• Corpus ID: 88514527

Extended multivariate generalised linear and non-linear mixed effects models

@article{Crowther2017ExtendedMG,
  title={Extended multivariate generalised linear and non-linear mixed effects models},
  author={Michael J. Crowther},
  journal={arXiv: Methodology},
  year={2017}
}
  • M. Crowther
  • Published 5 October 2017
  • Mathematics
  • arXiv: Methodology
Multivariate data occurs in a wide range of fields, with ever more flexible model specifications being proposed, often within a multivariate generalised linear mixed effects (MGLME) framework. In this article, we describe an extended framework, encompassing multiple outcomes of any type, each of which could be repeatedly measured (longitudinal), with any number of levels, and with any number of random effects at each level. Many standard distributions are described, as well as non-standard user… 

Tables from this paper

merlin: An R package for Mixed Effects Regression for Linear, Nonlinear and User-defined models
TLDR
The R package merlin performs flexible joint modelling of hierarchical multi-outcome data and allows for the estimation of models with unlimited numbers of continuous, binary, count and time-to-event outcomes, with unlimited levels of nested random effects.
Multilevel mixed-effects parametric survival analysis: Estimation, simulation, and application
TLDR
The community-contributed stmixed command serves as both an alternative to Stata’s official mestreg command and a complimentary command with substantial extensions for fitting multilevel survival models.
Multivariate Generalized Linear Mixed-Effects Models for the Analysis of Clinical Trial–Based Cost-Effectiveness Data
TLDR
The generalized linear mixed-model framework is extended to enable simultaneous modeling of multiple outcomes of mixed data types, such as those typically encountered in trial-based economic evaluations, taking into account correlation of outcomes due to repeated measurements on the same individual and other clustering effects.
merlin—A unified modeling framework for data analysis and methods development in Stata
The challenges in statistics and data science are rapidly growing because access to a multitude of data types continues to increase, as well as the sheer quantity of data. Analysts are now presented
Relaxing the assumption of constant transition rates in a multi-state model in hospital epidemiology
TLDR
It is concluded that methods and software are readily available for obtaining predictions from multi-state models that do not assume constant transition rates and the multistate package in Stata facilitates a range of predictions with confidence intervals, which can provide a more comprehensive understanding of the data.
Mixed‐effects models for health care longitudinal data with an informative visiting process: A Monte Carlo simulation study
TLDR
Through Monte Carlo simulation, this work compares different analytical approaches proposed to account for an informative visiting process to assess whether they lead to unbiased results and formalises a joint model for the observation process and the longitudinal outcome within an extended joint modelling framework.
Assessing and relaxing the Markov assumption in the illness-death model
Multi-state survival analysis considers several potential events of interest along a disease pathway. Such analyses are crucial to model complex patient trajectories and are increasingly being used
Comparison of statistical methods for the analysis of recurrent adverse events in the presence of non-proportional hazards and unobserved heterogeneity: a simulation study
TLDR
This work investigated whether the lognormal shared frailty models with restricted cubic splines and non-proportional hazards (LSF-NPH) assumption can improve estimates for both frailty variance and treatment effect compared to the conventional inverse Gaussian share frailty model with proportional hazard (ISF-PH), in the presence of time-dependent treatment effects and unobserved patient heterogeneity.
Measurement Error as a Missing Data Problem
TLDR
The link between measurement error and missing data is made and methods for correcting for bias due to covariate measurement error with reference to this link are described, including regression calibration, maximum likelihood and Bayesian methods, and multiple imputation.
Joint modelling of multivariate longitudinal clinical laboratory safety outcomes, concomitant medication and clinical adverse events: application to artemisinin-based treatment during pregnancy clinical trial
TLDR
Although the AEs did not vary across the treatments, the joint model yielded efficient AE incidence estimates compared to the Poisson model and showed a positive relationship between the AE and concomitant medication but not with laboratory outcomes.
...
1
2
...

References

SHOWING 1-10 OF 36 REFERENCES
Multilevel models with multivariate mixed response types
TLDR
A class of models for multivariate mixtures of Gaussian, ordered or unordered categorical responses and continuous distributions that are not Gaussian is built upon, each of which can be defined at any level of a multilevel data hierarchy.
Statistical inference in generalized linear mixed models: a review.
TLDR
A review of statistical inference in generalized linear mixed models (GLMMs) and an overview of available methods for testing hypotheses about the parameters of GLMMs, which are suitable for the analysis of non-normal data with a clustered structure.
Generalized multilevel structural equation modeling
TLDR
Maximum likelihood estimation and empirical Bayes latent score prediction within the GLLAMM framework can be performed using adaptive quadrature in gllamm, a freely available program running in Stata.
Multilevel mixed effects parametric survival models using adaptive Gauss–Hermite quadrature with application to recurrent events and individual participant data meta‐analysis
Multilevel mixed effects survival models are used in the analysis of clustered survival data, such as repeated events, multicenter clinical trials, and individual participant data (IPD)
Nonlinear mixed-effects models for pharmacokinetic data analysis: assessment of the random-effects distribution
  • Reza Drikvandi
  • Mathematics
    Journal of Pharmacokinetics and Pharmacodynamics
  • 2017
TLDR
The gradient function is investigated and its performance for such nonlinear mixed-effects models which are common in pharmacokinetics and pharmacodynamics is evaluated.
Approximations to the Log-Likelihood Function in the Nonlinear Mixed-Effects Model
Abstract Nonlinear mixed-effects models have received a great deal of attention in the statistical literature in recent years because of the flexibility they offer in handling the unbalanced
Reliable Estimation of Generalized Linear Mixed Models using Adaptive Quadrature
TLDR
A multilevel version ofthismethodin gllamm is implemented, a program that fits a large class of multileVEL latent variable models including multilesvel generalized linear mixed models, and it is shown that adaptive quadrature works well in problems where ordinary quadratures fails.
MIXREGLS: A Program for Mixed-Effects Location Scale Analysis.
TLDR
MIXREGLS is a program which provides estimates for a mixed-effects location scale model assuming a (conditionally) normally-distributed dependent variable and uses maximum likelihood estimation, utilizing both the EM algorithm and a Newton-Raphson solution.
Multilevel growth curve models that incorporate a random coefficient model for the level 1 variance function
Aim To present a flexible model for repeated measures longitudinal growth data within individuals that allows trends over time to incorporate individual-specific random effects. These may reflect the
Nested frailty models using maximum penalized likelihood estimation
TLDR
A maximum penalized likelihood estimation (MPnLE) is presented to estimate non-parametrically a continuous hazard function in a nested gamma-frailty model with right-censored and left-truncated data and it is illustrated that this semi-parametric approach yields satisfactory results in this complex setting.
...
1
2
3
4
...