis of early-phase oncology basket trials with information borrowing across cancer types

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STATISTICS IN MEDICINE 2020年第25期39卷 3459-3475页

作者： Jin, Jin Riviere, Marie-Karelle Luo, Xiaodong Dong, Yingwen Johns Hopkins Univ Bloomberg Sch Publ Hlth Dept Biostat 615 N Wolfe St Baltimore MD 21205 USA Sanofi Res & Dev Dept Biostat & Programming Chilly Mazarin France Sanofi Dept Biostat & Programming Bridgewater MA USA Sanofi Dept Biostat & Programming Oncol Cambridge MA USA

Research in oncology has changed the focus from histological properties of tumors in a specific organ to a specific genomic aberration potentially shared by multiple cancer types. This motivates the basket trial, which assesses the efficacy of treatment simultaneously on multiple cancer types that have a common aberration. Although the assumption of homogeneous treatment effects seems reasonable given the shared aberration, in reality, the treatment effect may vary by cancer type, and potentially only a subgroup of the cancer types respond to the treatment. Various approaches have been proposed to increase the trial power by borrowing information across cancer types, which, however, tend to inflate the type I error rate. In this article, we review some representative Bayesian information borrowing methods for the analysis of early-phase basket trials. We then propose a novel method called the Bayesian hierarchical model with a correlated prior (CBHM), which conducts more flexible borrowing across cancer types according to sample similarity. We did simulation studies to compare CBHM with independent analysis and three information borrowing approaches: the conventional Bayesian hierarchical model, the EXNEX approach, and Liu's two-stage approach. Simulation results show that all information borrowing approaches substantially improve the power of independent analysis if a large proportion of the cancer types truly respond to the treatment. Our proposed CBHM approach shows an advantage over the existing information borrowing approaches, with a power similar to that of EXNEX or Liu's approach, but the potential to provide substantially better control of type I error rate.

关键词： basket trials Bayesian hierarchical models between-indication similarity early-phase oncology study information borrowing across cancer types

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Adaptive and repeated cumulative meta-analyses of safety data during a new drug development process

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PHARMACEUTICAL STATISTICS 2015年第3期14卷 161-171页

作者： Quan, Hui Ma, Yingqiu Zheng, Yan Cho, Meehyung Lorenzato, Christelle Hecquet, Carole Sanofi Dept Biostat & Programming Bridgewater NJ 08807 USA

During a new drug development process, it is desirable to timely detect potential safety signals. For this purpose, repeated meta-analyses may be performed sequentially on accumulating safety data. Moreover, if the amount of safety data from the originally planned program is not enough to ensure adequate power to test a specific hypothesis (e.g., the noninferiority hypothesis of an event of interest), the total sample size may be increased by adding new studies to the program. Without appropriate adjustment, it is well known that the type I error rate will be inflated because of repeated analyses and sample size adjustment. In this paper, we discuss potential issues associated with adaptive and repeated cumulative meta-analyses of safety data conducted during a drug development process. We consider both frequentist and Bayesian approaches. A new drug development example is used to demonstrate the application of the methods. Copyright (c) 2015 John Wiley & Sons, Ltd.

关键词： conditional power combination test type I error rate control fixed and random effects models noninferiority

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Interim treatment selection with a flexible selection margin in clinical trials

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STATISTICS IN MEDICINE 2013年第15期32卷 2529-2543页

作者： Wu, Yujun Zhao, Peng-Liang Sanofi Aventis US Biostat & Programming Bridgewater NJ 08807 USA

When several treatment arms are administered along with a control arm in a trial, dropping the non-promising treatments at an early stage helps to save the resources and expedite the trial. In such adaptive designs with treatment selection, a common selection rule is to pick the most promising treatment, for example, the treatment with the numerically highest mean response, at the interim stage. However, with only a single treatment selected for final evaluation, this selection rule is often too inflexible. We modified this interim selection rule by introducing a flexible selection margin to judge the acceptable treatment difference. Another treatment could be selected at the interim stage in addition to the empirically best one if the differences of the observed treatment effect between them do not exceed this margin. We considered the study starting with two treatment arms and a control arm. We developed hypothesis testing procedures to assess the selected treatment(s) by taking into account the interim selection process. Compared with the one-winner selection designs, the modified selection rule makes the design more flexible and practical. Copyright (c) 2012 John Wiley & Sons, Ltd.

关键词： interim treatment selection adaptive design interim analysis clinical trial drop-the-losers design

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A NOTE ON POWER AND SAMPLE SIZE CALCULATIONS FOR THE KRUSKAL-WALLIS TEST FOR ORDERED CATEGORICAL DATA

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JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2012年第6期22卷 1162-1173页

作者： Fan, Chunpeng Zhang, Donghui Sanofi US Dept Biostat & Programming Bridgewater NJ 08807 USA

Although the Kruskal-Wallis test has been widely used to analyze ordered categorical data, power and sample size methods for this test have been investigated to a much lesser extent when the underlying multinomial distributions are unknown. This article generalizes the power and sample size procedures proposed by Fan et al. (2011) for continuous data to ordered categorical data, when estimates from a pilot study are used in the place of knowledge of the true underlying distribution. Simulations show that the proposed power and sample size formulas perform well. A myelin oligodendrocyte glycoprotein (MOG) induced experimental autoimmunce encephalomyelitis (EAE) mouse study is used to demonstrate the application of the methods.

关键词： Ordinal data Rank test Ties

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A CASE STUDY OF MODELING AND EXPOSURE-RESPONSE PREDICTION FOR COUNT DATA

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JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2014年第5期24卷 1073-1090页

作者： Quan, Hui Mao, Xuezhou Wei, Lynn Wang, Lin Sanofi Biostat & Programming Bridgewater NJ 08807 USA

Even with two doses of an experimental drug in Phase III studies, with the commonly used approach for assessing treatment effects of individual doses, it may still be difficult to determine the final commercial dose. In such a scenario, with plasma concentration data collected in the studies, a modeling approach can be applied to predict treatment effects at different plasma concentration levels. Through an established relationship between plasma concentration and dose, the treatment effects of doses not studied in the Phase III studies can then be predicted. The results can further be applied to justify the final dose confirmation or selection. In this article, a Phase III program example with count data as the primary endpoint in the multiple sclerosis area is used to illustrate the application of such a technique for dose confirmation. Several models, such as the overdispersion Poisson model, negative binomial model, and recurrent event models, are considered. The negative binomial model is preferable due to better data fitting and the capability of within-treatment assessment and between-treatment comparison.

关键词： Annualized relapse rate Negative binomial Plasma concentration Recurrent event Risk ratio Trial simulation

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Sample size calculations for comparing two groups of count data

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JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2019年第1期29卷 115-127页

作者： Wang, Lin Fan, Chunpeng Sanofi US Inc Dept Biostat & Programming Bridgewater NJ USA

y A sample size formula for comparing two groups of count data is derived using the method of moments by matching the first and second moments of the distribution of the count data, and it does not need any further distributional assumption. Compared to sample size formulas derived using a likelihood-based approach or using simulations, the proposed sample size formula applies to count data following any distribution in addition to the negative binomial distribution. The proposed sample size formula can be used even when the study is analyzed with a likelihood-based approach. Because asymptotically, the method of moments is no more efficient than likelihood-based approaches, the proposed sample size formula can be viewed as an upper bound of the required sample size by likelihood-based approaches to start the study. Applications of the sample size formula are illustrated using an asthma study design.

关键词： Method of moments mixture of negative binomial distributions recurrent events

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