scholarly journals Extensions to Bayesian generalized linear mixed effects models for household tuberculosis transmission

2017 ◽  
Vol 36 (16) ◽  
pp. 2522-2532 ◽  
Author(s):  
Avery I. McIntosh ◽  
Gheorghe Doros ◽  
Edward C. Jones-López ◽  
Mary Gaeddert ◽  
Helen E. Jenkins ◽  
...  
Keyword(s):  
Mixed Effects ◽  
Mixed Effects Models ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Tuberculosis Transmission ◽  
Generalized Linear Mixed Effects

scholarly journals Construction of the Design Matrix for Generalized Linear Mixed-Effects Models in the Context of Clinical Trials of Treatment Sequences

2018 ◽  
Vol 41 (2) ◽  
pp. 191-233 ◽  
Author(s):  
Francisco J. Diaz
Keyword(s):  
Clinical Trials ◽  
Data Analysis ◽  
Full Rank ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Design Matrix ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Generalized Linear Mixed Effects ◽  
Treatment Sequences

The problem of constructing a design matrix of full rank for generalized linear mixed-effects models (GLMMs) has not been addressed in statistical literature in the context of clinical trials of treatment sequences. Solving this problem is important because the most popular estimation methods for GLMMs assume a design matrix of full rank, and GLMMs are useful tools in statistical practice. We propose new developments in GLMMs that address this problem. We present a new model for the design and analysis of clinical trials of treatment sequences, which utilizes some special sequences called skip sequences. We present a theorem showing that estimators computed through quasi-likelihood, maximum likelihood or generalized least squares, or through robust approaches, exist only if appropriate skip sequences are used. We prove theorems that establish methods for implementing skip sequences in practice. In particular, one of these theorems computes the necessary skip sequences explicitly. Our new approach allows building design matrices of full rank and facilitates the implementation of regression models in the experimental design and data analysis of clinical trials of treatment sequences. We also explain why the standard approach to constructing dummy variables is inappropriate in studies of treatment sequences. The methods are illustrated with a data analysis of the STAR*D study of sequences of treatments for depression.

Where Should We Eat? Lunch Source and Dietary Measures Among Youth During the School Week

2015 ◽  
Vol 76 (4) ◽  
pp. 157-165 ◽  
Author(s):  
Amanda C. Jones ◽  
David Hammond ◽  
Jessica L. Reid ◽  
Scott T. Leatherdale
Keyword(s):  
Fast Food ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
School Grade ◽  
Linear Mixed Effects Models ◽  
Sugar Sweetened Beverage ◽  
Linear Mixed Effects ◽  
White Ethnicity ◽  
School Week ◽  
Generalized Linear Mixed Effects

Purpose: To examine lunch sources during the school week among students and the associations with fruits and vegetable (F&V) and sugar-sweetened beverage (SSB) consumption. Methods: Students (n = 23 680) from 43 Ontario, Canada, secondary schools completed a health behaviour survey in the Year 1 COMPASS study. Analysis used generalized linear mixed effects models. Results: The most frequently reported lunch source was home (2.9 days per school week), then the school cafeteria (1.1) and fast-food places or restaurants (FFRs) (0.9). Eating a home lunch was associated with having less spending money, white ethnicity, and females; whereas cafeteria lunch was associated with more spending money, lower school grade, and females. A FFR lunch was associated with males, more spending money, and higher physical activity. Greater frequency of a home lunch was associated with greater F&V consumption. Greater frequency of a FFR lunch was associated with more frequent SSB consumption. Cafeteria lunches were associated with increases in both SSB and F&V. Conclusions: Eating a lunch obtained from outside of the home is a regular behaviour among students. Sources of school-week lunches may have an important influence on dietary intake among youth. These findings reinforce the need for strategies to promote healthier lunch sources and healthier food options.

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