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Robust designs in longitudinal studies accounting for parameter and model uncertainties - application to count data

JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2020年第1期30卷 31-45页

作者： Loingeville, Florence Thu Thuy Nguyen Riviere, Marie-Karelle Mentre, France INSERM IAME UMR 1137 F-75018 Paris France Univ Paris Diderot Sorbonne Paris Cite F-75018 Paris France Univ Lille Fac Pharm Publ Hlth Epidemiol & Healthcare Qual EA 2694 F-59000 Lille France Sanofi Aventis R&D Stat Methodol Grp Biostat & Programming Dept Chilly Mazarin France

Nonlinear mixed effect models (NLMEMs) are widely used for the analysis of longitudinal data. To design these studies, optimal designs based on the expected Fisher information matrix (FIM) can be used. A method evaluating the FIM using Monte-Carlo Hamiltonian Monte-Carlo (MC-HMC) has been proposed and implemented in the R package MIXFIM using Stan. This approach, however, requires a priori knowledge of models and parameters, which leads to locally optimal designs. The objective of this work was to extend this MC-HMC-based method to evaluate the FIM in NLMEMs accounting for uncertainty in parameters and in models. When introducing uncertainty in the population parameters, we evaluated the robust FIM as the expectation of the FIM computed by MC-HMC over the distribution of these parameters. Then, the compound D-optimality criterion (CD optimality), corresponding to a weighted product of the D-optimality criteria of several candidate models, was used to find a common CD-optimal design for the set of candidate models. Finally, a compound DE-criterion (CDE optimality), corresponding to a weighted product of the normalized determinants of the robust FIMs of all the candidate models accounting for uncertainty in parameters, was calculated to find the CDE-optimal design which was robust on both parameters and model. These methods were applied in a longitudinal Poisson count model. We assumed prior distributions on the population parameters, as well as several candidate models describing the relationship between the logarithm of the event rate parameter and the dose. We found that assuming uncertainty in parameters could lead to different optimal designs, and misspecification of models could induce designs with low efficiencies. The CD- or CDE-optimal designs therefore provided a good compromise for different candidate models. Finally, the proposed approach allows for the first time optimization of designs for repeated discrete data accounting for parameter and model uncerta

关键词： Fisher information matrix longitudinal count data Markov chain Hamiltonian Monte Carlo nonlinear mixed effect models optimal design Robust design

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Adjuvant study of amcenestrant (SAR439859) versus tamoxifen for patients with hormone receptor-positive (HR plus ) early breast cancer (EBC), who have discontinued adjuvant aromatase inhibitor therapy due to treatment-related toxicity (AMEERA-6)

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ANNALS OF ONCOLOGY 2022年 33卷 S162-S163页

作者： Rossi, G. Brain, E. Dueck, A. De Swert, H. Marreaud, S. I. Partridge, A. Herold, C. Vachon, H. Spanic, T. Arahmani, A. Verbiest, T. Wang, L. Goulioti, T. Malanda, B. Carey, L. A. Anneheim, S. Paux, G. Poncet, C. Metzger, O. Cameron, D. Breast Int Grp BIG AISBL Res & Dev Dept Brussels Belgium Inst Curie Hop Rene Huguenin Dept Med Oncol St Claud France Alliance Fdn Trials AFT Boston MA USA Breast Int Grp BIG AISBL Res Operat Dept Brussels Belgium European Org Res & Treatment Canc EORTC Dept Med Brussels Belgium Alliance Fdn Trials AFT Dept Med Oncol Boston MA USA Sanofi Genzyme Oncol Cambridge MA USA European Org Res & Treatment Canc EORTC Qual Life Dept Brussels Belgium Europa Donna European Breast Canc Coalit Milan Italy Breast ternat Grp BIG AISBL Res Operat Dept Brussels Belgium European Org Res & Treatment Canc EORTC Data Management Brussels Belgium Sanofi Translat Med & Early Dev Boston MA USA Breast Int Grp BIG AISBL Brussels Belgium European Org Res & Treatment Canc EORTC Projects Direct Brussels Belgium North Carolina Lineberger Comprehens Canc Ctr Div Med Oncol Chapel Hill NC USA Sanofi Aventis Rech & Developpement Oncol Dev Vitry Sur Seine France Sanofi Aventis US LLC Biostat & Programming Bridgewater MA USA European Org Res & Treatment Canc EORTC Stat Brussels Belgium Univ Edin burgh Canc Res Ctr Inst Genet & Canc Western Gen Hosp Edinburgh Midlothian Scotland

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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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COMPARISON OF STATISTICAL MODELS ADJUSTING FOR BASELINE IN THE ANALYSIS OF PARALLEL-GROUP THOROUGH QT/QTc STUDIES

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JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2012年第3期22卷 438-462页

作者： Sun, Guowen (Gordon) Quan, Hui Kringle, Robert Meng, Zhaoling Celgene Corp Biostat & Programming Summit NJ USA Sanofi Aventis Biostat & Programming Bridgewater NJ USA

The ICH E14 guidance recommends the use of a time-matched baseline, while others recommend alternative baseline definitions including a day-averaged baseline. In this article we consider six models adjusting for baselines. We derive the explicit covariances and compare their power under various conditions. Simulation results are provided. We conclude that type I error rates are controlled. However, one model outperforms the others on statistical power under certain conditions. In general, the analysis of covariance (ANCOVA) model using a day-averaged baseline is preferred. If the time-matched baseline has to be used as per requests from regulatory agencies, the analysis by time point using ANCOVA model should be recommended.

关键词： Day-averaged baseline Parallel studies Statistical power Time-matched baseline Type I error

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Blinded assessment of treatment effects for survival endpoint in an ongoing trial

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PHARMACEUTICAL STATISTICS 2012年第3期11卷 204-213页

作者： Xie, Jun Quan, Hui Zhang, Ji Purdue Univ Dept Stat W Lafayette IN 47907 USA Sanofi Aventis Biostat & Programming Dept Bridgewater NJ 08807 USA

Many assumptions, including assumptions regarding treatment effects, are made at the design stage of a clinical trial for power and sample size calculations. It is desirable to check these assumptions during the trial by using blinded data. Methods for sample size re-estimation based on blinded data analyses have been proposed for normal and binary endpoints. However, there is a debate that no reliable estimate of the treatment effect can be obtained in a typical clinical trial situation. In this paper, we consider the case of a survival endpoint and investigate the feasibility of estimating the treatment effect in an ongoing trial without unblinding. We incorporate information of a surrogate endpoint and investigate three estimation procedures, including a classification method and two expectationmaximization (EM) algorithms. Simulations and a clinical trial example are used to assess the performance of the procedures. Our studies show that the expectationmaximization algorithms highly depend on the initial estimates of the model parameters. Despite utilization of a surrogate endpoint, all three methods have large variations in the treatment effect estimates and hence fail to provide a precise conclusion about the treatment effect. Copyright (c) 2012 John Wiley & Sons, Ltd.

关键词： EM algorithm hazard ratio interim analysis surrogate endpoint

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Optimal inferences for proportional hazards model with parametric covariate transformations

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ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS 2012年第4期64卷 715-736页

作者： Fan, Chunpeng Fine, Jason P. Jeong, Jong-Hyeon Sanofi Aventis Dept Biostat & Programming Bridgewater NJ 08807 USA Univ N Carolina Dept Biostat Chapel Hill NC 27599 USA Univ Pittsburgh Dept Biostat Pittsburgh PA 15261 USA

The traditional Cox model assumes a log-linear relationship between covariates and the underlying hazard function. However, the linearity may be invalid in real data. We study a Cox model which employs unknown parametric covariate transformations. This model is applicable to observational studies or randomized trials when a treatment effect is investigated after controlling for a confounding variable that may have non-log-linear relationship with the underlying hazard function. While the proposed generalization is simple, the inferential issues are challenging due to the loss of identifiability under no effects of transformed covariates. Optimal tests are derived for certain alternatives. Rigorous parametric inference is established under regularity conditions and non-zero transformed covariate effects. The estimates perform well in simulation studies with realistic sample size, and the proposed tests are more powerful than the usual partial likelihood ratio test, which is no longer optimal. Data from a breast cancer trial are used to illustrate the model building strategy and the better fit of the proposed model, comparing to the traditional Cox model.

关键词： Cox proportional hazards model Optimal test Semi-parametric model

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A statistical approach to central monitoring of data quality in clinical trials

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CLINICAL TRIALS 2012年第6期9卷 705-713页

作者： Venet, David Doffagne, Erik Burzykowski, Tomasz Beckers, Francois Tellier, Yves Genevois-Marlin, Eric Becker, Ursula Bee, Valerie Wilson, Veronique Legrand, Catherine Buyse, Marc IDDI Inc Houston TX 77060 USA IDDI Louvain Belgium Free Univ Brussels IRIDIA Brussels Belgium Hasselt Univ Interuniv Inst Biostat & Stat Bioinformat I BioSt Diepenbeek Belgium GlaxoSmithKline Biol Wavre Belgium Sanofi Aventis R&D Biostat & Programming Bridgewater NJ USA F Hoffmann LaRoche Ltd Basel Switzerland Translat Res Oncol TRIO Paris France Catholic Univ Louvain Inst Stat Biostat & Actuarial Sci ISBA B-1348 Louvain Belgium

Background Classical monitoring approaches rely on extensive on-site visits and source data verification. These activities are associated with high cost and a limited contribution to data quality. Central statistical monitoring is of particular interest to address these shortcomings. Purpose This article outlines the principles of central statistical monitoring and the challenges of implementing it in actual trials. Methods A statistical approach to central monitoring is based on a large number of statistical tests performed on all variables collected in the database, in order to identify centers that differ from the others. The tests generate a high-dimensional matrix of p-values, which can be analyzed by statistical methods and bioinformatic tools to identify extreme centers. Results Results from actual trials are provided to illustrate typical findings that can be expected from a central statistical monitoring approach, which detects abnormal patterns that were not (or could not have been) detected by on-site monitoring. Limitations Central statistical monitoring can only address problems present in the data. Important aspects of trial conduct such as a lack of informed consent documentation, for instance, require other approaches. The results provided here are empirical examples from a limited number of studies. Conclusion Central statistical monitoring can both optimize on-site monitoring and improve data quality and as such provides a cost-effective way of meeting regulatory requirements for clinical trials. Clinical Trials 2012;9: 705-713. http://***

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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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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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An Alternative Way to Classify Missing Data Mechanism in Clinical TrialsA Dialogue on Missing Data

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JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2011年第2期21卷 355-361页

作者： Wei, Lynn Sanofi Aventis US Biostat & Programming Bridgewater NJ 08807 USA

Statisticians in pharmaceutical field are constantly challenged by missing data caused by patient dropout in clinical trials. What the targeted population parameter for statistical inference should be when missing data are present has been a much-debated point. Many missing data methods aim at the so-called hypothetical parameter, i.e., treatment effect of a drug assuming no patients dropout from a clinical trial for the drug. Other methods intend to combine all dropout information into the treatment effect estimate. We believe that patient dropouts should not be treated equally when determining the population parameter of treatment effect. The objective of clinical trials, after all, is to evaluate a drug's effect on patients. Dropouts due to drug-related reasons such as drug-induced adverse experience are part of the drug's attributes, while dropout due to non-drug-related reasons, such as protocol deviation, are not inherent characteristics of the drug. Hence we propose to classify the patient dropouts into two classes: intrinsic (drug-related) and extrinsic (non-drug-related) dropouts. The former should be taken into account when defining the population parameter of the treatment effect, while the latter should not be. This classification will help determine a target population parameter that depicts a fair picture of a drug's effect, while the common classification of missing data as missing completely random (MCAR), missing at random (MAR), and missing not at random (MNAR) will help define appropriate statistical approach to analysis when missing data exist. Other related issues, such as statistical inference under this classification and implementing the classification in real clinical trials, are also touched upon here.

关键词： Extrinsic Intrinsic Missing data in clinical trials

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