Ruiwen Zhou, J. Philip Miller, Mae Gordon, Michael Kass, Mingquan Lin, Yifan Peng, Fuhai Li, Jiarui Feng, Lei Liu
Glaucoma is a major cause of blindness and vision impairment worldwide, and visual field (VF) tests are essential for monitoring the conversion of glaucoma. While previous studies have primarily focused on using VF data at a single time point for glaucoma prediction, there has been limited exploration of longitudinal trajectories. Additionally, many deep learning techniques treat the time-to-glaucoma prediction as a binary classification problem (glaucoma Yes/No), resulting in the misclassification of some censored subjects into the nonglaucoma category and decreased power. To tackle these challenges, we propose and implement several deep-learning approaches that naturally incorporate temporal and spatial information from longitudinal VF data to predict time-to-glaucoma. When evaluated on the Ocular Hypertension Treatment Study (OHTS) dataset, our proposed convolutional neural network (CNN)-long short-term memory (LSTM) emerged as the top-performing model among all those examined. The implementation code can be found online (https://github.com/rivenzhou/VF_prediction).
{"title":"Deep learning models to predict primary open-angle glaucoma","authors":"Ruiwen Zhou, J. Philip Miller, Mae Gordon, Michael Kass, Mingquan Lin, Yifan Peng, Fuhai Li, Jiarui Feng, Lei Liu","doi":"10.1002/sta4.649","DOIUrl":"https://doi.org/10.1002/sta4.649","url":null,"abstract":"Glaucoma is a major cause of blindness and vision impairment worldwide, and visual field (VF) tests are essential for monitoring the conversion of glaucoma. While previous studies have primarily focused on using VF data at a single time point for glaucoma prediction, there has been limited exploration of longitudinal trajectories. Additionally, many deep learning techniques treat the time-to-glaucoma prediction as a binary classification problem (glaucoma Yes/No), resulting in the misclassification of some censored subjects into the nonglaucoma category and decreased power. To tackle these challenges, we propose and implement several deep-learning approaches that naturally incorporate temporal and spatial information from longitudinal VF data to predict time-to-glaucoma. When evaluated on the Ocular Hypertension Treatment Study (OHTS) dataset, our proposed convolutional neural network (CNN)-long short-term memory (LSTM) emerged as the top-performing model among all those examined. The implementation code can be found online (https://github.com/rivenzhou/VF_prediction).","PeriodicalId":56159,"journal":{"name":"Stat","volume":"17 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139756536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Consider a situation where one is interested in estimating the density of a survival time that is subject to random right censoring and measurement errors. This happens often in practice, like in public health (pregnancy length), medicine (duration of infection), ecology (duration of forest fire), among others. We assume a classical additive measurement error model with Gaussian noise and unknown error variance and a random right censoring scheme. Under this setup, we develop minimal conditions under which the assumed model is identifiable when no auxiliary variables or validation data are available, and we offer a flexible estimation strategy using Laguerre polynomials for the estimation of the error variance and the density of the survival time. The asymptotic normality of the proposed estimators is established, and the numerical performance of the methodology is investigated on both simulated and real data on gestational age.
{"title":"Estimation of the density for censored and contaminated data","authors":"Ingrid Van Keilegom, Elif Kekeç","doi":"10.1002/sta4.651","DOIUrl":"https://doi.org/10.1002/sta4.651","url":null,"abstract":"Consider a situation where one is interested in estimating the density of a survival time that is subject to random right censoring and measurement errors. This happens often in practice, like in public health (pregnancy length), medicine (duration of infection), ecology (duration of forest fire), among others. We assume a classical additive measurement error model with Gaussian noise and unknown error variance and a random right censoring scheme. Under this setup, we develop minimal conditions under which the assumed model is identifiable when no auxiliary variables or validation data are available, and we offer a flexible estimation strategy using Laguerre polynomials for the estimation of the error variance and the density of the survival time. The asymptotic normality of the proposed estimators is established, and the numerical performance of the methodology is investigated on both simulated and real data on gestational age.","PeriodicalId":56159,"journal":{"name":"Stat","volume":"28 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139756535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In predictive modelling for high-stake decision-making, predictors must be not only accurate but also reliable. Conformal prediction (CP) is a promising approach for obtaining the coverage of prediction results with fewer theoretical assumptions. To obtain the prediction set by so-called full-CP, we need to refit the predictor for all possible values of prediction results, which is only possible for simple predictors. For complex predictors such as random forests (RFs) or neural networks (NNs), split-CP is often employed where the data is split into two parts: one part for fitting and another for computing the prediction set. Unfortunately, because of the reduced sample size, split-CP is inferior to full-CP both in fitting as well as prediction set computation. In this paper, we develop a full-CP of sparse high-order interaction model (SHIM), which is sufficiently flexible as it can take into account high-order interactions among variables. We resolve the computational challenge for full-CP of SHIM by introducing a novel approach called homotopy mining. Through numerical experiments, we demonstrate that SHIM is as accurate as complex predictors such as RF and NN and enjoys the superior statistical power of full-CP.
{"title":"A confidence machine for sparse high-order interaction model","authors":"Diptesh Das, Eugene Ndiaye, Ichiro Takeuchi","doi":"10.1002/sta4.633","DOIUrl":"https://doi.org/10.1002/sta4.633","url":null,"abstract":"In predictive modelling for high-stake decision-making, predictors must be not only accurate but also reliable. Conformal prediction (CP) is a promising approach for obtaining the coverage of prediction results with fewer theoretical assumptions. To obtain the prediction set by so-called full-CP, we need to refit the predictor for all possible values of prediction results, which is only possible for simple predictors. For complex predictors such as random forests (RFs) or neural networks (NNs), split-CP is often employed where the data is split into two parts: one part for fitting and another for computing the prediction set. Unfortunately, because of the reduced sample size, split-CP is inferior to full-CP both in fitting as well as prediction set computation. In this paper, we develop a full-CP of sparse high-order interaction model (SHIM), which is sufficiently flexible as it can take into account high-order interactions among variables. We resolve the computational challenge for full-CP of SHIM by introducing a novel approach called homotopy mining. Through numerical experiments, we demonstrate that SHIM is as accurate as complex predictors such as RF and NN and enjoys the superior statistical power of full-CP.","PeriodicalId":56159,"journal":{"name":"Stat","volume":"35 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139756534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Modelling multivariate time series of counts in a parsimonious way is a popular topic. In this paper, we consider an integer-valued network autoregressive model with a non-random neighbourhood structure, which uses negative binomial distribution as the conditional marginal distribution and the softplus function as the link function. The new model generalizes existing ones in the literature and has a great flexibility in modelling. Stationary conditions in cases of fixed dimension and increasing dimension are given. Parameters are estimated by maximizing the quasi-likelihood function, and related asymptotic properties of the estimators are established. A simulation study is conducted to assess performances of the estimators, and a real data example is analysed to show superior performances of the proposed model compared with existing ones.
{"title":"Softplus negative binomial network autoregression","authors":"Xiangyu Guo, Fukang Zhu","doi":"10.1002/sta4.638","DOIUrl":"https://doi.org/10.1002/sta4.638","url":null,"abstract":"Modelling multivariate time series of counts in a parsimonious way is a popular topic. In this paper, we consider an integer-valued network autoregressive model with a non-random neighbourhood structure, which uses negative binomial distribution as the conditional marginal distribution and the softplus function as the link function. The new model generalizes existing ones in the literature and has a great flexibility in modelling. Stationary conditions in cases of fixed dimension and increasing dimension are given. Parameters are estimated by maximizing the quasi-likelihood function, and related asymptotic properties of the estimators are established. A simulation study is conducted to assess performances of the estimators, and a real data example is analysed to show superior performances of the proposed model compared with existing ones.","PeriodicalId":56159,"journal":{"name":"Stat","volume":"12 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139499895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bayesian additive regression trees (BART) is a nonparametric model that is known for its flexibility and strong statistical foundation. To address a robust and flexible approach to analyse ordinal data, we extend BART into an ordered probit regression framework (OPBART). Further, we propose a semiparametric setting for OPBART (semi-OPBART) to model covariates of interest parametrically and confounding variables nonparametrically. We also provide Gibbs sampling procedures to implement the proposed models. In both simulations and real data studies, the proposed models demonstrate superior performance over other competing ordinal models. We also highlight enhanced interpretability of semi-OPBART in terms of inference through marginal effects.
{"title":"Ordered probit Bayesian additive regression trees for ordinal data","authors":"Jaeyong Lee, Beom Seuk Hwang","doi":"10.1002/sta4.643","DOIUrl":"https://doi.org/10.1002/sta4.643","url":null,"abstract":"Bayesian additive regression trees (BART) is a nonparametric model that is known for its flexibility and strong statistical foundation. To address a robust and flexible approach to analyse ordinal data, we extend BART into an ordered probit regression framework (OPBART). Further, we propose a semiparametric setting for OPBART (semi-OPBART) to model covariates of interest parametrically and confounding variables nonparametrically. We also provide Gibbs sampling procedures to implement the proposed models. In both simulations and real data studies, the proposed models demonstrate superior performance over other competing ordinal models. We also highlight enhanced interpretability of semi-OPBART in terms of inference through marginal effects.","PeriodicalId":56159,"journal":{"name":"Stat","volume":"12 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139495176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dylan Spicker, Erica E. M. Moodie, Susan M. Shortreed
Precision medicine is a framework for developing evidence-based medical recommendations that seeks to determine the optimal sequence of treatments, tailored to all of the relevant, observable patient-level characteristics. Because precision medicine relies on highly sensitive, patient-level data, ensuring the privacy of participants is of great importance. Dynamic treatment regimes (DTRs) provide one formalization of precision medicine in a longitudinal setting. Outcome-weighted learning (OWL) is a family of techniques for estimating optimal DTRs based on observational data. OWL techniques leverage support vector machine (SVM) classifiers in order to perform estimation. SVMs perform classification based on a set of influential points in the data known as support vectors. The classification rule produced by SVMs often requires direct access to the support vectors. Thus, releasing a treatment policy estimated with OWL requires the release of patient data for a subset of patients in the sample. As a result, the classification rules from SVMs constitute a severe privacy violation for those individuals whose data comprise the support vectors. This privacy violation is a major concern, particularly in light of the potentially highly sensitive medical data that are used in DTR estimation. Differential privacy has emerged as a mathematical framework for ensuring the privacy of individual-level data, with provable guarantees on the likelihood that individual characteristics can be determined by an adversary. We provide the first investigation of differential privacy in the context of DTRs and provide a differentially private OWL estimator, with theoretical results allowing us to quantify the cost of privacy in terms of the accuracy of the private estimators.
{"title":"Differentially private outcome-weighted learning for optimal dynamic treatment regime estimation","authors":"Dylan Spicker, Erica E. M. Moodie, Susan M. Shortreed","doi":"10.1002/sta4.641","DOIUrl":"https://doi.org/10.1002/sta4.641","url":null,"abstract":"Precision medicine is a framework for developing evidence-based medical recommendations that seeks to determine the optimal sequence of treatments, tailored to all of the relevant, observable patient-level characteristics. Because precision medicine relies on highly sensitive, patient-level data, ensuring the privacy of participants is of great importance. Dynamic treatment regimes (DTRs) provide one formalization of precision medicine in a longitudinal setting. Outcome-weighted learning (OWL) is a family of techniques for estimating optimal DTRs based on observational data. OWL techniques leverage support vector machine (SVM) classifiers in order to perform estimation. SVMs perform classification based on a set of influential points in the data known as support vectors. The classification rule produced by SVMs often requires direct access to the support vectors. Thus, releasing a treatment policy estimated with OWL requires the release of patient data for a subset of patients in the sample. As a result, the classification rules from SVMs constitute a severe privacy violation for those individuals whose data comprise the support vectors. This privacy violation is a major concern, particularly in light of the potentially highly sensitive medical data that are used in DTR estimation. Differential privacy has emerged as a mathematical framework for ensuring the privacy of individual-level data, with provable guarantees on the likelihood that individual characteristics can be determined by an adversary. We provide the first investigation of differential privacy in the context of DTRs and provide a differentially private OWL estimator, with theoretical results allowing us to quantify the cost of privacy in terms of the accuracy of the private estimators.","PeriodicalId":56159,"journal":{"name":"Stat","volume":"12 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139499925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Omics data, routinely collected in various clinical settings, are of a complex and network-structured nature. Recent progress in RNA sequencing (RNA-seq) allows us to explore whole-genome gene expression profiles and to develop predictive model for disease risk. In this study, we propose a novel Bayesian approach to construct RNA-seq-based risk score leveraging gene expression network for disease risk prediction. Specifically, we consider a hierarchical model with spike and slab priors over regression coefficients as well as entries in the inverse covariance matrix for covariates to simultaneously perform variable selection and network estimation in high-dimensional logistic regression. Through theoretical investigation and simulation studies, our method is shown to both enjoy desirable consistency properties and achieve superior empirical performance compared with other state-of-the-art methods. We analyse RNA-seq gene expression data from 441 asthmatic and 254 non-asthmatic samples to form a weighted network-guided risk score and benchmark the proposed method against existing approaches for asthma risk stratification.
{"title":"Development of network-guided transcriptomic risk score for disease prediction","authors":"Xuan Cao, Liangliang Zhang, Kyoungjae Lee","doi":"10.1002/sta4.648","DOIUrl":"https://doi.org/10.1002/sta4.648","url":null,"abstract":"Omics data, routinely collected in various clinical settings, are of a complex and network-structured nature. Recent progress in RNA sequencing (RNA-seq) allows us to explore whole-genome gene expression profiles and to develop predictive model for disease risk. In this study, we propose a novel Bayesian approach to construct RNA-seq-based risk score leveraging gene expression network for disease risk prediction. Specifically, we consider a hierarchical model with spike and slab priors over regression coefficients as well as entries in the inverse covariance matrix for covariates to simultaneously perform variable selection and network estimation in high-dimensional logistic regression. Through theoretical investigation and simulation studies, our method is shown to both enjoy desirable consistency properties and achieve superior empirical performance compared with other state-of-the-art methods. We analyse RNA-seq gene expression data from 441 asthmatic and 254 non-asthmatic samples to form a weighted network-guided risk score and benchmark the proposed method against existing approaches for asthma risk stratification.","PeriodicalId":56159,"journal":{"name":"Stat","volume":"37 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139481289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A key aspect where extreme values methods differ from standard statistical models is through having asymptotic theory to provide a theoretical justification for the nature of the models used for extrapolation. In multivariate extremes, many different asymptotic theories have been proposed, partly as a consequence of the lack of ordering property with vector random variables. One class of multivariate models, based on conditional limit theory as one variable becomes extreme has received wide practical usage. The underpinning value of this approach has been supported by further theoretical characterisations of the limiting relationships. However, the paper “Conditional extreme value models: fallacies and pitfalls” by Holger Drees and Anja Janßen provides a number of counterexamples to these results. This paper studies these counterexamples in a conditional extremes framework which involves marginal standardisation to a common exponentially decaying tailed marginal distribution. Our calculations show that some of the issues identified can be addressed in this way.
{"title":"Some benefits of standardisation for conditional extremes","authors":"Christian Rohrbeck, Jonathan A. Tawn","doi":"10.1002/sta4.647","DOIUrl":"https://doi.org/10.1002/sta4.647","url":null,"abstract":"A key aspect where extreme values methods differ from standard statistical models is through having asymptotic theory to provide a theoretical justification for the nature of the models used for extrapolation. In multivariate extremes, many different asymptotic theories have been proposed, partly as a consequence of the lack of ordering property with vector random variables. One class of multivariate models, based on conditional limit theory as one variable becomes extreme has received wide practical usage. The underpinning value of this approach has been supported by further theoretical characterisations of the limiting relationships. However, the paper “Conditional extreme value models: fallacies and pitfalls” by Holger Drees and Anja Janßen provides a number of counterexamples to these results. This paper studies these counterexamples in a conditional extremes framework which involves marginal standardisation to a common exponentially decaying tailed marginal distribution. Our calculations show that some of the issues identified can be addressed in this way.","PeriodicalId":56159,"journal":{"name":"Stat","volume":"5 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139476920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marianne Huebner, Laura Bond, Felesia Stukes, Joel Herndon, David J. Edwards, Gina-Maria Pomann
Data science consulting and collaboration units (DSUs) are core infrastructure for research at universities. Activities span data management, study design, data analysis, data visualization, predictive modelling, preparing reports, manuscript writing and advising on statistical methods and may include an experiential or teaching component. Partnerships are needed for a thriving DSU as an active part of the larger university network. Guidance for identifying, developing and managing successful partnerships for DSUs can be summarized in six rules: (1) align with institutional strategic plans, (2) cultivate partnerships that fit your mission, (3) ensure sustainability and prepare for growth, (4) define clear expectations in a partnership agreement, (5) communicate and (6) expect the unexpected. While these rules are not exhaustive, they are derived from experiences in a diverse set of DSUs, which vary by administrative home, mission, staffing and funding model. As examples in this paper illustrate, these rules can be adapted to different organizational models for DSUs. Clear expectations in partnership agreements are essential for high quality and consistent collaborations and address core activities, duration, staffing, cost and evaluation. A DSU is an organizational asset that should involve thoughtful investment if the institution is to gain real value.
{"title":"Developing partnerships for academic data science consulting and collaboration units","authors":"Marianne Huebner, Laura Bond, Felesia Stukes, Joel Herndon, David J. Edwards, Gina-Maria Pomann","doi":"10.1002/sta4.644","DOIUrl":"https://doi.org/10.1002/sta4.644","url":null,"abstract":"Data science consulting and collaboration units (DSUs) are core infrastructure for research at universities. Activities span data management, study design, data analysis, data visualization, predictive modelling, preparing reports, manuscript writing and advising on statistical methods and may include an experiential or teaching component. Partnerships are needed for a thriving DSU as an active part of the larger university network. Guidance for identifying, developing and managing successful partnerships for DSUs can be summarized in six rules: (1) align with institutional strategic plans, (2) cultivate partnerships that fit your mission, (3) ensure sustainability and prepare for growth, (4) define clear expectations in a partnership agreement, (5) communicate and (6) expect the unexpected. While these rules are not exhaustive, they are derived from experiences in a diverse set of DSUs, which vary by administrative home, mission, staffing and funding model. As examples in this paper illustrate, these rules can be adapted to different organizational models for DSUs. Clear expectations in partnership agreements are essential for high quality and consistent collaborations and address core activities, duration, staffing, cost and evaluation. A DSU is an organizational asset that should involve thoughtful investment if the institution is to gain real value.","PeriodicalId":56159,"journal":{"name":"Stat","volume":"5 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139459547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Testing for equivalence, rather than testing for a difference, is an important component of some scientific studies. While the focus of the existing literature is on comparing two groups for equivalence, real-world applications arise regularly that require testing across more than two groups. This paper reviews the existing approaches for testing across multiple groups and proposes a novel framework for multigroup equivalence testing under a Bayesian paradigm. This approach allows for a more scientifically meaningful definition of the equivalence margin and a more powerful test than the few existing alternatives. This approach also allows a new definition of equivalence based on future differences.
{"title":"Equivalence testing for multiple groups","authors":"Tony Pourmohamad, Herbert K. H. Lee","doi":"10.1002/sta4.645","DOIUrl":"https://doi.org/10.1002/sta4.645","url":null,"abstract":"Testing for equivalence, rather than testing for a difference, is an important component of some scientific studies. While the focus of the existing literature is on comparing two groups for equivalence, real-world applications arise regularly that require testing across more than two groups. This paper reviews the existing approaches for testing across multiple groups and proposes a novel framework for multigroup equivalence testing under a Bayesian paradigm. This approach allows for a more scientifically meaningful definition of the equivalence margin and a more powerful test than the few existing alternatives. This approach also allows a new definition of equivalence based on future differences.","PeriodicalId":56159,"journal":{"name":"Stat","volume":"3 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139460065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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