[PDF] Top 20 Mixed effects models for joint modeling of sequence data in longitudinal studies
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Mixed effects models for joint modeling of sequence data in longitudinal studies
... that mixed-effects models pro- vided a powerful tool for longitudinal data analysis accounting for nested data structure, timedependence within repeated measures, and confounding ... See full document
5
Joint modeling of multivariate longitudinal data and survival data in several observational studies of Huntington’s disease
... candidate models, and there are a number of measures that can be used for Bayesian model ...hazards modeling, AUC has been shown to be relatively insensitive to model differences, unless the effect sizes ... See full document
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Köhler, Meike (2017): Flexible Bayesian joint models for longitudinal biomarkers and time-to-event outcomes with applications to type 1 diabetes research. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik
... T1D studies and data from two cohorts, the presented combined German BABYDIAB/BABYDIET cohort as well as a multinational ...the data set considered in the application in Section ...small data ... See full document
166
Evaluating associations between the benefits and risks of drug therapy in type 2 diabetes: a joint modeling approach
... that joint modeling can be a useful approach for evaluating associations between the benefits and risks of drug ...Using joint models for longitudinal and time- to-event data, we ... See full document
9
Gene analysis for longitudinal family data using random effects models
... the joint effect of single-nucleotide polymorph- isms (SNPs) located in a gene is a popular alternative to single-marker ...genetic data is reduced, and gene-specific summaries are produced, and (b) these ... See full document
5
q2-longitudinal: Longitudinal and Paired-Sample Analyses of Microbiome Data
... ABSTRACT Studies of host-associated and environmental microbiomes often incor- porate longitudinal sampling or paired samples in their experimental ...dinal studies, we developed ... See full document
9
<p>Bayesian Joint Modeling of Longitudinal and Survival Time Measurement of Hypertension Patients</p>
... linear mixed effect model was assumed for the longitudinal process, while exponential, Weibull, lognormal, and loglogistic distributions were assumed for the survival ...the data analyzed using ... See full document
9
Flexible linear mixed models with improper priors for longitudinal and survival data
... frontier models), alternative distributional assumptions have been explored, such as smooth flexible (semi-nonparametric) distributions for the random effects and normal residual errors [Zhang and Davidian, ... See full document
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Semiparametric Mixed Models for Censored Longitudinal Data.
... regression models for longitudinal data ...stochastic mixed model that incorporates a general random effects term as well as a stationary or nonstationary process to allow for more ... See full document
99
Missingness Mechanism that Incorporated Joint Modeling of Longitudinal Data with Monotone Dropout
... selection models to the ARMD data by combining the measurement model with the logistic regression for dropout model, in line with [5] using a generic function maximization ...treatments effects for ... See full document
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Semiparametric approaches to inference in joint models for longitudinal and time-to-event data
... certain longitudinal data model parameters under the assumption of normality results in estimators that are apparently approximately unbiased and as efficient as those that accommodate nonnormality even ... See full document
97
Joint modeling of multivariate longitudinal data and the dropout process in a competing risk setting: application to ICU data
... Such joint models in this particular setting required modelling ...piecewise mixed-effects model could have been ...the data, due to the absence of any time points after day 7 except at ... See full document
13
Joint outcome modeling using shared frailties with application to temporal streamflow data
... for joint anal- ysis of longitudinal data and time-to-event ...clinical studies, where longitudinal measurements on a response may be recorded along with a time-to-event ...outcome. ... See full document
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Efficient two-step multivariate random effects meta-analysis of individual participant data for longitudinal clinical trials using mixed effects models
... the mixed effects models that adequately address the between-studies heterogen- eity using random effects ...trial data sharing ...simulation studies and a real ... See full document
12
Mixed effects models for GAW18 longitudinal blood pressure data
... association studies (GWAS) have been used for examining genetic variants associated with blood pressure and hypertension ...genetic effects. Genetic Analysis Workshop 18 (GAW18) data included ... See full document
5
Bayesian Joint Modelling of Disease Progression Marker and Time to Death Event of HIV/AIDS Patients under ART Follow-up
... for joint models is quite limited. In practice, the best longitudinal model can be selected based on the analysis of observed longitudinal data, and the best survival model can be ... See full document
10
Performance of mixed effects models in the analysis of mediated longitudinal data
... to longitudinal data analyses [4-9], and the equivalence of LMMs and special cases of SEMs in set- tings without mediating variables has been well docu- mented in the SEM literature ...explicit ... See full document
11
Analysis of repeated measurements with missing data
... selection models are highly sensitive to parametric assumptions and that the identifiability problem is ‘masked’ [Molenberghs and Kenward, 2007] has led some researchers to avoid such ...all models for ... See full document
227
Non-linear mixed models in the analysis of mediated longitudinal data with binary outcomes
... Real data example: alcohol and HIV disease progression To demonstrate the application of both the logit and probit NLMMs and SEMs evaluated in the simulation study, we analyzed data from a prospective ... See full document
10
The prediction accuracy of dynamic mixed-effects models in clustered data
... prediction models in clinical research and medical practice, there are often major concerns about model generalizability across different populations and clinical ...of data from a small number of clusters ... See full document
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