Generalized linear mixed models (GLMMs) estimate fixed and random effects and are
especially useful when the dependent variable is binary, ordinal, count or quantitative but …

This book unifies and extends latent variable models, including multilevel or generalized
linear mixed models, longitudinal or panel models, item response or factor models, latent …

Background: Financial incentive designs to increase physical activity have not been well-
examined. Objective: To test the effectiveness of 3 methods to frame financial incentives to …

Introduction Distracted driving has been well researched, however the comparison between
different age-gender groups on the impact of distracted driving has not been explored. Most …

Maximum likelihood estimates in GLMM are often difficult to be obtained since the
calculation involves high dimensional integrals. It is not easy to find analytical solutions for …

Analyzing longitudinal data in health studies is challenging due to sparse and error‐prone
measurements, strong within‐individual correlation, missing data and various trajectory …

Statistical inference for generalized linear mixed models (GLMM) is highly challenging
because the marginalized likelihood may involve analytically intractable integrals. In this …

Background The attained power, calculated conditional on the realized allocation, of a
clinical trial may differ from the expected power, obtained pre-randomization through …

We describe how to approximate the intractable marginal likelihood that arises when fitting
generalized linear mixed models. We prove that non-adaptive quadrature approximations …

Objective Neighbourhood deprivation increases the risk of colorectal neoplasia and
contributes to racial disparities observed in this disease. Developing race-specific advanced …