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Dimension reduction with expectation of conditional difference

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

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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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High-dimensional Cell Image Data Analysis in Drug Discovery

High-dimensional Cell Image Data Analysis in Drug Discovery

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The Third IMS-China International Conference on Statistics and Probability(第三届国际数理统计学会中国分会年会)

作者： Donghui Zhang Xianhong Xie Sanofi Aventis Global Biostatistics and Programming 200 Crossing Blvd Bridgewater NJ 08807 USA

In high-content screening (HCS) assay, the cell images in each well of multi-well plates are captured and multiple cell features (i.e., cell counts, size, shape, intensity,texture, overlap of colors) are extracted from *** high-dimensional features/parameters provide more representatives of biological models that allow phenotype definition and pathway analysis for both compound and target screenings,and for analysis of mechanisms of actions,

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

arXiv

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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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Misunderstanding of a New Approach to Drug-Placebo Difference Calculation in Short Term Antidepressant-Drug Trials

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Open Journal of Statistics 2015年第2期5卷 113-119页

作者： Yanning Liu Quantitative Science China Janssen Research & Development Shanghai China Biostatistics and Programming Janssen Research & Development LLC Titusville NJ USA

In clinical trials, drug effect is measured by a difference between subjects who are treated by experimental drug against placebo-treated subjects. In case of binary data, with observing YES/NO on each subject in certain period of time, it is the proportion of subjects who respond in treatment group minus the proportion of responders in placebo group (for example, 50% vs. 30%). However, a greater difference was proposed by Rihmer et al. (2011) [1] with their supporting arguments, in that antidepressant response and placebo response had different mechanisms and there were equal chances for antidepressant responder to be responding to placebo and not responding to placebo at all. Therefore, the authors proposed 50% - 30% * 50% when the response rate in the treatment group and the placebo group are 50% and 30% respectively, resulting in higher drug-placebo difference than traditional understanding of 50% - 30%. In this article, we tried to explain why the authors misunderstood the drug-placebo concept for evaluating drug superiority, their misunderstanding of assumptions of traditional calculation, as well as their wrong reasoning on their proposed approach. All in all, we conclude the traditional approach of 50% - 30% is the right way of evaluating drug-placebo difference and the possible methods to control impact of placebo effect are briefly discussed at the end of this article.

关键词： Antidepressant Placebo Effect Short-Term Antidepressant Effect Unipolar Major Depression

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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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What was the cause of Franklin Delano Roosevelt's paralytic illness?

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Journal of Medical Biography 2003年第4期11卷 232-240页

作者： Goldman, Armond S Schmalstieg, Elisabeth J Freeman, Daniel H Goldman, Daniel A Schmalstieg, Frank C Texas University of Texas Medical Branch in Galveston United States University of Texas Medical Branch in Galveston Texas United States Office of Biostatistics University of Texas Medical Branch Galveston Texas United States President of Expert Health Data Programming Inc.

In 1921, when he was 39 years of age, Franklin Delano Roosevelt contracted an illness characterized by: fever;protracted symmetric, ascending paralysis;facial paralysis;bladder and bowel dysfunction;numbness;and dysaesthesia. The symptoms gradually resolved except for paralysis of the lower extremities. The diagnosis at the onset of the illness and thereafter was paralytic poliomyelitis. Yet his age and many features of the illness are more consistent with a diagnosis of Guillain-Barré syndrome, an autoimmune polyneuritis. The likelihoods (posterior probabilities) of poliomyelitis and Guillain-Barré syndrome were investigated by Bayesian analysis. Posterior probabilities were calculated by multiplying the prior probability (disease incidence in Roosevelt's age group) by the symptom probability (likelihood of a symptom occurring in a disease). Six of eight posterior probabilities strongly favoured Guillain-Barré syndrome. © 2003, SAGE Publications. All rights reserved.

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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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Performance evaluation of practice courses using fuzzy cognitive maps

Performance evaluation of practice courses using fuzzy cogni...

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International Conference on Information Technology Based Higher Education and Training, ITHET

作者： Ahmet Albayrak Muammer Albayrak Department of Computer Programming Karadeniz Technical University Trabzon Turkey Department of Biostatistics and Medical Informatics Karadeniz Technical University Trabzon Turkey

ISBN: (纸本)9781509007790

In this study, the effect of practical courses (especially robotics) on students' success is evaluated using fuzzy cognitive maps. This study particularly focuses on how practical courses increase the learning achievements in vocational schools. For this purpose, students and academicians were chosen as stakeholders for this study. Student were selected from departments of machine, electronic and computer. In total views of 30 students were taken and evaluated using fuzzy cognitive maps before the study. Students were studied on three projects within this study. These projects are autonomous cleaning robot, chameleon robot and sumo wrestler robot. Each robot was developed by a group consisting of ten students. Three main outputs determined to be gained by students in the robot development process as sensor calibration, improvement of mechanical systems and software optimization. These outputs are also among the output concepts of the fuzzy cognitive map. Sensor calibration, improvement of the mechanical systems and optimization of the software are the critical tasks respectively for the student groups of electronics, machine and computer. Each student group shared students of their departments homogeneously. Academicians were selected as one from each department. Fuzzy cognitive maps' survey applied to 15 academicians. After the design and development processes of robots, students and academicians views re-evaluated. It was observed clearly from the comparison of the results that, learning achievement, desire to learn, desire to interdisciplinary collaboration, self-confidence, interest in robotic technologies, satisfaction and desire to give priority to practical training in other subjects are increased by %45, %63, %42, %74, %85, %88 and %76 respectively.

关键词： Mathematical model Robot sensing systems Fuzzy cognitive maps Training Stakeholders

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On some "disadvantages" of the population approach

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AAPS Journal 2005年第2期7卷 E374-E382页

作者： Nedelman, Jerry R. Biostatistics and Statistical Programming Novartis Pharmaceuticals East Hanover NJ 07936 One Health Plaza United States

In a seminal article on population pharmacokinetic modeling, researchers demonstrated how means and variances of pharmacokinetic parameters for a patient population could be inferred from sparse data collected under conditions of routine patient care. But they also identified 4 potential concerns about their methodology: unobserved confounding variables may bias the inferences;conditions under which data are collected may lead to inaccuracies of reporting or recording;correlations among important predictor variables may reduce statistical efficiency;and costs cannot be controlled by principles of study design. Experiences are reviewed that relate to these potential disadvantages. A method is presented for diagnosing the possible presence of confounding. A model is constructed and applied that captures the influences of data inaccuracies. An example of selecting from among correlated covariates is summarized. Finally, a methodology for optimal study design is reviewed and applied to an example. Copyright ©2003. All Rights Reserved.

关键词： Confounding Errors in variables Noncompliance Optimal design Variable selection

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