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Statistical Methods for Analyzing EQ-5D in Randomized Clinical Trials: A Systematic Literature Review

Value in Health 2025年

作者： Yan, Jiajun Humphries, Brittany Xie, Ruinan Yin, Ziran Bo, Zhenyan Diao, Sha Cai, Jing Tse, Preston Li, Meixuan Pullenayegum, Eleanor Lee, Shun Fu Xie, Feng Department of Health Research Methods Evidence and Impact McMaster University Hamilton ON Canada Department of Applied Mathematics Hong Kong Polytechnic University Hong Kong China Department of Biostatistics and Bioinformatics School of Medicine Duke University Chapel Hill NC United States Department of Pharmacy/Evidence-based Pharmacy Center West China Second University Hospital Sichuan University Sichuan China Department of Mathematics and Statistics McMaster University Hamilton ON Canada Evidence-Based Medicine Center School of Basic Medical Sciences Lanzhou University Lanzhou China Health Technology Assessment Centre Evidence-Based Social Science Research Center School of Public Health Lanzhou University Lanzhou China Child Health Evaluative Sciences Hospital for Sick Children Toronto ON Canada Dalla Lana School of Public Health University of Toronto Toronto ON Canada Population Health Research Institute Hamilton Health Sciences Hamilton ON Canada Centre for Health Economics and Policy Analysis McMaster University Hamilton ON Canada

Objectives: We conducted a systematic literature review to summarize the application of statistical methods for analyzing treatment effect on EQ-5D in randomized clinical trials (RCTs). Method: We searched 2 electronic databases (MEDLINE and EMBASE, from inception through 2021) and ***. Eligible studies were RCTs that analyzed postbaseline EQ-5D data by treatment group. Information on trial characteristics, EQ-5D data characteristics, and statistical methods were extracted. Descriptive statistics were used to summarize results by dimension response, EQ visual analog scale (EQ VAS), and EQ-5D utility. Results: A total of 2125 trials met the eligibility criteria. EQ-5D was commonly considered a secondary (n = 1219, 57.4%) or exploratory (n = 775, 36.5%) endpoint in RCTs. EQ-5D utilities were the most analyzed. Both utilities and EQ VAS were primarily analyzed in numerical format. The most common statistical models for analyzing utilities were the linear fixed-effect model for single postbaseline (192/589, 32.6%) and the linear mixed-effect model for multiple post-baselines (338/984, 34.3%). Of the 2054 studies that analyzed numerical EQ-5D, 221 (10.8%) examined model assumptions and 438 (21.3%) adjusted for the baseline score. Missing data were explicitly assessed in 661 trials, among which 347 (52.5% of 661) applied imputations, with the 2 most used imputation methods being multiple imputations (n = 200, 57.6% of 347) and last observation carried forward (n = 106, 30.5% of 347). Conclusions: This review found that health utilities are the most frequently analyzed EQ-5D data collected in clinical trials, followed by EQ VAS. Significant variation was observed in the selection of models, with most trials lacking adjustments for baseline data and appropriate methods for handling missing data. © 2025

关键词： EQ-5D patient-reported outcome randomized clinical trial statistical analysis

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A Method for Molecular Design Based on Linear Regression and Integer Programming 22

A Method for Molecular Design Based on Linear Regression and...

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12th International Conference on Bioscience, Biochemistry and bioinformatics, ICBBB 2022

作者： Zhu, Jianshen Azam, Naveed A. Haraguchi, Kazuya Zhao, Liang Nagamochi, Hiroshi Akutsu, Tatsuya Department of Applied Mathematics and Physics Kyoto University Kyoto Japan Kyoto University Kyoto Japan Bioinformatics Center Institute for Chemical Research Kyoto University Kyoto Japan

ISBN: (纸本)9781450387385

Recently a novel framework has been proposed for designing the molecular structure of chemical compounds using both artificial neural networks (ANNs) and mixed integer linear programming (MILP). In the framework, we first define a feature vector of a chemical graph and construct an ANN that maps to a predicted value (x) of a chemical property πto . After this, we formulate an MILP that simulates the computation process of from and that of (x) from x. Given a target value y∗of the chemical property, we infer a chemical graph such that by solving the MILP. In this paper, we use linear regression to construct a prediction function instead of ANNs. For this, we derive an MILP formulation that simulates the computation process of a prediction function by linear regression. The results of computational experiments suggest our method can infer chemical graphs with around up to 50 non-hydrogen atoms. © 2022 Owner/Author.

关键词： Linear regression

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Information-theoretic approach to network modularity

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Physical Review E 2005年第4期71卷 046117-046117页

作者： Etay Ziv Manuel Middendorf Chris H. Wiggins Department of Applied Physics and Applied Mathematics Center for Computational Biology and Bioinformatics Columbia University New York New York 10027 USA

Exploiting recent developments in information theory, we propose, illustrate, and validate a principled information-theoretic algorithm for module discovery and the resulting measure of network modularity. This measure is an order parameter (a dimensionless number between 0 and 1). Comparison is made with other approaches to module discovery and to quantifying network modularity (using Monte Carlo generated Erdös-like modular networks). Finally, the network information bottleneck (NIB) algorithm is applied to a number of real world networks, including the “social” network of coauthors at the 2004 APS March Meeting.

关键词： Information theory

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Recent progress and prospects of electrolytes for electrocatalytic nitrogen reduction toward ammonia

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Chinese Chemical Letters 2024年第4期35卷 3-16页

作者： Sajid Mahmood Haiyan Wang Fang Chen Yijun Zhong Yong Hu Key Laboratory of the Ministry of Education for Advanced Catalysis Materials Department of ChemistryZhejiang Normal UniversityJinhua 321004China The Center for Applied Mathematics and Bioinformatics(CAMB) Gulf University for Science and TechnologyMishref 32093Kuwait Hangzhou Institute of Advanced Studies Zhejiang Normal UniversityHangzhou 311231China

Electrochemical nitrogen reduction reaction(ENRR) provides a promising strategy to achieve sustainable synthesis of ammonia. However, despite great efforts devoted to this research field, the problems such as low energy efficiency and weak selectivity still impede its practical implementation. Most of the research to date has been concentrated on creating sophisticated electrocatalysts, and adequate knowledge of electrolytes is still lacking. Herein, the recent progress in electrolytes for ENRR, including alkaline, neutral,acidic, water-in-salt, organic, ionic liquid, and mixed water-organic electrolytes, is thoroughly reviewed to obtain an in-depth understanding of their effects on electrocatalytic performance. Recently developed representative electrocatalysts in various types of electrolytes are also introduced, and future research priorities of different electrolytes are proposed to develop new and efficient ENRR systems.

关键词： Electrocatalytic nitrogen reduction Ammonia Electrolytes Production rate Faradaic efficiency

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Complexity measures of supervised classifications tasks: A case study for cancer gene expression data

Complexity measures of supervised classifications tasks: A c...

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2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010

作者： De Souto, Marcilio C. P. Lorena, Ana C. Spolaôr, Newton Costa, Ivan G. Department of Informatics and Applied Mathematics Federal University of Rio Grande Do Norte Brazil Center of Mathematics Computation and Cognition ABC Federal University Brazil Bioinformatics Laboratory State University of West of Parana Brazil Center of Informatics Federal University of Pernambuco Brazil

ISBN: (纸本)9781424469178

Machine Learning algorithms have been widely used for gene expression data classification, despite the fact that these data have often intrinsic limitations, such as high dimensionality and a small number of examples. Few studies try to characterize to which extent these aspects can influence the performance of the classification models induced. In this paper we compute different measures characterizing the complexity of gene expression data sets for cancer diagnosis. We then investigate how these measures relate to the classification performances achieved by support vector machines, a popular Machine Learning technique usually employed in the analysis of gene expression data. The results obtained indicate that some of the complexity indices utilized are indeed successful in explaining the difficulty involved in the classification of cancer gene expression data. © 2010 IEEE.

关键词： Classification (of information)

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Mathematical modeling of malaria transmission dynamics in humans with mobility and control states

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Infectious Disease Modelling 2023年第4期8卷 1015-1031页

作者： Gbenga Adegbite Sunday Edeki Itunuoluwa Isewon Jerry Emmanuel Titilope Dokunmu Solomon Rotimi Jelili Oyelade Ezekiel Adebiyi Covenant University Bioinformatics Research Covenant UniversityOtaNigeria Department of Computer and Information Sciences Covenant UniversityOtaNigeria Department of Mathematics Covenant UniversityOtaNigeria Department of Biochemistry Covenant UniversityOtaNigeria Covenant Applied Informatics and Communications-African Centre of Excellence Covenant UniversityOtaOgun StateNigeria Division of Applied Bioinformatics German Cancer Research Center(DKFZ)HeidelbergGermany

Malaria importation is one of the hypothetical drivers of malaria transmission dynamics across the *** studies on malaria importation focused on the effect of the use of conventional malaria control strategies as approved by the World Health Organization(WHO)on malaria transmission dynamics but did not capture the effect of the use of traditional malaria control strategies by vigilant *** order to handle the aforementioned situation,a novel system of Ordinary Differential Equations(ODEs)was developed comprising the human and the malaria vector *** of the system was carried out to assess its quantitative *** novel computational algorithm used to solve the developed system of ODEs was implemented and benchmarked with the existing Runge-Kutta numerical solution ***,simulations of different vigilant conditions useful to control malaria were carried *** novel system of malaria models was well-posed and epidemiologically meaningful based on its quantitative *** novel algorithm performed relatively better in terms of model simulation accuracy than *** the best model-fit condition of 98%vigilance to the use of conventional and traditional malaria control strategies,this study revealed that malaria importation has a persistent impact on malaria transmission *** lieu of this,this study opined that total vigilance to the use of the WHO-approved and traditional malaria management tools would be the most effective control strategy against malaria importation.

关键词： Malaria importation Traditional malaria control Ordinary differential equation Quantitative properties Novel algorithm Runge-Kutta

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Set-based Particle Swarm Optimization for Data Clustering: Comparison and Analysis of Control Parameters 23

Set-based Particle Swarm Optimization for Data Clustering: C...

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7th International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence, ISMSI 2023

作者： de Wet, Rijk Marius Engelbrecht, Andries Department of Industrial Engineering Stellenbosch University Stellenbosch South Africa Computer Science Division Stellenbosch University Stellenbosch South Africa Center for Applied Mathematics and Bioinformatics Gulf University for Science and Technology Kuwait

ISBN: (纸本)9781450399920

Data clustering is a highly studied field of data science and computational intelligence. Population-based algorithms such as particle swarm optimization (PSO) have shown to be effective at data clustering. Set-based particle swarm optimization (SBPSO) is a generic set-based PSO variant that has shown promise in clustering stationary and non-stationary data. In this paper, SBPSO is used to cluster fifteen datasets with diverse characteristics. The clustering ability of SBPSO is compared in depth to the performance of six other tuned clustering algorithms. A sensitivity analysis of the SBPSO control parameters is performed to determine the effect that variation in these control parameters have on swarm diversity and other measures. SBPSO ranked third from among the algorithms evaluated and proved a viable clustering algorithm. A trade-off between swarm diversity and clustering ability was discovered, and the control parameters that control this trade-off were determined. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Sensitivity analysis

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Comparing the transmission potential from sequence and surveillance data of 2009 North American influenza pandemic waves

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Infectious Disease Modelling 2023年第1期8卷 240-252页

作者： Venkata R.Duvvuri Joseph T.Hicks Lambodhar Damodaran Martin Grunnill Thomas Braukmann Jianhong Wu Jonathan B.Gubbay Samir N.Patel Justin Bahl Public Health Ontario TorontoOntarioCanada Department of Laboratory Medicine and Pathobiology Faculty of MedicineUniversity of TorontoTorontoOntarioCanada Laboratory for Industrial and Applied Mathematics Department of Mathematics and StatisticsYork UniversityTorontoOntarioCanada Center for the Ecology of Infectious Disease Department of Infectious DiseasesInstitute of BioinformaticsUniversity of GeorgiaAthensGeorgia Department of Epidemiology and Biostatistics Institute of BioinformaticsUniversity of GeorgiaAthensGeorgia Duke-NUS Graduate Medical School Singapore

Technological advancements in phylodynamic modeling coupled with the accessibility of real-time pathogen genetic data are increasingly important for understanding the infectious disease transmission *** this study,we compare the transmission potentials of North American influenza A(H1N1)pdm09 derived from sequence data to that derived from surveillance *** impact of the choice of tree-priors,informative epidemiological priors,and evolutionary parameters on the transmission potential estimation is *** American Influenza A(H1N1)pdm09 hemagglutinin(HA)gene sequences are analyzed using the coalescent and birth-death tree prior models to estimate the basic reproduction number(R_(0)).Epidemiological priors gathered from published literature are used to simulate the birth-death skyline ***-sampling marginal likelihood estimation is conducted to assess model fit.A bibliographic search to gather surveillancebased R_(0)values were consistently lower(mean≤1.2)when estimated by coalescent models than by the birth-death models with informative priors on the duration of infectiousness(mean≥1.3 to≤2.88 days).The user-defined informative priors for use in the birth-death model shift the directionality of epidemiological and evolutionary parameters compared to non-informative *** there was no certain impact of clock rate and tree height on the R_(0)estimation,an opposite relationship was observed between coalescent and birth-death tree *** was no significant difference(p=0.46)between the birth-death model and surveillance R0 *** study concludes that treeprior methodological differences may have a substantial impact on the transmission potential estimation as well as the evolutionary *** study also reports a consensus between the sequence-based R_(0)estimation and surveillanceased R_(0)***,these outcomes shed light on the potential role of phylodynamic modeling to augment existing surveillance

关键词： Phylodynamics Pandemic 2009 H1N1 Reproduction number Coalescent growth models Birth-death models Pathogen sequence data Public health

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An Inverse QSAR Method Based on Decision Tree and Integer Programming 17th

An Inverse QSAR Method Based on Decision Tree and Integer Pr...

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17th International Conference on Intelligent Computing, ICIC 2021

作者： Tanaka, Kouki Zhu, Jianshen Azam, Naveed Ahmed Haraguchi, Kazuya Zhao, Liang Nagamochi, Hiroshi Akutsu, Tatsuya Department of Applied Mathematics and Physics Kyoto University Kyoto606-8501 Japan Kyoto University Kyoto606-8306 Japan Bioinformatics Center Institute for Chemical Research Kyoto University Uji611-0011 Japan

ISBN: (纸本)9783030845285

Recently a novel framework has been proposed for designing the molecular structure of chemical compounds using both artificial neural networks (ANNs) and mixed integer linear programming (MILP). In the framework, we first define a feature vector f(C ) of a chemical graph C and construct an ANN that maps x = f(C ) to a predicted value η(x) of a chemical property π to C. After this, we formulate an MILP that simulates the computation process of f(C ) from C and that of η(x) from x. Given a target value y∗ of the chemical property π, we infer a chemical graph C † such that η(f(C †)) = y∗ by solving the MILP. In this paper, in pursuit of alternative learning models, we design an original decision tree that is based on a set of separating hyperplanes in the feature space which is then used to construct a prediction function η instead of ANNs. For this, we derive an MILP formulation that simulates the computation process of the proposed decision tree. The results of computational experiments suggest our method can infer chemical graphs with around up to 50 non-hydrogen atoms and that the prediction function based on a decision tree outperforms that with ANNs for several chemical properties. © 2021, Springer Nature Switzerland AG.

关键词： Decision trees

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An efficient method of computing impact degrees for multiple reactions in metabolic networks with cycles

An efficient method of computing impact degrees for multiple...

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作者： Tamura, Takeyuki Cong, Yang Akutsu, Tatsuya Ching, Wai-Ki Bioinformatics Center Institute for Chemical Research Kyoto University Uji-shi 611-0011 Japan Advanced Modeling and Applied Computing Laboratory Department of Mathematics University of Hong Kong Pokfulam Road Hong Kong Hong Kong

The impact degree is a measure of the robustness of a metabolic network against deletion of single or multiple reaction(s). Although such a measure is useful for mining important enzymes/genes, it was defined only for networks without cycles. In this paper, we extend the impact degree for metabolic networks containing cycles and develop a simple algorithm to calculate the impact degree. Furthermore we improve this algorithm to reduce computation time for the impact degree by deletions of multiple reactions. We applied our method to the metabolic network of E. coli, that includes reference pathways, consisting of 3281 reaction nodes and 2444 compound nodes, downloaded from KEGG database, and calculate the distribution of the impact degree. The results of our computational experiments show that the improved algorithm is 18.4 times faster than the simple algorithm for deletion of reaction-pairs and 11.4 times faster for deletion of reaction-triplets. We also enumerate genes with high impact degrees for single and multiple reaction deletions. © 2011 The Institute of Electronics, Information and Communication Engineers.

关键词： Metabolism

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