scholarly journals Comparison of Generalized Linear Model and Generalized Linear Mixed Model – An Application to Low Birth Weight Data

2020 ◽  
pp. 31-37
Author(s):  
Michael Fosu Ofori ◽  
Stephen B. Twum ◽  
Jackson A. Y. Osborne
Keyword(s):  
Linear Model ◽  
Generalized Linear Models ◽  
Generalized Linear Model ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Linear Models ◽  
Generalized Linear Mixed Model ◽  
Correlated Data ◽  
Parameter Estimates ◽  
Over Dispersion

Background: Generalized Linear models are mostly fitted to data that are not correlated. However, very often data that are collected from health and epidemiological studies are correlated either as a result of the sampling methods or the randomness associated with the collection of such data. Therefore, fitting generalized linear models to such data that produce only fixed effects could lead to over dispersion in the model estimates. Objectives: The objective of this study is to fit both generalized linear and generalized linear mixed models to a correlated data and compare the results of the two models. Methods: Logistic regression is employed in fitting the generalized linear model since the dependent variable in the study is bivariate whilst the GLIMMIX model in SAS is used to fit the generalized linear mixed model. Results: The generalized linear model produces over dispersion with higher errors among the parameter estimates than the generalized linear mixed model. Conclusion: In dealing with a more correlated data, generalized linear mixed model, which can handle both fixed and random effects, is preferable to generalized linear model.

scholarly journals Non-linear Mixed Models in a Dose Response Modelling

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Madona Yunita Wijaya
Keyword(s):  
Linear Model ◽  
Dose Response ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Linear Models ◽  
Growth Curves ◽  
Limiting Factor ◽  
Dose Time ◽  
Non Linear ◽  
Diffusion Function

AbstractStudy designs in which an outcome is measured more than once from time to time result in longitudinal data. Most of the methodological works have been done in the setting of linear and generalized linear models, where some amount of linearity is retained. However, this still be considered a limiting factor and non-linear models is another option offering its flexibility. Non-linear model involves complexity of non-linear dependence on parameters than that in the generalized linear class. It has been utilized in many situations such as modeling of growth curves and dose-response modeling. The latter modeling will be the main interest in this study to construct a dose-response relationship, as a function of time to IBD (inflammatory bowel disease) dataset. The dataset comes from a clinical trial with 291 subjects measured during a 7 week period. Both linear and non-linear models are considered. A dose time response model with generalized diffusion function is utilized for the non-linear models. The fit of non-linear models are found to be more flexible than linear models hence able to capture more variability present in the data.Keywords: IBD; longitudinal; linear mixed model; non-linear mixed model. AbstrakDesain studi dimana hasil diukur berulang kali dari waktu ke waktu menghasilkan data longitudinal. Sebagian besar metodologi yang digunakan untuk menganalisis data longitudinal adalah model linear dan model linear umum (generalized linear model) dimana sejumlah linearitas tertentu dipertahankan. Asumsi linearitas ini masih dipandang memiliki keterbatasan dan model non-linear adalah pilihan metode lainnya yang menawarkan fleksibilitas. Model non-linear telah digunakan di berbagai macam situasi seperti model kurva pertumbuhan , model farmakokinetika, dan farmakodinamika, dan model respon-dosis. Model respon-dosis akan menjadi fokus dalam penelitian ini untuk membangun hubungan dosis-respon sebagai fungsi waktu dari data IBD dengan menggunakan model linear dan non-linear. Hasil penelitian menunjukan bahwa model non-linear lebih fleksibel daripada model linear sehingga mampu menangkap lebih banyak variabilitas yang ada di dalam data.Keywords: IBD; longitudinal; model linear; model non-linear.

scholarly journals Instantaneous Geometric Rates via Generalized Linear Models

2017 ◽  
Vol 17 (2) ◽  
pp. 358-371 ◽  
Author(s):  
Andrea Discacciati ◽  
Matteo Bottai
Keyword(s):  
Clinical Trial ◽  
Randomized Clinical Trial ◽  
Linear Model ◽  
Generalized Linear Models ◽  
Generalized Linear Model ◽  
Renal Carcinoma ◽  
Linear Models ◽  
Rate Model ◽  
Model Framework ◽  
Link Functions

The instantaneous geometric rate represents the instantaneous probability of an event of interest per unit of time. In this article, we propose a method to model the effect of covariates on the instantaneous geometric rate with two models: the proportional instantaneous geometric rate model and the proportional instantaneous geometric odds model. We show that these models can be fit within the generalized linear model framework by using two nonstandard link functions that we implement in the user-defined link programs log_igr and logit_igr. We illustrate how to fit these models and how to interpret the results with an example from a randomized clinical trial on survival in patients with metastatic renal carcinoma.

Analyzing discontinuities in longitudinal count data: A multilevel generalized linear mixed model.

2020 ◽  
Author(s):  
James L. Peugh ◽  
Sarah J. Beal ◽  
Meghan E. McGrady ◽  
Michael D. Toland ◽  
Constance Mara
Keyword(s):  
Count Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Longitudinal Count Data

scholarly journals Reducing Aggression by Developing Emotional and Inhibitory Control

2021 ◽  
Vol 18 (10) ◽  
pp. 5263
Author(s):  
Miriam Romero-López ◽  
María Carmen Pichardo ◽  
Ana Justicia-Arráez ◽  
Judit Bembibre-Serrano
Keyword(s):  
Psychosocial Development ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Emotional Control ◽  
Control Group ◽  
Post Intervention ◽  
Mixed Model Analysis ◽  
Intervention Measures ◽  
Experimental Group

The objective of this study is to measure the effectiveness of a program on improving inhibitory and emotional control among children. In addition, it is assessed whether the improvement of these skills has an effect on the reduction of aggressive behavior in pre-school children. The participants were 100 children, 50 belonging to the control group and 50 to the experimental group, aged between 5 and 6 years. Pre-intervention and post-intervention measures of inhibitory and emotional control (BRIEF-P) and aggression (BASC) were taken. A Generalized Linear Mixed Model analysis (GLMM) was performed and found that children in the experimental group scored higher on inhibitory and emotional control compared to their peers in the control group. In addition, these improvements have an effect on the decrease in aggressiveness. In conclusion, preventive research should have among its priorities the design of such program given their implications for psychosocial development.

scholarly journals Changes in Dairy Cow Behavior with and without Assistance at Calving

Agriculture ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 722
Author(s):  
Bethan Cavendish ◽  
John McDonagh ◽  
Georgios Tzimiropoulos ◽  
Kimberley R. Slinger ◽  
Zoë J. Huggett ◽  
...  
Keyword(s):  
Dairy Cows ◽  
Video Surveillance ◽  
Dairy Cow ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Behavioral Data ◽  
Behavioral Patterns ◽  
Holstein Friesian ◽  
Time Periods

The aim of this study was to characterize calving behavior of dairy cows and to compare the duration and frequency of behaviors for assisted and unassisted dairy cows at calving. Behavioral data from nine hours prior to calving were collected for 35 Holstein-Friesian dairy cows. Cows were continuously monitored under 24 h video surveillance. The behaviors of standing, lying, walking, shuffle, eating, drinking and contractions were recorded for each cow until birth. A generalized linear mixed model was used to assess differences in the duration and frequency of behaviors prior to calving for assisted and unassisted cows. The nine hours prior to calving was assessed in three-hour time periods. The study found that the cows spent a large proportion of their time either lying (0.49) or standing (0.35), with a higher frequency of standing (0.36) and shuffle (0.26) bouts than other behaviors during the study. There were no differences in behavior between assisted and unassisted cows. During the three-hours prior to calving, the duration and bouts of lying, including contractions, were higher than during other time periods. While changes in behavior failed to identify an association with calving assistance, the monitoring of behavioral patterns could be used as an alert to the progress of parturition.

A generalized linear mixed model association tool for biobank-scale data

2021 ◽  
Author(s):  
Longda Jiang ◽  
Zhili Zheng ◽  
Hailing Fang ◽  
Jian Yang
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Scale Data

Mixed Generalized Linear Model for Estimating Household Trip Production

2000 ◽  
Vol 1718 (1) ◽  
pp. 61-67 ◽  
Author(s):  
Chang-Jen Lan ◽  
Patricia S. Hu
Keyword(s):  
Linear Model ◽  
Generalized Linear Model ◽  
Negative Binomial ◽  
Empirical Distribution ◽  
Distribution Functions ◽  
Model Specification ◽  
Parameter Estimates ◽  
Modeling Framework ◽  
Personal Transportation ◽  
Trip Production

An innovative modeling framework to estimate household trip rates using 1995 Nationwide Personal Transportation Survey data is presented. A generalized linear model with a mixture of negative binomial probability distribution functions was developed on the basis of characteristics observed from the empirical distribution of household daily trips. This model provides a more flexible framework and a better model specification for analyzing household-specific trip production behavior. Compared with traditional least squares-based regression models, the parameter estimates from the proposed model are more efficient. Although the mean accuracies from the two modeling approaches are comparable, the mixed generalized linear model is more robust in identifying outliers due to its unsymmetric prediction bounds derived from more correct model specification.

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