Statistical Modelling最新文献

{"title":"Reflections on Murray Aitkin's contributions to nonparametric mixture models and Bayes factors","authors":"A. Agresti, F. Bartolucci, A. Mira","doi":"10.1177/1471082X20981312","DOIUrl":"https://doi.org/10.1177/1471082X20981312","url":null,"abstract":"We describe two interesting and innovative strands of Murray Aitkin's research publications, dealing with mixture models and with Bayesian inference. Of his considerable publications on mixture models, we focus on a nonparametric random effects approach in generalized linear mixed modelling, which has proven useful in a wide variety of applications. As an early proponent of ways of implementing the Bayesian paradigm, Aitkin proposed an alternative Bayes factor based on a posterior mean likelihood. We discuss these innovative approaches and some research lines motivated by them and also suggest future related methodological implementations.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"22 1","pages":"33 - 45"},"PeriodicalIF":1.0,"publicationDate":"2021-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1471082X20981312","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44826179","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}

{"title":"Editorial","authors":"B. Marx, Komárek Arnošt, V. Núñez-Antón","doi":"10.1177/1471082x20943971","DOIUrl":"https://doi.org/10.1177/1471082x20943971","url":null,"abstract":"","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"21 1","pages":"7 - 7"},"PeriodicalIF":1.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1471082x20943971","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45061698","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}

{"title":"Assessing Importance of Biomarkers: a Bayesian Joint Modeling Approach of Longitudinal and Survival Data with Semicompeting Risks.","authors":"Fan Zhang,&nbsp;Ming-Hui Chen,&nbsp;Xiuyu Julie Cong,&nbsp;Qingxia Chen","doi":"10.1177/1471082x20933363","DOIUrl":"https://doi.org/10.1177/1471082x20933363","url":null,"abstract":"<p><p>Longitudinal biomarkers such as patient-reported outcomes (PROs) and quality of life (QOL) are routinely collected in cancer clinical trials or other studies. Joint modeling of PRO/QOL and survival data can provide a comparative assessment of patient-reported changes in specific symptoms or global measures that correspond to changes in survival. Motivated by a head and neck cancer clinical trial, we develop a class of trajectory-based models for longitudinal and survival data with disease progression. Specifically, we propose a class of mixed effects regression models for longitudinal measures, a cure rate model for the disease progression time (<i>T</i> _<i>P</i> ), and a Cox proportional hazards model with time-varying covariates for the overall survival time (<i>T</i> _<i>D</i> ) to account for <i>T</i> _<i>P</i> and treatment switching. Under the semi-competing risks framework, the disease progression is the nonterminal event, the occurrence of which is subject to a terminal event of death. The properties of the proposed models are examined in detail. Within the Bayesian paradigm, we derive the decompositions of the deviance information criterion (DIC) and the logarithm of the pseudo marginal likelihood (LPML) to assess the fit of the longitudinal component of the model and the fit of each survival component, separately. We further develop ΔDIC as well as ΔLPML to determine the importance and contribution of the longitudinal data to the model fit of the <i>T</i> _<i>P</i> and <i>T</i> _<i>D</i> data.</p>","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"21 1-2","pages":"30-55"},"PeriodicalIF":1.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1471082x20933363","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39258068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint modelling of longitudinal and survival data in the presence of competing risks with applications to prostate cancer data.","authors":"Md Tuhin Sheikh, Joseph G Ibrahim, Jonathan A Gelfond, Wei Sun, Ming-Hui Chen","doi":"10.1177/1471082X20944620","DOIUrl":"10.1177/1471082X20944620","url":null,"abstract":"<p><p>This research is motivated from the data from a large Selenium and Vitamin E Cancer Prevention Trial (SELECT). The prostate specific antigens (PSAs) were collected longitudinally, and the survival endpoint was the time to low-grade cancer or the time to high-grade cancer (competing risks). In this article, the goal is to model the longitudinal PSA data and the time-to-prostate cancer (PC) due to low- or high-grade. We consider the low-grade and high-grade as two competing causes of developing PC. A joint model for simultaneously analysing longitudinal and time-to-event data in the presence of multiple causes of failure (or competing risk) is proposed within the Bayesian framework. The proposed model allows for handling the missing causes of failure in the SELECT data and implementing an efficient Markov chain Monte Carlo sampling algorithm to sample from the posterior distribution via a novel reparameterization technique. Bayesian criteria, ΔDIC_Surv, and ΔWAIC_Surv, are introduced to quantify the gain in fit in the survival sub-model due to the inclusion of longitudinal data. A simulation study is conducted to examine the empirical performance of the posterior estimates as well as ΔDIC_Surv and ΔWAIC_Surv and a detailed analysis of the SELECT data is also carried out to further demonstrate the proposed methodology.</p>","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"21 1-2","pages":"72-94"},"PeriodicalIF":1.2,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8225229/pdf/nihms-1634286.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39132726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Guest Editorial","authors":"C. Armero, V. Gómez‐Rubio","doi":"10.1177/1471082X20967121","DOIUrl":"https://doi.org/10.1177/1471082X20967121","url":null,"abstract":"The main objective of this journal, Statistical Modelling, deals with original papers which consider statistical modelling as a fundamental tool for statistical learning, both methodological and applied. This special issue, devoted to Bayesian Inference for Joint Models in Survival Analysis, has been entirely inspired by this idea. Survival joint models account for complex structured modelling. Typically, the outcomes of interest are times-to-event which can be jointly analysed with other type of information in order to improve inference and gain a better insight on the scientific question under study. Usually, longitudinal input is modelled jointly with time-to-event data to allow the inclusion of temporal covariates in the survival model, but joint modelling can be extended to deal with other types of data such as spatial observations. In addition, joint models are also suitable for dealing with longitudinal scenarios with non-ignorable missing patterns which can be described in terms of survival tools. Bayesian inference offers a flexible and attractive conceptual framework to joint models of survival data mainly due to its special conception of probability that allows to quantify in probabilistic terms all the sources of uncertainty, observable or not, in the problem under study, and the use of Bayes’ theorem to sequentially update probabilities as more relevant information is obtained. Bayes computation for complex models is not easy. This topic is particularly important in the framework of Bayesian survival joint models because their practical implementation generates new computational scenarios that involve novel questions and challenges. This special issue contains eight articles which include new proposals for model implementation, methodological developments as well as interesting practical applications. Although most of the papers in this issue are methodological, all of them have a special section in which the proposed methodology is applied to a real problem, usually coming from medical contexts. Below, we briefly present the different works in this special issue. The conceptual framework of Beesley and Taylor is multistate models, a class of stochastic processes which account for event history data with individuals who may experience different events in time. This article focuses on model selection, a key topic in multistate models due to the high number of parameters in its specification which are exacerbated by complicated patterns derived from data missingness, the presence of highly correlated predictors, and complex hierarchical parameter relationships. Model selection is based on shrinkage methods that Bayesian methodology addresses through the specification of prior distributions. Horseshoe priors, and spike and slab priors defined in terms of a mixture of two normal distributions and the particular case of a spike with point mass at zero are considered. These proposals are discussed for an illness-and-death model and a gener","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"21 1","pages":"8 - 10"},"PeriodicalIF":1.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1471082X20967121","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42100946","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}

{"title":"Renewal model for anomalous traffic in Internet2 links","authors":"John Nicholson, Piotr Kokoszka, Robert Lund, Peter Kiessler, Julia Sharp","doi":"10.1177/1471082x19983146","DOIUrl":"https://doi.org/10.1177/1471082x19983146","url":null,"abstract":"<p>We propose and estimate an alternating renewal model describing the propagation of anomalies in a backbone internet network in the United States. Internet anomalies, either caused by equipment malfunction, news events or malicious attacks, have been a focus of research in network engineering since the advent of the internet over 30 years ago. This article contributes to the understanding of statistical properties of the times between the arrivals of the anomalies, their duration and stochastic structure. Anomalous, or active, time periods are modelled as periods containing clusters or 1s, where 1 indicates a presence of an anomaly. The inactive periods consisting entirely of 0s dominate the 0–1 time series in every link. Since the active periods contain 0s, a separation parameter is introduced and estimated jointly with all other parameters of the model. Our statistical analysis shows that the integer-valued separation parameter and five other non-negative, scalar parameters satisfactorily describe all statistical properties of the observed 0–1 series.</p>","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"11 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138539652","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}

{"title":"Renewal model for anomalous traffic in Internet2 links","authors":"John Nicholson, Piotr Kokoszka, Robert Lund, Peter Kiessler, Julia Sharp","doi":"10.1177/1471082x19983146","DOIUrl":"https://doi.org/10.1177/1471082x19983146","url":null,"abstract":"<p>We propose and estimate an alternating renewal model describing the propagation of anomalies in a backbone internet network in the United States. Internet anomalies, either caused by equipment malfunction, news events or malicious attacks, have been a focus of research in network engineering since the advent of the internet over 30 years ago. This article contributes to the understanding of statistical properties of the times between the arrivals of the anomalies, their duration and stochastic structure. Anomalous, or active, time periods are modelled as periods containing clusters or 1s, where 1 indicates a presence of an anomaly. The inactive periods consisting entirely of 0s dominate the 0–1 time series in every link. Since the active periods contain 0s, a separation parameter is introduced and estimated jointly with all other parameters of the model. Our statistical analysis shows that the integer-valued separation parameter and five other non-negative, scalar parameters satisfactorily describe all statistical properties of the observed 0–1 series.</p>","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"11 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138539582","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}

{"title":"Renewal model for anomalous traffic in Internet2 links","authors":"J. Nicholson, P. Kokoszka, Robert Lund, P. Kiessler, J. Sharp","doi":"10.1177/1471082X20983146","DOIUrl":"https://doi.org/10.1177/1471082X20983146","url":null,"abstract":"We propose and estimate an alternating renewal model describing the propagation of anomalies in a backbone internet network in the United States. Internet anomalies, either caused by equipment malfunction, news events or malicious attacks, have been a focus of research in network engineering since the advent of the internet over 30 years ago. This article contributes to the understanding of statistical properties of the times between the arrivals of the anomalies, their duration and stochastic structure. Anomalous, or active, time periods are modelled as periods containing clusters or 1s, where 1 indicates a presence of an anomaly. The inactive periods consisting entirely of 0s dominate the 0–1 time series in every link. Since the active periods contain 0s, a separation parameter is introduced and estimated jointly with all other parameters of the model. Our statistical analysis shows that the integer-valued separation parameter and five other non-negative, scalar parameters satisfactorily describe all statistical properties of the observed 0–1 series.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"22 1","pages":"430 - 456"},"PeriodicalIF":1.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1471082X20983146","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45358658","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}

{"title":"Multivariate ordinal random effects models including subject and group specific response style effects","authors":"G. Schauberger, G. Tutz","doi":"10.1177/1471082X20978034","DOIUrl":"https://doi.org/10.1177/1471082X20978034","url":null,"abstract":"Common random effects models for repeated measurements account for the heterogeneity in the population by including subject-specific intercepts or variable effects. They do not account for the heterogeneity in answering tendencies. For ordinal responses in particular, the tendency to choose extreme or middle responses can vary in the population. Extended models are proposed that account for this type of heterogeneity. Location effects as well as the tendency to extreme or middle responses are modelled as functions of explanatory variables. It is demonstrated that ignoring response styles may affect the accuracy of parameter estimates. An example demonstrates the applicability of the method.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"22 1","pages":"409 - 429"},"PeriodicalIF":1.0,"publicationDate":"2021-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1471082X20978034","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42190802","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}

{"title":"A mixed hidden Markov model for multivariate monotone disease processes in the presence of measurement errors","authors":"L. Naranjo, E. Lesaffre, C. J. Pérez","doi":"10.1177/1471082X20973473","DOIUrl":"https://doi.org/10.1177/1471082X20973473","url":null,"abstract":"Motivated by a longitudinal oral health study, the Signal-Tandmobiel® study, an inhomogeneous mixed hidden Markov model with continuous state-space is proposed to explain the caries disease process in children between 6 and 12 years of age. The binary caries experience outcomes are subject to misclassification. We modelled this misclassification process via a longitudinal latent continuous response subject to a measurement error process and showing a monotone behaviour. The baseline distributions of the unobservable continuous processes are defined as a function of the covariates through the specification of conditional distributions making use of the Markov property. In addition, random effects are considered to model the relationships among the multivariate responses. Our approach is in contrast with a previous approach working on the binary outcome scale. This method requires conditional independence of the possibly corrupted binary outcomes on the true binary outcomes. We assumed conditional independence on the latent scale, which is a weaker assumption than conditional independence on the binary scale. The aim of this article is therefore to show the properties of a model for a progressive longitudinal response with misclassification on the manifest scale but modelled on the latent scale. The model parameters are estimated in a Bayesian way using an efficient Markov chain Monte Carlo method. The model performance is shown through a simulation-based example, and the analysis of the motivating dataset is presented.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"22 1","pages":"385 - 408"},"PeriodicalIF":1.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1471082X20973473","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48802296","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}