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On generalized fixed sequence procedures for controlling the FWER

STATISTICS IN MEDICINE 2015年第30期34卷 3968-3983页

作者： Qiu, Zhiying Guo, Wenge Lynch, Gavin Sanofi Biostat & Programming Bridgewater NJ 08807 USA New Jersey Inst Technol Newark NJ 07102 USA

Testing a sequence of pre-ordered hypotheses to decide which of these can be rejected or accepted while controlling the familywise error rate (FWER) is of importance in many scientific studies such as clinical trials. In this paper, we first introduce a generalized fixed sequence procedure whose critical values are defined by using a function of the numbers of rejections and acceptances, and which allows follow-up hypotheses to be tested even if some earlier hypotheses are not rejected. We then construct the least favorable configuration for this generalized fixed sequence procedure and present a sufficient condition for the FWER control under arbitrary dependence. Based on the condition, we develop three new generalized fixed sequence procedures controlling the FWER under arbitrary dependence. We also prove that each generalized fixed sequence procedure can be described as a specific closed testing procedure. Through simulation studies and a clinical trial example, we compare the power performance of these proposed procedures with those of the existing FWER controlling procedures. Finally, when the pairwise joint distributions of the true null p-values are known, we further improve these procedures by incorporating pairwise correlation information while maintaining the control of the FWER. Copyright (C) 2015 John Wiley & Sons, Ltd.

关键词： critical values fallback procedure familywise error rate fixed sequence procedure multiple testing power

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Interim analysis based on the weighted log-rank test for delayed treatment effects under staggered patient entry

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JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2016年第5期26卷 842-858页

作者： Yoshida, Mizuki Matsuyama, Yutaka Pfizer Japan Inc Biostat & Programming Vaccine Res Tokyo Japan Univ Tokyo Sch Publ Hlth Grad Sch Med Dept Biostat Tokyo Japan

Fleming and Harrington's G(,) class of weighted log-rank tests is appropriate for detecting delayed treatment effects such as those seen in cancer vaccines. A conditional power (CP) and an alpha spending function (ASF) approach are useful for interim analyses that are conducted with the aim of early termination due to futility and efficacy, respectively. However, calculation of the CP and the total Type I error probability are often not considered for delayed effects under the staggered patient entry. In this article, we first propose methods for calculating the CP analytically based on the weighted log-rank test. We compared the performances of the proposed methods with two other methods (i.e., usual log-rank test and optimal one) under the delayed alternatives. Our simulations demonstrated that the CP based on the weighted log-rank test was more powerful than that of the usual log-rank test and was comparable to the CP based on the optimal log-rank test. Second, we quantitatively evaluated the degree to which the Type I error probability was inflated when an ASF approach with forced independent increments assumption was applied to the weighted log-rank test. The proposed method will provide valuable tools in the decision-making stage of the interim analysis.

关键词： Delayed treatment effect group sequential method interim analysis stochastic curtailment time-to-event data weighted log-rank test

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On Sample Size of the Kruskal-Wallis Test with Application to a Mouse Peritoneal Cavity Study

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BIOMETRICS 2011年第1期67卷 213-224页

作者： Fan, Chunpeng Zhang, Donghui Zhang, Cun-Hui Sanofi Aventis BX2 416A Dept Biostat & Programming Bridgewater NJ 08807 USA Rutgers State Univ Dept Stat & Biostat Piscataway NJ 08854 USA

As the nonparametric generalization of the one-way analysis of variance model, the Kruskal-Wallis test applies when the goal is to test the difference between multiple samples and the underlying population distributions are nonnormal or unknown. Although the Kruskal-Wallis test has been widely used for data analysis, power and sample size methods for this test have been investigated to a much lesser extent. This article proposes new power and sample size calculation methods for the Kruskal-Wallis test based on the pilot study in either a completely nonparametric model or a semiparametric location model. No assumption is made on the shape of the underlying population distributions. Simulation results show that, in terms of sample size calculation for the Kruskal-Wallis test, the proposed methods are more reliable and preferable to some more traditional methods. A mouse peritoneal cavity study is used to demonstrate the application of the methods.

关键词： Kruskal-Wallis test Location shift alternatives Power Sample size

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Assessments of Conditional and Unconditional Type I Error Probabilities for Bayesian Hypothesis Testing with Historical Data Borrowing

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STATISTICS IN BIOSCIENCES 2022年第1期14卷 139-157页

作者： Quan, Hui Chen, Xiaofei Chen, Xun Luo, Xiaodong Sanofi Biostat & Programming 55 Corp Dr Bridgewater NJ 08807 USA

Applications of Bayesian designs allow the borrowing of the strength of historical information and become more and more attractive in new drug developments. Nonetheless, according to the FDA guidance issued in 2020, Bayesian designs are classified as complex innovative designs that have rarely been used to provide substantial evidence of effectiveness in new drug applications. Moreover, as the historical data have already been observed and fixed, a question which arises is whether we should treat the Bayesian analysis as a conditional or unconditional analysis. Basically, it is essential to understand the frequentist operating characteristics of a Bayesian design either theoretically or through simulation in order to appropriately assess the right type I error probability and apply it to a clinical trial. In this research, we use a relatively simple setting of a normal distribution for the study endpoint to illustrate and compare the conditional and unconditional Bayesian analysis. Both scenarios of borrowing historical information of treatment effect and historical control data are considered. The thinking is applicable to the other settings or endpoints through the asymptotic normality of the distributions for the estimators of either the within or between treatment effects. Simulations are conducted to evaluate the characteristics of the methods.

关键词： Historical data borrowing Design prior Posterior probability Conditional power

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Unified approaches to assessing treatment effect of traditional Chinese medicine based on health profiles

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JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2020年第3期30卷 564-573页

作者： Cheng, Bin Zhang, Bingzhi Chow, Shein-Chung Columbia Univ Dept Biostat New York NY 10032 USA Sanofi Dept Biostat & Programming Bridgewater MA USA Duke Univ Sch Med Dept Biostat & Bioinformat Durham NC USA

Two dissimilarity indices are introduced to measure the disharmony of a human body system by mimicking the population bioequivalence and the individual bioequivalence concepts. Hypotheses for the treatment effect of a traditional Chinese medicine are formulated based on the two indices and then tested under the proposed designs by reverting an approximate confidence upper bound. The proposed methods can also be used when a drug product has multiple components or a trial has multiple endpoints.

关键词： Profile analysis principal component analysis bioequivalence biosimilarity multiple endpoints

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Comparisons between ITT and treatment emergent adverse event analyses

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STATISTICS IN MEDICINE 2008年第26期27卷 5356-5376页

作者： Quan, Hui Sun, Qiankun Zhang, Ji Shih, Weichung J. Sanofi Aventis Biostat & Programming Bridgewater NJ 08807 USA Univ Med & Dent New Jersey Sch Publ Hlth Dept Biostat Piscataway NJ 08854 USA

Different from the use of intention-to-treat (ITT) analysis for efficacy evaluation, many pharmaceutical companies currently use treatment emergent (TE) analysis for adverse event (AE) safety analysis. In the TE analysis, study period and AEs occurring after a pre-specified post-treatment window will not be included. One consideration for using the TE AE analysis is that including substantial off-drug period and events in the analysis may dilute the Power for detecting safety signals especially if after discontinuation residual treatment effect diminishes quickly. We perform quantitative analyses to compare the unbiasedness and power of the ITT and TE AE analyses under several different settings and metrics (difference in rates and relative risk). Results show that unbiasedness and power are not always in the same direction. The choice of an approach for a particular trial should depend on the focus of the analysis. A data example is used to illustrate these points. Copyright (C) 2008 John Wiley & Sons. Ltd.

关键词： fixed study duration design fixed stopping time design off-drug period informative censoring estimation bias power safety analysis

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A Bayesian method to detect drug-drug interaction using external information for spontaneous reporting system

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STATISTICS IN MEDICINE 2024年第18期43卷 3353-3363页

作者： Tada, Keisuke Maruo, Kazushi Gosho, Masahiko Sanofi KK Biostat & Programming Shinjuku Ku Tokyo Japan Univ Tsukuba Inst Med Dept Biostat Tennodai 1-1-1 Tsukuba Ibaraki 3058575 Japan

Due to the insufficiency of safety assessments of clinical trials for drugs, further assessments are required for post-marketed drugs. In addition to adverse drug reactions (ADRs) induced by one drug, drug-drug interaction (DDI)-induced ADR should also be investigated. The spontaneous reporting system (SRS) is a powerful tool for evaluating the safety of drugs continually. In this study, we propose a novel Bayesian method for detecting potential DDIs in a database collected by the SRS. By applying a power prior, the proposed method can borrow information from similar drugs for a drug assessed DDI to increase sensitivity of detection. The proposed method can also adjust the amount of the information borrowed by tuning the parameters in power prior. In the simulation study, we demonstrate the aforementioned increase in sensitivity. Depending on the scenarios, approximately 20 points of sensitivity of the proposed method increase from an existing method to a maximum. We also indicate the possibility of early detection of potential DDIs by the proposed method through analysis of the database shared by the Food and Drug Administration. In conclusion, the proposed method has a higher sensitivity and a novel criterion to detect potential DDIs early, provided similar drugs have similar observed-expected ratios to the drug under assessment.

关键词： adverse drug reaction dynamic borrowing pharmacovigilance power prior signal detection

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To tolerate or to agree: A tutorial on tolerance intervals in method comparison studies with BivRegBLS R Package

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STATISTICS IN MEDICINE 2020年第28期39卷 4334-4349页

作者： Francq, Bernard G. Berger, Marion Boachie, Charles GSK Tech R&D CMC Stat Sci Rixensart Belgium Sanofi Non Clin Biostat Biostat & Programming Montpellier France Phastar Sheffield S Yorkshire England

The well-known agreement interval by Bland and Altman is extensively applied in method comparison studies. Two clinical measurement methods are considered interchangeable if their differences are not clinically significant. The agreement interval is commonly applied to assess the spread of the differences. However, this interval is approximate (too narrow) and several authors propose calculating a confidence interval around each bound. This article demonstrates that this approach is misleading, awkward, and confusing. On the other hand, tolerance intervals are exact and can include a confidence level if needed. Tolerance intervals are also easier to calculate and to interpret. Real data sets are used to illustrate the tolerance intervals with the R package BivRegBLS under normal or log-normal assumptions. Furthermore, it is also explained how to assess the coverage probabilities of the tolerance intervals with simulations.

关键词： agreement Bland-Altman BivRegBLS coverage probabilities method comparison studies prediction interval R package tolerance interval

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Rejoinder to Letter to the Editor on "A New Principal Stratum Estimand Investigating the Treatment Effect in Patients Who Would Comply, If Treated With a Specific Treatment"

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STATISTICS IN BIOPHARMACEUTICAL RESEARCH 2021年第4期13卷 511-512页

作者： Larsen, Klaus Groes Josiassen, Mette Krog H Lundbeck & Co AS Dept Biostat & Programming Ottiliavej 9 DK-2500 Valby Denmark

"Rejoinder to Letter to the Editor on “A New Principal Stratum Estimand Investigating the Treatment Effect in Patients Who Would Comply, If Treated With a Specific Treatment”."Statistics in Biopharmaceutic...

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A seamless phase II/III design with dose optimization for oncology drug development

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STATISTICS IN MEDICINE 2024年第18期43卷 3383-3402页

作者： Li, Yuhan Zhang, Yiding Mi, Gu Lin, Ji Univ Illinois Dept Stat Champaign IL USA Sanofi US Dept Biostat & Programming Cambridge MA 02139 USA

The US FDA's Project Optimus initiative that emphasizes dose optimization prior to marketing approval represents a pivotal shift in oncology drug development. It has a ripple effect for rethinking what changes may be made to conventional pivotal trial designs to incorporate a dose optimization component. Aligned with this initiative, we propose a novel seamless phase II/III design with dose optimization (SDDO framework). The proposed design starts with dose optimization in a randomized setting, leading to an interim analysis focused on optimal dose selection, trial continuation decisions, and sample size re-estimation (SSR). Based on the decision at interim analysis, patient enrollment continues for both the selected dose arm and control arm, and the significance of treatment effects will be determined at final analysis. The SDDO framework offers increased flexibility and cost-efficiency through sample size adjustment, while stringently controlling the Type I error. This proposed design also facilitates both accelerated approval (AA) and regular approval in a "one-trial" approach. Extensive simulation studies confirm that our design reliably identifies the optimal dosage and makes preferable decisions with a reduced sample size while retaining statistical power.

关键词： dose optimization oncology project optimus sample-size re-estimation seamless design

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