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Random effects model

About: Random effects model is a research topic. Over the lifetime, 8388 publications have been published within this topic receiving 438823 citations. The topic is also known as: random effects & random effect.


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Journal ArticleDOI
TL;DR: The Median Hazard Ratio (MHR) is a useful and intuitive measure for expressing cluster heterogeneity in the outcome and, thereby, estimating general contextual effects in multilevel survival analysis.
Abstract: Multilevel data occurs frequently in many research areas like health services research and epidemiology. A suitable way to analyze such data is through the use of multilevel regression models (MLRM). MLRM incorporate cluster-specific random effects which allow one to partition the total individual variance into between-cluster variation and between-individual variation. Statistically, MLRM account for the dependency of the data within clusters and provide correct estimates of uncertainty around regression coefficients. Substantively, the magnitude of the effect of clustering provides a measure of the General Contextual Effect (GCE). When outcomes are binary, the GCE can also be quantified by measures of heterogeneity like the Median Odds Ratio (MOR) calculated from a multilevel logistic regression model. Time-to-event outcomes within a multilevel structure occur commonly in epidemiological and medical research. However, the Median Hazard Ratio (MHR) that corresponds to the MOR in multilevel (i.e., 'frailty') Cox proportional hazards regression is rarely used. Analogously to the MOR, the MHR is the median relative change in the hazard of the occurrence of the outcome when comparing identical subjects from two randomly selected different clusters that are ordered by risk. We illustrate the application and interpretation of the MHR in a case study analyzing the hazard of mortality in patients hospitalized for acute myocardial infarction at hospitals in Ontario, Canada. We provide R code for computing the MHR. The MHR is a useful and intuitive measure for expressing cluster heterogeneity in the outcome and, thereby, estimating general contextual effects in multilevel survival analysis. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

70 citations

Journal ArticleDOI
TL;DR: In this article, the authors consider the relationship of covariates to the time to caries of permanent first molars and suggest an accelerated failure time model with random effects, taking into account that the observations are clustered.
Abstract: In this article we consider the relationship of covariates to the time to caries of permanent first molars. This involves an analysis of multivariate doubly interval-censored data. To describe this relationship, we suggest an accelerated failure time model with random effects, taking into account that the observations are clustered. Indeed, up to four permanent molars per child enter into the analysis, implying up to four caries times for each child. Each distributional part of the model is specified in a flexible way as a penalized Gaussian mixture with an overspecified number of mixture components. A Bayesian approach with the Markov chain Monte Carlo methodology is used to estimate the model parameters, and a software package in the R language has been written that implements it.

70 citations

Reference BookDOI
16 Jan 2013
TL;DR: Analysis of mixed data: An overview Alexander R. de Leon and Keumhee Carriere Chough combine univariate and multivariate random forests for enhancing predictions of mixed outcomes with joint modeling of mixed count and continuous longitudinal data.
Abstract: Analysis of mixed data: An overview Alexander R de Leon and Keumhee Carriere Chough Introduction Early developments in mixed data analysis Joint analysis of mixed outcomes Highlights of book Combining univariate and multivariate random forests for enhancing predictions of mixed outcomes Abdessamad Dine, Denis Larocque, and Francois Bellavance Introduction Predictions from univariate and multivariate random forests Simulation study Discussion Joint tests for mixed traits in genetic association studies Minjung Kwak, Gang Zheng, and Colin O Wu Introduction Analysis of binary or quantitative traits Joint analysis of mixed traits Application Discussion Bias in factor score regression and a simple solution Takahiro Hoshino and Peter M Bentler Introduction Model Bias due to estimated factor scores: Factor analysis model Proposed estimation method Simulation studies Application Theoretical details Discussion Joint modeling of mixed count and continuous longitudinal data Jian Kang and Ying Yang Introduction Complete data model Handling missing data problem Application Discussion Factorization and latent variable models for joint analysis of binary and continuous outcomes Armando Teixeira-Pinto and Jaroslaw Harezlak Introduction Clinical trial on bare-metal and drug-eluting stents Separate analyses Factorization models for binary and continuous outcomes Latent variable models for binary and continuous outcomes Software Discussion Regression models for analyzing clustered binary and continuous outcomes under the assumption of exchangeability E Olusegun George, Dale Bowman, and Qi An Introduction Distribution theory and likelihood representation Parametric models Application to DEHP data Litter-specific joint quantitative risk assessment Discussion Random effects models for joint analysis of repeatedly measured discrete and continuous outcomes Ralitza Gueorguieva Introduction Models Estimation and inference Applications Discussion Hierarchical modeling of endpoints of different types with generalized linear mixed models Christel Faes Introduction Multivariate multi-level models Special cases Likelihood inference Applications Discussion Joint analysis of mixed discrete and continuous outcomes via copula models Beilei Wu, Alexander R de Leon, and Niroshan Withanage Introduction Joint models via copulas Associations Likelihood estimation Analysis of ethylene glycol toxicity data Discussion Analysis of mixed outcomes in econometrics: Applications in health economics David M Zimmer Introduction Random effects models Copula models Application to drug spending and health status Application to nondrug spending and drug usage Discussion Sparse Bayesian modeling of mixed econometric data using data augmentation Helga Wagner and Regina Tuchler Introduction Model specification Logit-normal model Modeling material deprivation and household income Estimating consumer behavior from panel data Discussion Bayesian methods for the analysis of mixed categorical and continuous (incomplete) data Michael J Daniels and Jeremy T Gaskins Introduction Examples Characterizing dependence (Informative) Priors Incomplete responses General computational issues Analysis of examples Discussion

70 citations

Journal ArticleDOI
TL;DR: The epsilon-method has been implemented by us as a generic option in the open-source Template Model Builder software, and could be adapted within other mixed-effects modeling tools such as Automatic Differentiation Model Builder for random effects.

70 citations

Journal ArticleDOI
TL;DR: This paper considers the problem of estimation of the rate of change of a disease marker in longitudinal studies, in which some subjects drop out prematurely (informatively) due to attrition, while others experience a non-informative drop-out process (end of study, withdrawal).
Abstract: Many cohort studies and clinical trials have designs which involve repeated measurements of disease markers. One problem in such longitudinal studies, when the primary interest is to estimate and to compare the evolution of a disease marker, is that planned data are not collected because of missing data due to missing visits and/or withdrawal or attrition (for example, death). Several methods to analyse such data are available, provided that the data are missing at random. However, serious biases can occur when missingness is informative. In such cases, one needs to apply methods that simultaneously model the observed data and the missingness process. In this paper we consider the problem of estimation of the rate of change of a disease marker in longitudinal studies, in which some subjects drop out prematurely (informatively) due to attrition, while others experience a non-informative drop-out process (end of study, withdrawal). We propose a method which combines a linear random effects model for the underlying pattern of the marker with a log-normal survival model for the informative drop-out process. Joint estimates are obtained through the restricted iterative generalized least squares method which are equivalent to restricted maximum likelihood estimates. A nested EM algorithm is applied to deal with censored survival data. The advantages of this method are: it provides a unified approach to estimate all the model parameters; it can effectively deal with irregular data (that is, measured at irregular time points), a complicated covariance structure and a complex underlying profile of the response variable; it does not entail such complex computation as would be required to maximize the joint likelihood. The method is illustrated by modelling CD4 count data in a clinical trial in patients with advanced HIV infection while its performance is tested by simulation studies.

69 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
2023198
2022433
2021409
2020380
2019404