检索结果-内蒙古大学图书馆

丛书名： Use R!

1000年

作者： Dan Lin Ziv Shkedy Daniel Yekutieli Dhammika Amaratunga Luc Bijnens

Robust Model Selection and Estimation for Censored Survival Data with High Dimensional Genomic Covariates

学校读者我要写书评

暂无评论

Acta biotheoretica 2019年第3期67卷 225-251页

作者： Guorong Chen Sijian Wang Guannan Sun Huanxue Pan Department of Finance Beijing Forestry University Beijing China. Department of Statistics and Biostatistics Rutgers University New Brunswick NJ USA. Department of Biostatistics and Programming Sanofi China Beijing China. Department of Finance Beijing Forestry University Beijing China. hxpbjfu@***.

When relating genomic data to survival outcomes, there are three main challenges that are the censored survival outcomes, the high-dimensionality of the genomic data, and the non-normality of data. We propose a method to tackle these challenges simultaneously and obtain a robust estimation of detecting significant genes related to survival outcomes based on Accelerated Failure Time (AFT) model. Specifically, we include a general loss function to the AFT model, adopt model regularization and shrinkage technique, cope with parameters tuning and model selection, and develop an algorithm based on unified Expectation-Maximization approach for easy implementation. Simulation results demonstrate the advantages of the proposed method compared with existing methods when the data has heavy-tailed errors and correlated covariates. Two real case studies on patients are provided to illustrate the application of the proposed method.

关键词： AFT model General loss function Regularized robust model Survival data

Ranitidine (Zantac®) syrup versus ranitidine effervescent tablets (Zantac® EFFERdose®) in children: A single-center taste preference study

学校读者我要写书评

暂无评论

Pediatric Drugs 2006年第4期8卷 265-270页

作者： Ameen, Vanessa Z. Pobiner, Bonnie F. Giguere, Gregory C. Carter, Eric G. Clinical Pharmacology and Discovery Medicine Research and Development GlaxoSmithKline Research Triangle Park NC United States Gastroenterology Medicine Development Center GlaxoSmithKline Research Triangle Park NC United States Department of Biostatistics and Programming GlaxoSmithKline Research Triangle Park NC United States GlaxoSmithKline Research Triangle Park NC 27709 5 Moore Drive United States

Background: The histamine H2 receptor antagonist ranitidine is US FDA-approved for the treatment of gastroesophageal reflux disease and healing of erosive esophagitis in children ≥1 month of age. A low-dose strength ...

Background: The histamine H₂ receptor antagonist ranitidine is US FDA-approved for the treatment of gastroesophageal reflux disease and healing of erosive esophagitis in children ≥1 month of age. A low-dose strength of ranitidine is now available in a citrus-flavored 25mg effervescent tablet (dissolved in 5mL of water);this formulation was developed to facilitate use in infants and smaller children. Ranitidine syrup is available in a peppermint-flavored 15 mg/mL formulation. Objective: To compare taste preferences for ranitidine (Zantac®) syrup and ranitidine effervescent tablets dissolved in water (Zantac® EFFERdose®) in healthy children aged 4-8 years and their adult caregivers. Study Design and Methods: A randomized, single-blind, crossover, taste test trial was conducted in 102 children and 102 parents/legal guardians. All subjects received a single 45mg dose of each formulation. After tasting both preparations children were asked: "Now that you have tasted both medicines, which one of these medicines do you think tastes better?" Adults were asked four questions to assess whether they would administer the medication to the children. Results: Seventy-one percent (72/102) of the children preferred the taste of the ranitidine effervescent tablets compared with 29% (30/102) who preferred the syrup (p < 0.001). The majority of adults (71%) responded that they would prefer to administer the effervescent formulation based on taste. Adverse events consistent with product labeling were mild and were reported in four children and three adults: headache (n = 3), drowsiness (n = 1), abdominal pain/cramps (n = 2), and bloating/gas (n = 1). Conclusion: The taste of the ranitidine effervescent formulation dissolved in water is preferred over the ranitidine syrup. Better taste acceptance may facilitate ease of administration and compliance in pediatric patients. © 2006 Adis Data Information BV. All rights reserved.

关键词：

A Simulation-free Group Sequential Design with Max-combo Tests in the Presence of Non-proportional Hazards

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Wang, Lili Luo, Xiaodong Zheng, Cheng Department of Biotatistics University of Michigan Ann ArborMI United States Department of Biostatistics and Programming Research and Development Sanofi Us BridgewaterNJ United States

Non-proportional hazards (NPH) have been observed in many immuno-oncology clinical trials. Weighted log-rank tests (WLRT) with suitable weights can be used to improve the power of detecting the difference between survival curves in the presence of NPH. However, it is not easy to choose a proper WLRT in practice. A versatile maxcombo test was proposed to achieve the balance of robustness and efficiency, and has received increasing attention recently. Survival trials often warrant interim analyses due to their high cost and long durations. The integration and implementation of maxcombo tests in interim analyses often require extensive simulation studies. In this report, we propose a simulation-free approach for group sequential designs with the maxcombo test in survival trials. The simulation results support that the proposed method can successfully control the type I error rate and offer excellent accuracy and flexibility in estimating sample sizes, with light computation burden. Notably, our method displays strong robustness towards various model misspecifications and has been implemented in an R package for free access online. Copyright © 2019, The Authors. All rights reserved.

关键词： Hazards

A Family-based graphical approach for testing hierarchically ordered families of hypotheses

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Qiu, Zhiying Yu, Li Guo, Wenge Biostatistics and Programming Sanofi BridgewaterNJ08807 United States Department of Mathematical Sciences New Jersey Institute of Technology NewarkNJ07102 United States

In applications of clinical trials, tested hypotheses are often grouped as multiple hierarchically ordered families. To test such structured hypotheses, various gatekeeping strategies have been developed in the literature, such as series gatekeeping, parallel gatekeeping, tree-structured gatekeeping strategies, etc. However, these gatekeeping strategies are often either non-intuitive or less flexible when addressing increasingly complex logical relationships among families of hypotheses. In order to overcome the issue, in this paper, we develop a new family-based graphical approach, which can easily derive and visualize different gatekeeping strategies. In the proposed approach, a directed and weighted graph is used to represent the generated gatekeeping strategy where each node corresponds to a family of hypotheses and two simple updating rules are used for updating the critical value of each family and the transition coefficient between any two families. Theoretically, we show that the proposed graphical approach strongly controls the overall familywise error rate at a pre-specified level. Through some case studies and a real clinical example, we demonstrate simplicity and flexibility of the proposed *** Codes 62J15 Copyright © 2018, The Authors. All rights reserved.

关键词： Directed graphs

On multi-armed bandit designs for dose-finding clinical trials

学校读者我要写书评

暂无评论

The Journal of Machine Learning Research

The Journal of Machine Learning Research 2021年第1期22卷 686-723页

作者： Maryam Aziz Emilie Kaufmann Marie-Karelle Riviere Spotify NYC Univ. Lille CNRS Inria Centrale Lille UMR 9189 CRIStAL Lille France Statistical Methodology Group Biostatistics and Programming department Sanofi R&D Chilly-Mazarin France

We study the problem of finding the optimal dosage in early stage clinical trials through the multiarmed bandit lens. We advocate the use of the Thompson Sampling principle, a flexible algorithm that can accommodate different types of monotonicity assumptions on the toxicity and efficacy of the doses. For the simplest version of Thompson Sampling, based on a uniform prior distribution for each dose, we provide finite-time upper bounds on the number of sub-optimal dose selections, which is unprecedented for dose-finding algorithms. Through a large simulation study, we then show that variants of Thompson Sampling based on more sophisticated prior distributions outperform state-of-the-art dose identification algorithms in different types of dose-finding studies that occur in phase I or phase I/II trials.

关键词： multi-armed bandits adaptive clinical trials phase I clinical trials phase I/II clinical trials Thompson sampling Bayesian methods

Imbalanced randomization in clinical trials

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Chandereng, Thevaa Wei, Xiaodan Chappell, Rick Department of Statistics University of Wisconsin-Madison WI United States Department of Biostatistics & Medical Informatics University of Wisconsin-Madison WI United States Morgridge Institute of Research WI United States Biostatistics and Programming Sanofi BridgewaterNJ United States

Randomization is a common technique used in clinical trials to eliminate potential bias and confounders in a patient population. Equal allocation to treatment groups is the standard due to its optimal efficiency in many cases. However, in certain scenarios, unequal allocation can improve efficiency. In superiority trials with more than two groups, the optimal randomization is not always a balanced randomization. In non-inferiority trials, additive margin with equal variance is the only instance with balanced randomization. Optimal randomization for non-inferiority trials can be far from 1:1 and can greatly improve efficiency, a fact which is commonly overlooked. A tool for sample size calculation for non-inferiority trials with additive or multiplicative margin with normal, binomial or Poisson distribution is available at http://***/shiny/SSNI/. Copyright © 2018, The Authors. All rights reserved.

关键词： Efficiency

A Multi-Arm Two-Stage (MATS) Design for Proof-of-Concept and Dose Optimization in Early-Phase Oncology Trials

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Jiang, Zhenghao Mi, Gu Lin, Ji Lorenzato, Christelle Ji, Yuan Department of Statistics University of Chicago 5747 South Ellis Avenue ChicagoIL60637 United States Biostatistics and Programming Sanofi 450 Water Street CambridgeMA02141 United States Biostatistics and Programming Sanofi R&D Vitry-sur-Seine France Department of Public Health Science University of Chicago 5841 South Maryland Avenue ChicagoIL60637 United States

The Project Optimus initiative by the FDA's Oncology Center of Excellence is widely viewed as a groundbreaking effort to change the status quo of conventional dose-finding strategies in oncology. Unlike in other therapeutic areas where multiple doses are evaluated thoroughly in dose ranging studies, early-phase oncology dose-finding studies are characterized by the practice of identifying a single dose, such as the maximum tolerated dose (MTD) or the recommended phase 2 dose (RP2D). Following the spirit of Project Optimus, we propose an Multi-Arm Two-Stage (MATS) design for proof-of-concept (PoC) and dose optimization that allows the evaluation of two selected doses from a dose-escalation trial. The design assess the higher dose first across multiple indications in the first stage, and adaptively enters the second stage for an indication if the higher dose exhibits promising anti-tumor activities. In the second stage, a randomized comparison between the higher and lower doses is conducted to achieve proof-of-concept (PoC) and dose optimization. A Bayesian hierarchical model governs the statistical inference and decision making by borrowing information across doses, indications, and stages. Our simulation studies show that the proposed MATS design yield desirable performance. An R Shiny application has been developed and made available at https://***/mats/. Copyright © 2023, The Authors. All rights reserved.

关键词： Oncology

A VARIABLE SELECTION APPROACH for HIGHLY CORRELATED PREDICTORS in HIGH-DIMENSIONAL GENOMIC DATA

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Wencan, Z.H.U. Lévy-Leduc, Céline Ternès, Nils UMR MIA-Paris AgroParisTech INRAE Université Paris-Saclay Paris75005 France Biostatistics and Programming department Sanofi R&D Chilly Mazarin91380 France

In genomic studies, identifying biomarkers associated with a variable of interest is a major concern in biomedical research. Regularized approaches are classically used to perform variable selection in high-dimensional linear models. However, these methods can fail in highly correlated settings. We propose a novel variable selection approach called WLasso, taking these correlations into account. It consists in rewriting the initial high-dimensional linear model to remove the correlation between the biomarkers (predictors) and in applying the generalized Lasso criterion. The performance of WLasso is assessed using synthetic data in several scenarios and compared with recent alternative approaches. The results show that when the biomarkers are highly correlated, WLasso outperforms the other approaches in sparse high-dimensional frameworks. The method is also successfully illustrated on publicly available gene expression data in breast cancer. Our method is implemented in the WLasso R package which is available from the Comprehensive R Archive Network. Copyright © 2020, The Authors. All rights reserved.

关键词： Biomarkers

IDENTIFICATION OF PROGNOSTIC AND PREDICTIVE BIOMARKERS IN HIGH-DIMENSIONAL DATA WITH PPLASSO

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Zhu, Wencan Lévy-Leduc, Céline Ternès, Nils UMR MIA-Paris AgroParisTech INRAE Université Paris-Saclay Paris75005 France Biostatistics and Programming department Sanofi R&D Chilly Mazarin 91380 France

In clinical development, identification of prognostic and predictive biomarkers is essential to precision medicine. Prognostic biomarkers can be useful for anticipating the prognosis of individual patients, and predictive biomarkers can be used to identify patients more likely to benefit from a given treatment. Previous researches were mainly focused on clinical characteristics, and the use of genomic data in such an area is hardly studied. A new method is required to simultaneously select prognostic and predictive biomarkers in high dimensional genomic data where biomarkers are highly correlated. We propose a novel approach called PPLasso (Prognostic Predictive Lasso) integrating prognostic and predictive effects into one statistical model. PPLasso also takes into account the correlations between biomarkers that can alter the biomarker selection accuracy. Our method consists in transforming the design matrix to remove the correlations between the biomarkers before applying the generalized Lasso. In a comprehensive numerical evaluation, we show that PPLasso outperforms the Lasso type approaches on both prognostic and predictive biomarker identification in various scenarios. Finally, our method is applied to publicly available transcriptomic data from clinical trial RV144. Our method is implemented in the PPLasso R package which is available from the Comprehensive R Archive Network (CRAN). Copyright © 2022, The Authors. All rights reserved.

关键词： Biomarkers