scholarly journals Linear Mixed Models

2022 ◽  
pp. 141-170
Author(s):  
Osval Antonio Montesinos López ◽  
Abelardo Montesinos López ◽  
Jose Crossa
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Mixed Models ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Linear Mixed Models ◽  
Prediction Performance ◽  
Environment Interaction ◽  
Model Framework ◽  
Genotype Environment Interaction

AbstractThe linear mixed model framework is explained in detail in this chapter. We explore three methods of parameter estimation (maximum likelihood, EM algorithm, and REML) and illustrate how genomic-enabled predictions are performed under this framework. We illustrate the use of linear mixed models by using the predictor several components such as environments, genotypes, and genotype × environment interaction. Also, the linear mixed model is illustrated under a multi-trait framework that is important in the prediction performance when the degree of correlation between traits is moderate or large. We illustrate the use of single-trait and multi-trait linear mixed models and provide the R codes for performing the analyses.

Generalized linear mixed models for multi-reader multi-case studies of diagnostic tests

2015 ◽  
Vol 26 (3) ◽  
pp. 1373-1388 ◽  
Author(s):  
Wei Liu ◽  
Norberto Pantoja-Galicia ◽  
Bo Zhang ◽  
Richard M Kotz ◽  
Gene Pennello ◽  
...  
Keyword(s):  
Mixed Models ◽  
Diagnostic Tests ◽  
Fixed Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Models ◽  
Characteristic Curve ◽  
Linear Mixed Models ◽  
Parametric Bootstrap ◽  
Pseudo Likelihood

Diagnostic tests are often compared in multi-reader multi-case (MRMC) studies in which a number of cases (subjects with or without the disease in question) are examined by several readers using all tests to be compared. One of the commonly used methods for analyzing MRMC data is the Obuchowski–Rockette (OR) method, which assumes that the true area under the receiver operating characteristic curve (AUC) for each combination of reader and test follows a linear mixed model with fixed effects for test and random effects for reader and the reader–test interaction. This article proposes generalized linear mixed models which generalize the OR model by incorporating a range-appropriate link function that constrains the true AUCs to the unit interval. The proposed models can be estimated by maximizing a pseudo-likelihood based on the approximate normality of AUC estimates. A Monte Carlo expectation-maximization algorithm can be used to maximize the pseudo-likelihood, and a non-parametric bootstrap procedure can be used for inference. The proposed method is evaluated in a simulation study and applied to an MRMC study of breast cancer detection.

Does Sample Attrition Affect the Assessment of Frailty Trajectories Among Older Adults? A Joint Model Approach

Gerontology ◽  
2018 ◽  
Vol 64 (5) ◽  
pp. 430-439 ◽  
Author(s):  
Erwin Stolz ◽  
Hannes Mayerl ◽  
Éva Rásky ◽  
Wolfgang Freidl
Keyword(s):  
Older Adults ◽  
Mixed Models ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Linear Mixed Models ◽  
Joint Model ◽  
Joint Models ◽  
Sample Attrition ◽  
Standard Linear ◽  
The Impact

Background: Frailty constitutes an important risk factor for adverse outcomes among older adults. In longitudinal studies on frailty, selective sample attrition may threaten the validity of results. Objective: To assess the impact of sample attrition on frailty index trajectories and gaps related to socio-economic status (education) therein among older adults in Europe. Methods: A total of 64,143 observations from 21,044 respondents (50+) from the Survey of Health, Ageing and Retirement in Europe across 12 years of follow-up (2004–2015) and subject to substantial sample attrition (59%) were analysed. We compared results of a standard linear mixed model assuming missing at random (MAR) sample attrition with a joint model assuming missing not at random sample attrition. Results: Estimated frailty trajectories of both the mixed and joint models were identical up to an age of 80 years, above which modest underestimation occurred when a standard linear mixed model was used rather than a joint model. The latter effect was larger for men than women. Substantial education-based inequality in frailty continued throughout old age in both the mixed and joint models. Conclusion: Linear mixed models assuming MAR sample attrition provided good estimates of frailty trajectories up until high age. Thus, the validity of existing studies estimating frailty trajectories based on standard linear mixed models seems not threatened by substantial sample attrition.

scholarly journals Prediction of Friction Degradation in Highways with Linear Mixed Models

Coatings ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 187
Author(s):  
Adriana Santos ◽  
Elisabete F. Freitas ◽  
Susana Faria ◽  
Joel R. M. Oliveira ◽  
Ana Maria A. C. Rocha
Keyword(s):  
Mixed Models ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Linear Mixed Models ◽  
Pavement Management ◽  
Climate Conditions ◽  
Pavement Management Systems ◽  
Geometrical Features ◽  
Highway Network ◽  
Network Manager

The development of a linear mixed model to describe the degradation of friction on flexible road pavements to be included in pavement management systems is the aim of this study. It also aims at showing that, at the network level, factors such as temperature, rainfall, hypsometry, type of layer, and geometric alignment features may influence the degradation of friction throughout time. A dataset from six districts of Portugal with 7204 sections was made available by the Ascendi Concession highway network. Linear mixed models with random effects in the intercept were developed for the two-level and three-level datasets involving time, section and district. While the three-level models are region-specific, the two-level models offer the possibility to be adopted to other areas. For both levels, two approaches were made: One integrating into the model only the variables inherent to traffic and climate conditions and the other including also the factors intrinsic to the highway characteristics. The prediction accuracy of the model was improved when the variables hypsometry, geometrical features, and type of layer were considered. Therefore, accurate predictions for friction evolution throughout time are available to assist the network manager to optimize the overall level of road safety.

scholarly journals Ludicrous Speed Linear Mixed Models for Genome-Wide Association Studies

2017 ◽  
Author(s):  
Carl Kadie ◽  
David Heckerman
Keyword(s):  
Mixed Models ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Linear Mixed Models ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Confounding Factors ◽  
Genome Wide ◽  
A Genome

AbstractWe have developed Ludicrous Speed Linear Mixed Models, a version of FaST-LMM optimized for the cloud. The approach can perform a genome-wide association analysis on a dataset of one million SNPs across one million individuals at a cost of about 868 CPU days with an elapsed time on the order of two weeks. A Python implementation is available at https://fastlmm.github.io/.SignificanceIdentifying SNP-phenotype correlations using GWAS is difficult because effect sizes are so small for common, complex diseases. To address this issue, institutions are creating extremely large cohorts with sample sizes on the order of one million. Unfortunately, such cohorts are likely to contain confounding factors such as population structure and family/cryptic relatedness. The linear mixed model (LMM) can often correct for such confounding factors, but is too slow to use even with algebraic speedups known as FaST-LMM. We present a cloud implementation of FaST-LMM, called Ludicrous Speed LMM, that can process one million samples and one million test SNPs in a reasonable amount of time and at a reasonable cost.

scholarly journals Modeling the longitudinal outcomes of congestive heart failure patients: A case study at Wachemo University Nigist Eleni Mohammed Memorial Referral Hospital

2021 ◽  
Author(s):  
Mohammed Sultan ◽  
Ritbano Ahmed
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Longitudinal Data ◽  
Mixed Models ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Linear Mixed Models ◽  
Covariance Structure ◽  
Repeated Measurements ◽  
Within Subjects

Abstract The linear mixed model is one of the common models used to analyze the longitudinal data;it may comprise of separate (Univariate), joint Bivariate, and joint Multivariate linear mixed model, which is predicted on the number of response variables incorporated in the analysis. Adjusting for correlation matrix and covariance matrix between and within subjects is one reason why modern longitudinal data analysis techniques are deemed more appropriate than some of the previous methods of analysis. Some studies assume that the correlation between observation is zero. However, it is unlikely that repeated measurements on the same individual Will actually be independent. To that end, comparing the different linear mixed models identifying the appropriate model demonstrates that the evolution of patients with congestive heart failure is necessary.In this study the separate, bivariate, and multivariate linear mixed models were compared with different covariance and correlation structures. Finally, a multivariate linear mixed model with autoregressive order one correlation structure and unstructured covariance structure for random effects, to consider within and between patient's variations, was considered as a best model to depict the evolution of patients with congestive heart failure.

scholarly journals Lossless Distributed Linear Mixed Model with Application to Integration of Heterogeneous Healthcare Data

2020 ◽  
Author(s):  
Chongliang Luo ◽  
Md. Nazmul Islam ◽  
Natalie E. Sheils ◽  
Jenna M Reps ◽  
John Buresh ◽  
...  
Keyword(s):  
Mixed Models ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Individual Patient Data ◽  
Linear Mixed Models ◽  
Administrative Claims ◽  
Research Database ◽  
Aggregated Data ◽  
Healthcare Data ◽  
Sensitive Individual

Linear mixed models (LMMs) are commonly used in many areas including epidemiology for analyzing multi-site data with heterogeneous site-specific random effects. However, due to the regulation of protecting patients' privacy, sensitive individual patient data (IPD) are usually not allowed to be shared across sites. In this paper we propose a novel algorithm for distributed linear mixed models (DLMMs). Our proposed DLMM algorithm can achieve exactly the same results as if we had pooled IPD from all sites, hence the lossless property. The DLMM algorithm requires each site to contribute some aggregated data (AD) in only one iteration. We apply the proposed DLMM algorithm to analyze the association of length of stay of COVID-19 hospitalization with demographic and clinical characteristics using the administrative claims database from the UnitedHealth Group Clinical Research Database.

scholarly journals Modeling the Pulse Rate, Respiratory Rate, and Weight of Congestive Heart Failure Patients: A Case Study at Wachemo University Nigist Eleni Mohammed Memorial Referral Hospital

2021 ◽  
Author(s):  
Mohammed Sultan ◽  
Ritbano Ahmed Abdo
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Mixed Models ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Covariance Structure ◽  
Likelihood Estimation ◽  
Standard Errors

Abstract Background: The linear mixed model is one of the common models used to analyze the longitudinal data; it may comprise of Separate (Univariate), joint Bivariate or joint multivariate linear mixed model, which is predicated on the number of response variables incorporated in the analysis. Adjusting for correlation matrix and covariance matrix between and within subjects is one reason why modern longitudinal data analysis techniques are deemed more appropriate than some of the previous methods of analysis. Some studies assume that the correlation between observations is zero. However, it is unlikely that repeated measurements on the same individual will actually be independent. To that end, comparing the different linear mixed models and identifying the appropriate model demonstrates the evolution of patients with CHF.Methods: In this study the separate, bivariate and multivariate linear mixed models were analyzed with different covariance and correlation structures. The parameters in the models were estimated by maximum likelihood estimation and restricted maximum likelihood estimation techniques. The models were compared by AIC, BIC, and Log-likelihood ratio test. Results: The models with unstructured covariance structure for random effects and autoregressive order one for serial correlation structure had small AIC, BIC and -2LL and standard errors. Separate models had high AIC, BIC and -2LL and standard errors than bivariate and multivariate had small AIC, BIC and -2LL and standard errors than all models. Conclusions: Finally, a multivariate linear mixed model with autoregressive order one correlation structure and unstructured covariance structure for random effects, to consider within and between patients’ variations, was considered as the best model to depict the evolution of patients with congestive heart failure.

scholarly journals Identification of genetic loci affecting body mass index through interaction with multiple environmental factors using structured linear mixed model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hae-Un Jung ◽  
Won Jun Lee ◽  
Tae-Woong Ha ◽  
Ji-One Kang ◽  
Jihye Kim ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Environmental Factors ◽  
Body Mass ◽  
Bayes Factor ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Interaction Analysis ◽  
Carbohydrate Intake ◽  
Calorie Intake ◽  
Environment Interaction

AbstractMultiple environmental factors could interact with a single genetic factor to affect disease phenotypes. We used Struct-LMM to identify genetic variants that interacted with environmental factors related to body mass index (BMI) using data from the Korea Association Resource. The following factors were investigated: alcohol consumption, education, physical activity metabolic equivalent of task (PAMET), income, total calorie intake, protein intake, carbohydrate intake, and smoking status. Initial analysis identified 7 potential single nucleotide polymorphisms (SNPs) that interacted with the environmental factors (P value < 5.00 × 10−6). Of the 8 environmental factors, PAMET score was excluded for further analysis since it had an average Bayes Factor (BF) value < 1 (BF = 0.88). Interaction analysis using 7 environmental factors identified 11 SNPs (P value < 5.00 × 10−6). Of these, rs2391331 had the most significant interaction (P value = 7.27 × 10−9) and was located within the intron of EFNB2 (Chr 13). In addition, the gene-based genome-wide association study verified EFNB2 gene significantly interacting with 7 environmental factors (P value = 5.03 × 10−10). BF analysis indicated that most environmental factors, except carbohydrate intake, contributed to the interaction of rs2391331 on BMI. Although the replication of the results in other cohorts is warranted, these findings proved the usefulness of Struct-LMM to identify the gene–environment interaction affecting disease.

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