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POD-BIN: A PROBABILITY OF DECISION BAYESIAN INTERVAL DESIGN FOR TIME-TO-EVENT DOSE-FINDING TRIALS WITH MULTIPLE TOXICITY GRADES

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Annals of Applied Statistics 2025年第1期19卷 147-168页

作者： Liu, Meizi Lin, Ji Mi, Gu Lorenzato, Christelle Chen, Xun Ji, Yuan Statistical and Quantitative Sciences Takeda Japan Department of Biostatistics and Programming Sanofi France Department of Public Health Sciences University of Chicago United States

We introduce a Bayesian framework centered on the “probability of decision” for designing dose-finding trials. The proposed PoD-BIN design evaluates the posterior predictive probabilities of up-and-down decisions. In PoDBIN, multiple grades of toxicity, categorized as mild toxicity (MT) and doselimiting toxicity (DLT), are simultaneously modeled, with the primary outcome being time-to-toxicity for both MT and DLT. This approach allows the enrollment of new patients while previously enrolled patients are still being monitored for toxicity, potentially reducing the trial duration. The Bayesian decision rules in PoD-BIN employ the probability of decisions to balance the trade-off between accelerating the trial and the risk of exposing patients to excessively toxic doses. Through numerical examples, we illustrate the trade-off between speed and safety of PoD-BIN and compare it with existing designs. PoD-BIN demonstrates the ability to control the frequency of risky decisions while simultaneously shortening trial duration in simulations. © Institute of Mathematical Statistics, 2025.

关键词： and phrases. Dose limiting toxicity equivalence interval latent probit regression moderate toxicities phase I trial time to toxicity

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Variable Selection in High-Dimensional Logistic Regression Models Using a Whitening Approach

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 800-807页

作者： Wencan Zhu Céline Lévy-Leduc Nils Ternès UMR MIA- Paris AgroParisTech INRAE Université Paris-Saclay Gif-sur-Yvette France Department of Biostatistics and Programming Sanofi R&D Bridgewater NJ USA CNRS/Laboratoire de Probabilités Statistique et Modélisation (LPSM) Université Paris Cité Paris France

In bioinformatics, the rapid development of sequencing technology has enabled us to collect an increasing amount of omics data. Classification based on omics data is one of the central problems in biomedical research. However, omics data usually has a limited sample size but high feature dimensions, and it is assumed that only a few features (biomarkers) are active, i.e. informative to discriminate between different categories. Identifying active biomarkers for classification has therefore become fundamental for omics data analysis. Focusing on binary classification, we propose an innovative feature selection method aiming at dealing with the high correlations between the biomarkers. Our method, WLogit, consists in whitening the design matrix to remove the correlations between biomarkers, then using a penalized criterion adapted to the logistic regression model to select features. The results from numerical experiments suggest that WLogit can identify almost all active biomarkers even in the cases where the biomarkers are highly correlated, while the other methods fail, which consequently leads to higher classification accuracy. The performance of WLogit is also evaluated on two publicly available datasets, and the obtained classifier outperformed other methods in terms of prediction accuracy. Our method is implemented in the WLogit R package available from the Comprehensive R Archive Network (CRAN).

关键词： Biomarkers Correlation Feature extraction Biological system modeling Bioinformatics Logistic regression Integrated circuit modeling Diseases Computational modeling Computational biology

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Considerations on trial design and data analysis for multi-regional clinical trials

Considerations on trial design and data analysis for multi-r...

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2017第七届临床评价方法与应用国际研讨会暨生物统计国际研讨会

作者： Hui Quan Biostatistics and Programming Department of Sanofi

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Vitamin D intake and cardiovascular mortality in the NHANES i epidemiological follow-up study cohort

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Journal of Dietary Supplements 2012年第2期9卷 79-89页

作者： Fan, Chunpeng Nieto, F. Javier Bautista, Leonelo E. Fine, Jason P. Department of Biostatistics and Programming Sanofi U.S. Inc. Bridgewater NJ 08807 55 Corporate Drive United States Department of Population Health Sciences University of Wisconsin Madison WI United States Department of Biostatistics University of North Carolina Chapel Hill NC United States

The objective of the present study was to determine whether vitamin D intake is associated with CVD mortality in a general population sample. The association between vitamin D intake and CVD mortality (ICD-9 code 410414) rates was investigated using data from the the First National Health and Nutrition Examination Survey (NHANES I) Epidemiological Follow-up Study (NHEFS) 19711992. Overall, higher vitamin D intake was associated with lower CVD mortality. After adjustment for traditional risk factors for CVD, vitamin D intake showed mild but nonstatistically significant protective effects against CVD mortality with a hazard ratio for adequate as compared to low intake (with 95 confidence intervals) of 0.90 (0.74, 1.08). Hazard ratios were 0.95, 0.83, 0.88, and 1.02, in males, females, Whites, and Blacks, respectively (with 95 confidence intervals overlapping 1.0 in all cases). Thus, we did not find a statistically significant association between vitamin D intake and CVD mortality, although our findings are compatible with a mild protective effect, especially among females and Whites. © 2012 Informa Healthcare USA, Inc.

关键词： Dietary supplements ICD-9 Ischemic heart disease NHEFS

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

arXiv

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arXiv 2020年

作者： Jin, Jin Riviere, Marie-Karelle Luo, Xiaodong Dong, Yingwen Department of Biostatistics Johns Hopkins Bloomberg School of Public Health BaltimoreMD United States Department of Biostatistics and Programming Research and Development Sanofi Chilly-Mazarin France Department of Biostatistics and Programming Sanofi BridgewaterNJ United States Department of Biostatistics and Programming Oncology Sanofi CambridgeMA United States

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 paper, 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. Copyright © 2020, The Authors. All rights reserved.

关键词： Diseases

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Sequential Monitoring of Conditional Randomization Tests: Generalized Biased Coin Designs

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Sequential Analysis 2008年第3期27卷 234-253页

作者： Yanqiong Zhang Yanqiong.Zhang@*** Rosenberger, William F. Department of Biostatistics and Programming Sanofi-Aventis Malvern Pennsylvania USA Department of Statistics George Mason University Fairfax Virginia USA

For the generalized biased coin class of randomization procedures, Smythe (1988) proved asymptotic normality of the conditional linear rank test. Clinical trialists often undertake interim analysis to determine whether to stop the trial early for a substantial treatment effect. In this article, we will set up one interim analysis using a conditional randomization test. The joint asymptotic distribution of the interim test statistic and the final test statistic will be explored. We also define the concept of conditional information under a randomization model. [ABSTRACT FROM AUTHOR]

关键词： Conditional information fraction Joint asymptotic normality Linear rank test Spending functions

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Dimension reduction with expectation of conditional difference

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Statistical Theory and Related Fields 2023年第3期7卷 188-201页

作者： Wenhui Sheng Qingcong Yuan Department of Mathematical and Statistical Sciences Marquette UniversityMilwaukeeWIUSA Biostatistics and Programming SanofiUSBridgewaterNJUSA

In this article,we introduce a flexible model-free approach to sufficient dimension reduction analysis using the expectation of conditional difference *** any strict conditions,such as linearity condition or constant covariance condition,the method estimates the central subspace exhaustively and efficiently under linear or nonlinear relationships between response and *** method is especially meaningful when the response is *** also studiedthe√n-consistency and asymptotic normality of the *** efficacy of our method is demonstrated through both simulations and a real data analysis.

关键词： Central subspace expectation of conditionaldifference measure sufficientdimension reduction

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Sequential monitoring of conditional randomization tests: Generalized biased coin designs

Sequential monitoring of conditional randomization tests: Ge...

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作者： Zhang, Yanqiong Rosenberger, William F. Department of Biostatistics and Programming Sanofi-Aventis Malvern PA United States Department of Statistics George Mason University Fairfax VA United States Sanofi-Aventis 9 Great Valley Parkway Malvern PA 19355 United States

关键词： Statistics

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A Seamless Phase II/III Design with Dose Optimization for Oncology Drug Development

arXiv

引用

arXiv 2024年

作者： Li, Yuhan Zhang, Yiding Mi, Gu Lin, Ji Department of Statistics University of Illinois Urbana-Champaign United States Biostatistics and Programming Sanofi France

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. © 2024, CC BY.

关键词： Oncology

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Change-Point Analysis of Survival Data with Application in Clinical Trials

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Open Journal of Statistics 2014年第9期4卷 663-677页

作者： Xuan Chen Michael Baron Department of Biostatistics and Programming Sanofi Beijing China Department of Mathematics and Statistics American University Washington DC USA Department of Mathematical Sciences University of Texas at Dallas Richardson USA

Effects of many medical procedures appear after a time lag, when a significant change occurs in subjects’ failure rate. This paper focuses on the detection and estimation of such changes which is important for the evaluation and comparison of treatments and prediction of their effects. Unlike the classical change-point model, measurements may still be identically distributed, and the change point is a parameter of their common survival function. Some of the classical change-point detection techniques can still be used but the results are different. Contrary to the classical model, the maximum likelihood estimator of a change point appears consistent, even in presence of nuisance parameters. However, a more efficient procedure can be derived from Kaplan-Meier estimation of the survival function followed by the least-squares estimation of the change point. Strong consistency of these estimation schemes is proved. The finite-sample properties are examined by a Monte Carlo study. Proposed methods are applied to a recent clinical trial of the treatment program for strong drug dependence.

关键词： Change-Point Problem Failure Rate Kaplan-Meier Estimation Least Squares Estimation Maximum Likelihood Estimation Strong Consistency Survival Function

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