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

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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

Bayesian dose-regimen assessment in early phase oncology incorporating pharmacokinetics and pharmacodynamics

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arXiv 2020年

作者： Gerard, Emma Zohar, Sarah Thai, Hoai-Thu Lorenzato, Christelle Riviere, Marie-Karelle Ursino, Moreno Centre de Recherche des Cordeliers Sorbonne Université Inserm Université de Paris ParisF-75006 France Oncology biostatistics Biostatistics and Programming department Sanofi R&D Vitry-sur-Seine France Statistical Methodology Group Biostatistics and Programming department Sanofi R&D Chilly-Mazarin France Translation Disease Modeling Digital and Data Science Sanofi R&D Chilly-Mazarin France F-CRIN PARTNERS platform AP-HP Université de Paris Paris France

Phase I dose-finding trials in oncology seek to find the maximum tolerated dose (MTD) of a drug under a specific schedule. Evaluating drug-schedules aims at improving treatment safety while maintaining efficacy. However, while we can reasonably assume that toxicity increases with the dose for cytotoxic drugs, the relationship between toxicity and multiple schedules remains elusive. We proposed a Bayesian dose-regimen assessment method (DRtox) using pharmacokinetics/pharmacodynamics (PK/PD) information to estimate the maximum tolerated dose-regimen (MTD-regimen), at the end of the dose-escalation stage of a trial to be recommended for the next phase. We modeled the binary toxicity via a PD endpoint and estimated the dose-regimen toxicity relationship through the integration of a dose-regimen PD model and a PD toxicity model. For the dose-regimen PD model, we considered nonlinear mixed-effects models, and for the PD toxicity model, we proposed the following two Bayesian approaches: a logistic model and a hierarchical model. We evaluated the operating characteristics of the DRtox through simulation studies under various scenarios. The results showed that our method outperforms traditional model-based designs demonstrating a higher percentage of correctly selecting the MTD-regimen. Moreover, the inclusion of PK/PD information in the DRtox helped provide more precise estimates for the entire dose-regimen toxicity curve;therefore the DRtox may recommend alternative untested regimens for expansion cohorts. The DRtox should be applied at the end of the dose-escalation stage of an ongoing trial for patients with relapsed or refractory acute myeloid leukemia (NCT03594955) once all toxicity and PK/PD data are collected. © 2020, CC-BY.

关键词： Toxicity

Prenatal nitrate exposure from diet and drinking water and timing of puberty in sons and daughters: A nationwide cohort study

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Environment International 2022年 170卷 107659页

作者： Jul Clemmensen, Pernille Brix, Nis Schullehner, Jörg Lunddorf, Lea Lykke Harrits Ernst, Andreas Ebdrup, Ninna Hinchely Bjerregaard, Anne Ahrendt Hansen, Birgitte Thomas Stayner, Leslie Ingi Halldorsson, Thorhallur Frodi Olsen, Sjurdur Sigsgaard, Torben Kolstad, Henrik Albert Ramlau-Hansen, Cecilia Høst Department of Public Health Aarhus University Aarhus Denmark Department of Clinical Genetics Aarhus University Hospital Aarhus Denmark Geological Survey of Denmark and Greenland Aarhus Denmark Aarhus University Aarhus Denmark Fertility Clinic Horsens Regional Hospital Horsens Denmark Department of Epidemiology Research Center for Fetal Programming Statens Serum Institut Copenhagen Denmark Centre for Clinical Research and Prevention Bispebjerg and Frederiksberg Hospital Copenhagen Denmark Division of Epidemiology and Biostatistics University of Illinois at Chicago School of Public Health Chicago United States Faculty of Food Science and Nutrition School of Health Sciences University of Iceland Iceland Section of Epidemiology Institute of Public Health University of Copenhagen Denmark Cirrau -Centre for Integrated Register-based Research at Aarhus University Aarhus Denmark Department of Occupational Medicine Aarhus University Hospital Aarhus Denmark Department of Clinical Medicine Aarhus University Aarhus Denmark

Background: In Western countries, age at pubertal development has declined during the last century in girls, and probably also in boys. No studies have investigated whether nitrate, a widespread environmental exposure with teratogenic and hormone disrupting properties, might affect timing of puberty. Objectives: We investigated if prenatal exposure to nitrate from drinking water and diet was associated with timing of puberty. Methods: This cohort study included 15,819 children born from 2000 to 2003 within the Danish National Birth Cohort. Self-reported information on current status of various pubertal milestones was provided every six months by a questionnaire from 11 years of age until 18 years or full maturity, whichever came first. Maternal nitrate intake from diet (mg/day) was derived from a mid-pregnancy food frequency questionnaire and individual level nitrate exposure from drinking water (mg/L) was derived using measurements from Danish public waterworks. Adjusted average differences in months in age at attaining several pubertal milestones as well as the average age difference in age at attaining all the milestones were estimated separately for diet and water using a regression model for interval-censored data. C- and E-vitamin, red meat and processed meat intake were explored as potential effect modifiers in sub-analyses. Results: No strong associations were observed between prenatal exposure to nitrate and timing of puberty in children. However, sons born of mothers with a nitrate concentration in drinking water at their residential address of > 25 mg/L (half of the World Health Organisation (WHO) guideline value) compared with ≤ 1 mg/L showed a tendency towards earlier age at pubertal development with an average age difference of −1.2 months (95 % confidence interval,−3.0;0.6) for all the pubertal milestones combined. Discussion: Studies including more highly exposed children are needed before the current WHO drinking water guideline value for nitrate ca

关键词： Potable water

Implementation of Tripartite Estimands Using Adherence Causal Estimators Under the Causal Inference Framework

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arXiv 2020年

作者： Qu, Yongming Luo, Junxiang Ruberg, Stephen J. Department of Biometrics Eli Lilly and Company IndianapolisIN46285 United States Biostatistics and Programming Sanofi 55 Corporate Dr BridgewaterNJ08807 United States Analytix Thinking LCC 11121 Bentgrass Court IndianapolisIN46236 United States

Intercurrent events (ICEs) and missing values are inevitable in clinical trials of any size and duration, making it difficult to assess the treatment effect for all patients in randomized clinical trials. Defining the appropriate estimand that is relevant to the clinical research question is the first step in analyzing data. The tripartite estimands, which evaluate the treatment differences in the proportion of patients with ICEs due to adverse events, the proportion of patients with ICEs due to lack of efficacy, and the primary efficacy outcome for those who can adhere to study treatment under the causal inference framework, are of interest to many stakeholders in understanding the totality of treatment effects. In this manuscript, we discuss the details of how to estimate tripartite estimands based on a causal inference framework and how to interpret tripartite estimates through a phase 3 clinical study evaluating a basal insulin treatment for patients with type 1 diabetes. Copyright © 2020, The Authors. All rights reserved.

关键词： Clinical research

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

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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

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

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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

7 Phase 3 Trial of an Oral CXCR4 Antagonist, Mavorixafor, for Treatment of Patients With WHIM Syndrome: Preliminary Results From Ongoing Open-Label Extension Period of Continuous Mavorixafor Treatment

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Clinical Immunology 2024年 262卷 109949-109949页

作者： Tarrant, Teresa Badolato, Raffaele Donadieu, Jean Dubuc, Susan Hu, Yanping Jiang, Honghua Li, Sunny Steiner, Deborah Yan, Tina Arbet-Engels, Christophe Associate Professor of Medicine Rheumatology and Immunology/Division of Rheumatology and Immunology Department of Medicine Duke University Durham NC USA Full Professor of Pediatrics Chair of Pediatrics ASST Spedali Civili Brescia/Department of Clinical and Experimental Sciences University of Brescia & ASST Spedali Civili Brescia Italy Pediatric Physician/CHU Paris Est - Hôpital d’Enfants Armand-Trousseau Paris France Senior Director Pharmacovigilance & Risk Management/X4 Pharmaceuticals Boston MA USA Director Clinical Research/X4 Pharmaceuticals Boston MA USA Senior Director Biostatistics/X4 Pharmaceuticals Boston MA USA Senior Director Statistical Programming/X4 Pharmaceuticals Boston MA USA Vice President Clinical Development/X4 Pharmaceuticals Boston MA USA Director Statistical Programming/X4 Pharmaceuticals Boston MA USA Chief Medical Officer/X4 Pharmaceuticals Boston MA USA

Estimation of group means in generalized linear mixed models

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arXiv 2019年

作者： Duan, Jiexin Levine, Michael Luo, Junxiang Qu, Yongming Department of Statistics Purdue University West LafayetteIN47906 United States Biostatistics and programming 55 Corporate Drive Bridgewater SanofiNJ08807 United States Department of Biometrics Eli Lilly Corporate Center Drop Code 2232 IndianapolisIN United States

In this manuscript, we investigate the concept of the mean response for a treatment group mean as well as its estimation and prediction for generalized linear models with a subject-wise random effect. Generalized linear models are commonly used to analyze categorical data. The model-based mean for a treatment group usually estimates the response at the mean covariate. However, the mean response for the treatment group for studied population is at least equally important in the context of clinical trials. New methods were proposed to estimate such a mean response in generalized linear models;however, this has only been done when there are no random effects in the model. We suggest that, in a generalized linear mixed model (GLMM), there are at least two possible definitions of a treatment group mean response that can serve as estimation/prediction targets. The estimation of these treatment group means is important for healthcare professionals to be able to understand the absolute benefit versus risk. For both of these treatment group means, we propose a new set of methods that suggests how to estimate/predict both of them in a GLMM models with a univariate subject-wise random effect. Our methods also suggest an easy way of constructing corresponding confidence and prediction intervals for both possible treatment group means. Simulations show that proposed confidence and prediction intervals provide correct empirical coverage probability under most circumstances. Proposed methods have also been applied to analyze hypoglycemia data from diabetes clinical trials. Copyright © 2019, The Authors. All rights reserved.

关键词： Random processes

VP6-2022: Adjuvant pertuzumab and trastuzumab in patients with early HER-2 positive breast cancer in APHINITY: 8.4 years' follow-up

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Annals of Oncology 2022年第9期33卷 986-987页

作者： S. Loibl J. Jassem A. Sonnenblick D. Parlier E. Winer J. Bergh R.D. Gelber E. Restuccia Y-H. Im C. Huang F. Dalenc I. Calvo M. Procter C. Caballero E. Clark H.L. Gomez Moreno J. Bliss G. Viale J. Bines M. Piccart Medicine and Research Dept. German Breast Group (GBG) Forschungs GmbH Neu-Isenburg Germany Oncology & Radiotherapy Medical University of Gdansk Gdansk Poland Oncology Department Tel Aviv Sourasky Medical Center-(Ichilov) Tel Aviv Israel Clinical Trials Support Unit Institut Jules Bordet Brussels Belgium Yale Cancer Center Yale University School of Medicine - Yale Cancer Center New Haven CT USA Department of Oncology-Pathology Karolinska Institutet and Breast Cancer Centre Cancer Theme Karolinska University Hospital Stockholm Sweden Biostatistics Dana Farber Cancer Institute Boston MA USA Product Development Oncology F. Hoffmann-La Roche Ltd Basel Switzerland Medicine Dept. Hematology/Medical Oncology Division Samsung Medical Center (SMC) - Sungkyunkwan University School of Medicine Seoul Republic of Korea Surgery Dept. NTUH - National Taiwan University Hospital Taipei Taiwan Oncologie 1A Institut Universitaire du Cancer Toulouse - Oncopole Toulouse France Breast Cancer Unit MD Anderson Cancer Center Madrid Madrid Spain Statistics and Programming Frontier Science Scotland Kincraig UK Medical Department Breast International Group (BIG) - AISBL Brussels Belgium Data Sciences - Data & Statistical Sciences (DSS) Roche Products Limited Welwyn Garden City UK Medicine Department INEN - Instituto Nacional de Enfermedades Neoplasicas Lima Peru Clinical Trials and Statistics Unit ICR - Institute of Cancer Research London UK Department of Oncology Università degli Studi di Milano Milan Italy Medical Oncology Department Instituto Nacional de Cancer - Research Center Rio de Janeiro Brazil Institut Jules Bordet Brussels Belgium