检索结果-内蒙古大学图书馆

model-based recursive partitioning for Discrete Event Times

STATISTICAL ANALYSIS AND DATA MINING-AN ASA DATA SCIENCE JOURNAL 2025年第2期18卷

作者： Huber, Cynthia Schmid, Matthias Friede, Tim Univ Med Ctr Gottingen Dept Med Stat Gottingen Germany Univ Bonn Inst Med Biometry Informat & Epidemiol Med Fac Bonn Germany

model-based recursive partitioning (MOB) is used to identify subgroups based on various outcome measures, including time-to-event outcomes. Discrete time-to-event data are typically fitted using the generalized linear model (GLM) framework with binary outcome. However, direct application of MOB with GLMs for binary outcomes needs an augmented data matrix, which violates the assumption of independent observations required for MOB's splitting criterion. We propose a permutation approach tailored to discrete time-to-event data that controls the error rate of MOB's splitting criterion, avoiding spurious subgroup identification. In simulations, we compare this permutation approach to the na & iuml;ve approach applying MOB with regression models for binary outcomes directly to the augmented data and MOB using the sum of the score contributions. Our experiments showed that MOB using the permutation approach controls the Type I error rate and accurately identifies splitting variables. To illustrate the various MOB approaches for discrete time-to-event data, we apply them to an example data set on unemployment duration.

关键词： discrete event times model-based recursive partitioning survival tree

来源：评论

学校读者我要写书评

暂无评论

Detecting heterogeneity in the causal direction of dependence: A model-based recursive partitioning approach

引用

BEHAVIOR RESEARCH METHODS 2024年第4期56卷 2711-2730页

作者： Wiedermann, Wolfgang Zhang, Bixi Shi, Dexin Univ Missouri Missouri Prevent Sci InstColl Educ & Human Dev Dept Educ School & Counseling Psychol Statist Measurement & Evaluat Educ 13A Hill Hall Columbia MO 65211 USA City Univ New York Grad Ctr New York NY USA Univ South Carolina Columbia SC USA

Methods of causal discovery and direction of dependence to evaluate causal properties of variable relations have experienced rapid development. The majority of causal discovery methods, however, relies on the assumption of causal effect homogeneity, that is, the identified causal structure is expected to hold for the entire population. Because causal mechanisms can vary across subpopulations, we propose combining methods of model-based recursive partitioning and non-Gaussian causal discovery to identify such subpopulations. The resulting algorithm can discover subpopulations with potentially varying magnitude and causal direction of effects under mild parameter inequality assumptions. Feasibility conditions are described and results from synthetic data experiments are presented suggesting that large effects and large sample sizes are beneficial for detecting causally competing subgroups with acceptable statistical performance. In a real-world data example, the extraction of meaningful subgroups that differ in the causal mechanism underlying the development of numerical cognition is illustrated. Potential extensions and recommendations for best practice applications are discussed.

关键词： Causal discovery Direction of dependence Moderation model-based recursive partitioning Causal effect heterogeneity

来源：评论

学校读者我要写书评

暂无评论

Subgroup identification in individual participant data meta-analysis using model-based recursive partitioning

引用

ADVANCES IN DATA ANALYSIS AND CLASSIFICATION 2022年第3期16卷 797-815页

作者： Huber, Cynthia Benda, Norbert Friede, Tim Univ Med Ctr Gottingen Dept Med Stat Gottingen Germany Fed Inst Drugs & Med Devices BfArM Bonn Germany

model-based recursive partitioning (MOB) can be used to identify subgroups with differing treatment effects. The detection rate of treatment-by-covariate interactions and the accuracy of identified subgroups using MOB depend strongly on the sample size. Using data from multiple randomized controlled clinical trials can overcome the problem of too small samples. However, naively pooling data from multiple trials may result in the identification of spurious subgroups as differences in study design, subject selection and other sources of between-trial heterogeneity are ignored. In order to account for between-trial heterogeneity in individual participant data (IPD) meta-analysis random-effect models are frequently used. Commonly, heterogeneity in the treatment effect is modelled using random effects whereas heterogeneity in the baseline risks is modelled by either fixed effects or random effects. In this article, we propose metaMOB, a procedure using the generalized mixed-effects model tree (GLMM tree) algorithm for subgroup identification in IPD meta-analysis. Although the application of metaMOB is potentially wider, e.g. randomized experiments with participants in social sciences or preclinical experiments in life sciences, we focus on randomized controlled clinical trials. In a simulation study, metaMOB outperformed GLMM trees assuming a random intercept only and model-based recursive partitioning (MOB), whose algorithm is the basis for GLMM trees, with respect to the false discovery rates, accuracy of identified subgroups and accuracy of estimated treatment effect. The most robust and therefore most promising method is metaMOB with fixed effects for modelling the between-trial heterogeneity in the baseline risks.

关键词： model-based recursive partitioning Subgroup identification Meta-analysis Individual participant data IPD Treatment-by subgroup interaction Personalized medicine

来源：评论

学校读者我要写书评

暂无评论

Identifying treatment moderators of a trauma-informed parenting intervention with children in foster care: Using model-based recursive partitioning

引用

CHILD ABUSE & NEGLECT 2021年 117卷 105065-105065页

作者： Chung, Gerard Ansong, David Brevard, Kanisha C. Chen, Ding-Geng Univ N Carolina Sch Social Work Chapel Hill NC 27515 USA

Background: Trauma-informed parenting interventions have been used in child welfare to help caregivers respond to children in trauma-informed ways that can mitigate the effects of maltreatment and build strong caregiver-child relationships. Existing studies support their effectiveness with children and youth involved in the child welfare system. However, to further advance the effectiveness of evidenced-based intervention for child welfare populations, one key step is to identify subgroups of individuals who have different intervention responses or outcomes. Objective: To identify pre-treatment moderators that can distinguish subgroups of caregivers and children who benefit differently from an intervention. Participants and setting: 414 children in foster care (age 3 or younger) and their caregivers (birth, adoptive, kin, and nonkin) were randomly assigned to receive a trauma-informed parenting intervention in the Illinois Birth through Three Title IV-E waiver demonstration or foster care services as usual. Methods: model-based recursive partitioning (MOB) was used to identify treatment moderators and moderator interactions. MOB fits a parametric model and uses a data-driven method to find subgroups for which the specified parametric model has different parameters. Two parametric models (logistic and linear regression) were used in accordance with two outcomes: reunification (binary) and caregiver-child attachment (continuous). We examined 21 potential pre-treatment moderators in both models. Results: For the reunification outcome, the MOB produced the following three treatment moderators, which identified subgroups of participants who responded differently to the intervention: (a) caregivers' relationship with the child (kin vs. non-kin/permanent caregivers), (b) caregiverchild attachment, and (c) case history of physical abuse. For the attachment outcome, caregivers' age was found to be a treatment moderator. Future developments of trauma-informed interventions sh

关键词： Trauma Parenting Foster children model-based recursive partitioning Moderators Reunification

来源：评论

学校读者我要写书评

暂无评论

Application of a model-based recursive partitioning algorithm to predict crash frequency

引用

ACCIDENT ANALYSIS AND PREVENTION 2019年 132卷 105274-105274页

作者： Tang, Houjun Donnell, Eric T. Penn State Univ Dept Civil & Environm Engn 212 Sackett Bldg University Pk PA 16802 USA

Count regression models have been applied widely in traffic safety research to estimate expected crash frequencies on road segments. Data mining algorithms, such as classification and regression trees, have recently been introduced into the field to overcome some of the assumptions associated with statistical models. However, these data-driven algorithms usually provide non-parametric output, making it difficult to draw statistical inference or to evaluate how independent variables are associated with expected crash frequencies. In this paper, the model-based recursive partitioning (MOB) algorithm is applied in a crash frequency application. The algorithm incorporates the concept of recursive partitioning data in tree models and develops user-defined statistical models as outputs. The objective of this paper is to explore the potential of the MOB algorithm as a methodological alternative to parametric modeling methods in crash frequency analysis. To accomplish the objective, a standard negative binomial (NB) regression model, a NB model developed using the MOB algorithm, adjusted NB models which incorporate variables identified by the MOB algorithm, and a random parameters NB model are compared using 8 years of data collected from two-lane rural highways in Pennsylvania. The models are compared in terms of data fitness, sign and magnitude of statistical association between the independent and dependent variables, and predictive power. The results show that the MOB-NB model yields better data fitness than other NB models, and provides similar performance to the RPNB model, suggesting that the MOB-NB model may be capturing unobserved heterogeneity by dividing the data into subgroups. The presence of a passing zone and posted speed limit are two covariates identified by the MOB algorithm that differentiate variable effects among subgroups. In addition, the MOB-NB model provides the highest prediction accuracy based on the training and test data sets, although the diffe

关键词： Roadway safety Crash frequency Heterogeneity Data mining model-based recursive partitioning

来源：评论

学校读者我要写书评

暂无评论

Using model-based recursive partitioning for Treatment-Subgroup Interactions Detection in Real-World Data: A Myocardial Infarction Case Study 40

Using Model-Based Recursive Partitioning for Treatment-Subgr...

引用

Conference on Medical Informatics Europe (MIE)

作者： Chen, Tiange Li, Xiang Yang, Jingang Hu, Jingyi Xu, Meilin Qin, Yong Yang, Yuejin IBM Res China Beijing Peoples R China Fuwai Hosp Dept Cardiol Natl Ctr Cardiovasc Dis Beijing Peoples R China Pfizer Investment Co Ltd Beijing Peoples R China

ISBN: (纸本)9781614998525;9781614998518

The effects of treatment often vary over subpopulations characterized by baseline patient features. Detection of such treatment-subgroup interaction is of central importance to precision medicine and personalized care. In this paper, we propose an analytical framework for treatment-subgroup interactions detection and treatment effectiveness heterogeneity evaluation in a real-world data setting. model-based recursive partitioning analysis (MOB) is used for subgroup identification, filter-based confounder selection and multivariate logistic regression are used for confounding reduction and treatment effectiveness assessment. We illustrate this approach by a real-world case study that analyzes the effects of 15 drugs among patients with myocardial infarction (MI) using China Acute Myocardial Infarction (CAMI) registry data. The results show that our approach effectively identifies meaningful patient subgroups involved in treatment-subgroup interactions and thus can potentially aid decision making in personalized medicine.

关键词： treatment-subgroup interactions treatment effectiveness model-based recursive partitioning myocardial infarction

来源：评论

学校读者我要写书评

暂无评论

Investigating heterogeneity in IRTree models for multiple response processes with score-based partitioning

引用

BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY 2024年第0期78卷 420-439页

作者： Debelak, Rudolf Meiser, Thorsten Gernand, Alicia Univ Zurich Zurich Switzerland Univ Mannheim Mannheim Germany RPTU Kaiserslautern Landau Landau Germany

Item response tree (IRTree) models form a family of psychometric models that allow researchers to control for multiple response processes, such as different sorts of response styles, in the measurement of latent traits. While IRTree models can capture quantitative individual differences in both the latent traits of interest and the use of response categories, they maintain the basic assumption that the nature and weighting of latent response processes are homogeneous across the entire population of respondents. In the present research, we therefore propose a novel approach for detecting heterogeneity in the parameters of IRTree models across subgroups that engage in different response behavior. The approach uses score-based tests to reveal violations of parameter heterogeneity along extraneous person covariates, and it can be employed as a model-based partitioning algorithm to identify sources of differences in the strength of trait-based responding or other response processes. Simulation studies demonstrate generally accurate Type I error rates and sufficient power for metric, ordinal, and categorical person covariates and for different types of test statistics, with the potential to differentiate between different types of parameter heterogeneity. An empirical application illustrates the use of score-based partitioning in the analysis of latent response processes with real data.

关键词： IRTree models item response theory model-based recursive partitioning parameter heterogeneity response styles score-based tests

来源：评论

学校读者我要写书评

暂无评论

Random forest methodology for model-based recursive partitioning: the mobForest package for R

引用

BMC BIOINFORMATICS 2013年第1期14卷 1-8页

作者： Garge, Nikhil R. Bobashev, Georgiy Eggleston, Barry Res Triangle Inst Div Hlth Sci Res Triangle Pk NC 27709 USA

Background: recursive partitioning is a non-parametric modeling technique, widely used in regression and classification problems. model-based recursive partitioning is used to identify groups of observations with similar values of parameters of the model of interest. The mob() function in the party package in R implements model-based recursive partitioning method. This method produces predictions based on single tree models. Predictions obtained through single tree models are very sensitive to small changes to the learning sample. We extend the model-based recursive partition method to produce predictions based on multiple tree models constructed on random samples achieved either through bootstrapping (random sampling with replacement) or subsampling (random sampling without replacement) on learning data. Results: Here we present an R package called "mobForest" that implements bagging and random forests methodology for model-based recursive partitioning. The mobForest package constructs large number of model-based trees and the predictions are aggregated across these trees resulting in more stable predictions. The package also includes functions for computing predictive accuracy estimates and plots, residuals plot, and variable importance plot. Conclusion: The mobForest package implements a random forest type approach for model-based recursive partitioning. The R package along with it source code is available at http://***/package=mobForest.

关键词： Random forests model-based recursive partitioning Ensemble R

来源：评论

学校读者我要写书评

暂无评论

Heterogeneity in the association between social support and mental distress in old-age retirees - a computational approach using longitudinal cohort data

引用

BMC GERIATRICS 2024年第1期24卷 813页

作者： Lahelma, Jere Lahelma, Eero Laaksonen, Mikko Kuivalainen, Susan Koivisto, Mikko Lallukka, Tea Univ Helsinki Dept Comp Sci Helsinki Finland Finnish Ctr Pens Helsinki Finland Univ Helsinki Dept Publ Hlth Helsinki Finland

Background Mental distress among retirees and older people is a severe public health challenge, and information on new risk groups is needed. This study aims to identify subgroups of old-age retirees with varying associations between low social support and mental distress by applying model-based recursive partitioning (MOB). Methods We used the Helsinki Health Study follow-up survey data of old-age retired former municipal sector employees of the City of Helsinki, Finland. Phase 1 data were collected in 2000-2002, when all participants were employed, Phase 2 in 2007, Phase 3 in 2012, Phase 4 in 2017, and Phase 5 in 2022 (n = 4,466, 81% women). Social support and covariates were measured at each Phase 1-5 and the outcome, mental distress (Depression Anxiety Stress Scales [DASS-21]) was measured at a single occasion, during Phase 5. The three subscales and the common factor of general distress were analysed separately. An approach rooted in computational statistics was used to investigate risk factor heterogeneity in the association of low social support and mental distress. MOB combines decision trees with regression analysis to identify subgroups with the most significant heterogeneity among risk factors. Results Median (IQR) general distress score from DASS-21 was 5.7 (3.0, 9.0), while Social Support Questionnaire number-score (SSQN) was 1.5 (1.15, 2.05). The primary effect modifier for the association between social support and general distress was education (p < 0.001). Those with high education had a different association of low social support and general distress than those with low or medium education. Additionally, the subgroup with low and medium education had a significant effect modification for age (p = 0.01). For the association between low social support and depressive symptoms, the moderating effect of education was dependent on gender, as men with medium-high education had the weakest association, while for women with medium-high education the associa

关键词： Mental distress Social support Longitudinal study model-based recursive partitioning Old-age retiree DASS-21 Decision tree Subgroup analysis

来源：评论

学校读者我要写书评

暂无评论

Partial credit trees meet the partial gamma coefficient for quantifying DIF and DSF in polytomous items

引用

Behaviormetrika 2025年 1-37页

作者： Henninger, Mirka Radek, Jan Sengewald, Marie-Ann Strobl, Carolin Faculty of Psychology University of Basel Basel Switzerland Leibniz Institute for Educational Trajectories Bamberg Germany Department of Psychology University of Zurich Zurich Switzerland

Partial credit trees (PCtree) from the model-based recursive partitioning framework combine the partial credit measurement model for polytomous items with decision trees from machine learning. This method allows researchers to investigate measurement invariance by detecting differential item and differential step functioning (DIF/DSF) in a data driven way. In this manuscript, we extend PCtrees by an effect size measure for DIF/DSF in polytomous items, the partial gamma coefficient from psychometrics. We evaluate this extension of PCtrees in a series of simulation studies. Our results show that the partial gamma coefficient supports researchers in evaluating whether splits in the tree are meaningful, identifying DIF and DSF items, and can stop the tree from growing in case of negligible effect sizes. Furthermore, we assess and implement a correction for item-wise testing that is particularly crucial in longer tests. Finally, we illustrate the extension of PCtrees using data from the LISS panel to showcase its enhanced interpretability. © The Behaviormetric Society 2025.

关键词： Differential item functioning Differential step functioning Machine learning model-based recursive partitioning Partial credit model Psychometrics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：