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K-groups: A generalization of k-means clustering

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arXiv 2017年

作者： Li, Songzi Rizzo, Maria L. Biostatistics and Programming Department Pharmaceutical Product Deparment LLC WilmingtonNC28401 Dept. of Mathematics & Statistics Bowling Green State University Bowling GreenOH43403 United States

We propose a new class of distribution-based clustering algorithms, called k-groups, based on energy distance between samples. The energy distance clustering criterion assigns observations to clusters according to a multi-sample energy statistic that measures the distance between distributions. The energy distance determines a consistent test for equality of distributions, and it is based on a population distance that characterizes equality of distributions. The k-groups procedure therefore generalizes the k-means method, which separates clusters that have different means. We propose two k-groups algorithms: k-groups by first variation;and k-groups by second variation. The implementation of k-groups is partly based on Hartigan and Wong's algorithm for k-means. The algorithm is generalized from moving one point on each iteration (first variation) to moving m (m > 1) points. For univariate data, we prove that Hartigan and Wong's k-means algorithm is a special case of k-groups by first variation. The simulation results from univariate and multivariate cases show that our k-groups algorithms perform as well as Hartigan and Wong's k-means algorithm when clusters are well-separated and normally distributed. Moreover, both k-groups algorithms perform better than k-means when data does not have a finite first moment or data has strong skewness and heavy tails. For non-spherical clusters, both k-groups algorithms performed better than k-means in high dimension, and k-groups by first variation is consistent as dimension increases. In a case study on dermatology data with 34 features, both k-groups algorithms performed better than k-means. Copyright © 2017, The Authors. All rights reserved.

关键词： Cluster analysis

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

Ex utero intrapartum therapy in infants with congenital diaphragmatic hernia:a propensity score matching analysis

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World Journal of Pediatric Surgery 2022年第4期5卷 267-274页

作者： Yunlong Zhao Ying Wang Chao Liu Yulin Jiang Yandong Wei Hua Meng Shan Jian Xiting Zhu Lijian Pei Xiaochen Bai Feng Feng Yan Lv Xiya Zhou Qingwei Qi Jingna Li Lishuang Ma Peking Union Medical College Graduate School BeijingChina Department of Neonatal Surgery Children’s Hospital of Capital Institute of PediatricsCapital Institute of PediatricsBeijingChina Department of Obstetrics Peking Union Medical College HospitalBeijingChina Department of Ultrasound Peking Union Medical College HospitalBeijingChina Department of Pediatrics Peking Union Medical College HospitalBeijingChina Department of Biostatistics and Statistical Programming Everest Clinical Research CorporationBeijingChina Department of Anesthesiology Peking Union Medical College HospitalBeijingChina Department of Pediatrics Beijing Obstetrics and Gynecology HospitalCapital Medical UniversityBeijingChina Department of Radiology Peking Union Medical College HospitalBeijingChina

Objective Previous studies have shown that ex utero intrapartum therapy(EXIT)is safe and feasible for newborns with congenital diaphragmatic hernia(CDH).This study reports our experience with EXIT in fetuses with CDH in an attempt to explore the efficacy of EXIT on the survival rate of this *** A retrospective analysis of the clinical data of 116 children with CDH was *** children were assigned to EXIT and non-EXIT *** score matching(PSM)toward clinical data was performed,and the clinical characteristics and outcomes were *** survival at discharge as the main outcome,logistic regression analysis was carried out to explore the efficacy of EXIT on *** During the study period,30 of 116 children received *** PSM,the survival rates of the EXIT group and the non-EXIT group were 82.76%(24/29)and 48.28%(14/29),respectively(p=0.006).EXIT(OR=0.083,95%CI=0.013to 0.525,p=0.008),liver herniation(OR=16.955,95%CI=2.342 to 122.767,p=0.005),and gestational age at diagnosis(OR=0.662,95%CI=0.497 to 0.881,p=0.005)were independent mortality-related risk factors of all children with ***-nine of 116 children underwent *** PSM,the postoperative survival rates of the EXIT group and non-EXIT group were 84.6%(22/26)and 76.9%(20/26),respectively(p=0.754).Liver herniation(OR=10.451,95%CI=1.641 to 66.544,p=0.013)and gestational age at diagnosis(OR=0.736,95%CI=0.577 to 0.938,p=0.013)were independent mortality-related risk factors of children after *** EXIT can be performed safely for selected prenatally diagnosed CDH neonates with potentially better survival and does not cause more maternal complications compared with traditional cesarean section.

关键词： hernia diagnosis congenital

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

Conditional borrowing external data to establish a hybrid control arm in randomized clinical trials

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Journal of Biopharmaceutical Statistics 2022年第6期32卷 954-968页

作者： Hongfei Li Ram Tiwari Qian H. Li a Global Biometrics and Data Sciences Bristol Myers Squibb Princeton New Jersey USAb Department of Statistics University of Connecticut Storrs Connecticut USA a Global Biometrics and Data Sciences Bristol Myers Squibb Princeton New Jersey USA c Department of Biostatistics and Programming Statistics & Data Corporation Tempe Arizona USA

ABSTRACTABSTRACTUtilizing external data from the real world, including data from historical clinical trials, has received increasing interest in drug development. The use of external data to support drug evaluation in clinical trials has mainly been through using various matching methods for baseline characteristics to form external control arms in single-arm trials or to augment control arms of randomized controlled trials in hybrid approaches. However, matching the baseline characteristics between the trial and the external subjects can only guarantee comparability on the level of baseline characteristics. Differences in outcomes between the two data sources may still exist due to contemporaneous and operational characteristics. Similarity between the outcomes in the trial control and the external subjects with similar baseline characteristics can be critical in leveraging the external subjects in the clinical trials. In this paper, a resampling method for augmenting control arms in randomized controlled trials is proposed under the conditional borrowing framework. The new method establishes empirical distributions for the hazard ratio in outcomes between the external and trial control subjects. The borrowing decision is then derived from this empirical distribution using a measure of similarity. Once the borrowing decision is established, the borrowing weights for the external subjects, based on the similarity measure, are incorporated in the weighted partial likelihood to evaluate the treatment effect. The operating characteristics of the hybrid control arm, under both the conditional borrowing and unconditional borrowing frameworks, are evaluated. Simulation is conducted to evaluate Type I error, bias, and power. An illustrative example using simulated data is also presented.

关键词： Conditional borrowing hybrid control real-world data bias Type I error weighted partial likelihood

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

Modeling and forecasting of wheat of South Asian region countries and role in food security

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Advances in Computational Intelligence 2021年第6期1卷 1-8页

作者： Yonar, Aynur Yonar, Harun Mishra, Pradeep Kumari, Binita Abotaleb, Mostafa Badr, Amr Department of Statistics Faculty of Science Selçuk University Konya Turkey Department of Biostatistics Faculty of Veterinary Medicine Selçuk University Konya Turkey College of Agriculture J.N.K.V.V. Powarkheda India Department of Agricultural Economics Rashtriya Kisan (PG) College (affiliated to Chaudhary Charan Singh University Meerut) Shamli India Department of System Programming South Ural State University Chelyabinsk Russia Faculty of Science School of Science and Technology University of New England Armidale Australia

Wheat is the most important source of food on earth and vital for food safety. It contains 75–80% carbohydrates, 9–18% protein, fiber, many vitamins (especially B vitamins), calcium, iron, and many macronutrients and micronutrients. According to data from the International Grain Council (IGC), wheat has continued to be the most important food grain source for humans in the world. Therefore, determining wheat production behavior has a very important role in food security. In this study, we have modeled and forecasted the production of wheat for 6 years from 2020 to 2025 using ARIMA and Holt’s linear trend models in Afghanistan, Bangladesh, Bhutan, China, India, Nepal, and Pakistan, which are all countries in the South Asian region. Since there is an expectation of a decrease in wheat production in some of these countries, this study can provide these countries with the information they need to take appropriate decisions to prevent the occurrence of food problems in the future and to help deal with food security. Moreover, this projection helps with policy implications and planning.

关键词：

9O Phase I/II, open-label, first-in-human study of the anti-GPC3 T cell engager SAR444200 in patients with advanced solid tumors: Updated safety and pharmacokinetic analysis

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ESMO Open 2025年 10卷 104165-104165页

作者： Almhanna, K. Hong, J.Y. Chenard-Poirier, M. Ryoo, B-Y. Lim, D.W-T. El-Khoueiry, A.B. Goh, B-C. Samol, J. Kefsi, A. Perez, R. Meng, R. Masciari, S. Abbadessa, G. Guillemin-Paveau, H. Lepine, L. Zhang, Y. Dumbrava, E.E. Medical Oncology Warren Alpert Medical School of Brown University Providence United States of America Hematology-Oncology Samsung Medical Center (SMC) Seoul Republic of Korea Gastrointestinal Cancer Unit Centre Intégré de Cancérologie - Chu de Québec Québec Canada Department of Oncology Asan Medical Center - University of Ulsan Seoul Republic of Korea Medical Oncology Department NCCS - National Cancer Centre Singapore Medical Oncology Division USC - Kenneth J. Norris Jr. Comprehensive Cancer Center Los Angeles United States of America Hematology-Oncology NCIS - National University Cancer Institute Singapore Singapore Medical Oncology Department National Healthcare Group - Tan Tock Seng Hospital Singapore Oncology Early Development Sanofi Vitry-sur-Seine France Immuno Modulatory Agents-Early Development Sanofi Bridgewater United States of America Research and Development Department Sanofi Cambridge United States of America Early Development in Oncology Sanofi Cambridge United States of America Oncology Early Development Sanofi Cambridge United States of America Pharmacokinetics/Pharmacodynamics VA Sanofi Vitry-sur-Seine France Pharmacokinetics Dynamics and Metabolism Sanofi Vitry-sur-Seine France Department of Biostatistics and Programming Sanofi Cambridge United States of America Investigational Cancer Therapeutics Department The University of Texas MD Anderson Cancer Center - Main Building Houston United States of America

A Classification of Elements of Function Space F(,)

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arXiv 2023年

作者： Soltanifar, Mohsen Analytics Division College of Professional Studies Northeastern University 1400-410 West Georgia Street VancouverBCV6B 1Z3 Canada Biostatistics Division Dalla Lana School of Public Health University of Toronto 620-155 College Street TorontoONM5T 3M7 Canada Biostatistics & Programming Division Biometrics Department ClinChoice Inc 750-2 Robert Speck Parkway MississaugaONL4Z 1H8 Canada

In this paper, we classify the function space of all real-valued functions on R denoted as F (, ) into 28 distinct blocks. Each block contains elements that share common features in terms of the cardinality of their sets of continuity and differentiability. Alongside this classification, we introduce the concept of the Connection, which reveals a special relationship structure between four well-known real-valued functions in real analysis: the Cantor function, Dirichlet function, the Thomae function, and the Weierstrass function. Despite the significance of this field, several perspectives remain *** Codes 05A18, 26A15, 26A21, 26A24, 26A30, 03E10 © 2023, CC BY.

关键词： Functional analysis