An application of the mixed‐effects model and pattern mixture model to treatment groups with differential missingness suspected not‐missing‐at‐random

2020 ◽  
Author(s):  
Masahiko Gosho ◽  
Kazushi Maruo
Keyword(s):  
Mixture Model ◽  
Missing At Random ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Treatment Groups ◽  
Pattern Mixture Model ◽  
Not Missing At Random

Conditionally Linear Mixed-Effects Models With Latent Variable Covariates

1999 ◽  
Vol 24 (3) ◽  
pp. 245-270 ◽  
Author(s):  
Shelley A. Blozis ◽  
Robert Cudeck
Keyword(s):  
Random Effects ◽  
Repeated Measures ◽  
Latent Variable ◽  
Missing At Random ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Variable Model ◽  
Nonlinear Mixed Effects Model ◽  
Linear Mixed Effects ◽  
Model Features

A version of the nonlinear mixed-effects model is presented that allows random effects only on the linear coefficients. Nonlinear parameters are not stochastic. In nonlinear regression, this kind of model has been called conditionally linear. As a mixed-effects model, this structure is more flexible than the popular linear mixed-effects model, while being nearly as straightforward to estimate. In addition to the structure for the repeated measures, a latent variable model ( Browne, 1993 ) is specified for a distinct set of covariates that are related to the random effects in the second level. Unbalanced data are allowed on the repeated measures, and data that are missing at random are allowed on the repeated measures or on the observed variables of the factor analysis sub-model. Features of the model are illustrated by two examples.

scholarly journals Continuous(ly) missing outcome data in network meta-analysis: A one-stage pattern-mixture model approach

2021 ◽  
pp. 096228022098354
Author(s):  
Loukia M Spineli ◽  
Chrysostomos Kalyvas ◽  
Katerina Papadimitropoulou
Keyword(s):  
Mixture Model ◽  
Meta Analysis ◽  
Missing At Random ◽  
Outcome Data ◽  
Analysis Model ◽  
Continuous Outcome ◽  
Pattern Mixture Model ◽  
One Stage ◽  
Mixture Model Approach ◽  
Model Approach

Appropriate handling of aggregate missing outcome data is necessary to minimise bias in the conclusions of systematic reviews. The two-stage pattern-mixture model has been already proposed to address aggregate missing continuous outcome data. While this approach is more proper compared with the exclusion of missing continuous outcome data and simple imputation methods, it does not offer flexible modelling of missing continuous outcome data to investigate their implications on the conclusions thoroughly. Therefore, we propose a one-stage pattern-mixture model approach under the Bayesian framework to address missing continuous outcome data in a network of interventions and gain knowledge about the missingness process in different trials and interventions. We extend the hierarchical network meta-analysis model for one aggregate continuous outcome to incorporate a missingness parameter that measures the departure from the missing at random assumption. We consider various effect size estimates for continuous data, and two informative missingness parameters, the informative missingness difference of means and the informative missingness ratio of means. We incorporate our prior belief about the missingness parameters while allowing for several possibilities of prior structures to account for the fact that the missingness process may differ in the network. The method is exemplified in two networks from published reviews comprising a different amount of missing continuous outcome data.

scholarly journals A flexible nonlinear mixed effects model for HIV viral load rebound after treatment interruption

2020 ◽  
Vol 39 (15) ◽  
pp. 2051-2066 ◽  
Author(s):  
Rui Wang ◽  
Ante Bing ◽  
Cathy Wang ◽  
Yuchen Hu ◽  
Ronald J. Bosch ◽  
...  
Keyword(s):  
Viral Load ◽  
Treatment Interruption ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Nonlinear Mixed Effects ◽  
Hiv Viral Load ◽  
Nonlinear Mixed Effects Model ◽  
Viral Load Rebound

scholarly journals The state of Spanish /s/ variation in Concepción, Chile: Linguistic and social trends

2020 ◽  
Vol 6 (1) ◽  
pp. 132-153
Author(s):  
Brandon M. A. Rogers
Keyword(s):  
Social Factors ◽  
Central City ◽  
Mixed Effects ◽  
The State ◽  
Mixed Effects Model ◽  
Proportional Odds ◽  
Random Factor ◽  
Social Trends ◽  
Southern Central ◽  
Social Demographics

AbstractThe current study examines /s/ variation in the southern-central city of Concepción, Chile and its relation to a variety of linguistic and social factors. A proportional-odds mixed effects model, with the random factor of “speaker”, was used to treat the categorically coded data on a continuum of acoustical variation ([s] > [h] > ∅). The results presented show that contrary to the previous assertions, heavy sibilant reduction, especially elision, in Concepción, Chile is the rule, rather than the exception, to the extent that it is no longer a marker of certain social demographics as has been reported previously. Furthermore, based on the trends reported, it is likely that this has been the case for several decades. Finally, the overall observed trends are indicative that the rates of /s/ elision will continue to increase across social demographics and different phonetic and phonological contexts in Concepción, Chile.

Estimating local arterial stiffness using mixed-effects model-based residuals: a novel approach

2021 ◽  
Author(s):  
Avinash Chandran ◽  
Derek W. Brown ◽  
Gabriel H. Zieff ◽  
Zachary Y. Kerr ◽  
Daniel Credeur ◽  
...  
Keyword(s):  
Arterial Stiffness ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Model Based ◽  
Novel Approach
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