Generalized linear mixed models provide a flexible framework for modeling a range of data, although with non-Gaussian response variables the likelihood cannot be obtained in closed form. Markov chain Monte Carlo methods solve this problem by sampling from a series of simpler conditional distributions that can be evaluated. The R package MCMCglmm implements such an algorithm for a range of model fitting problems. More than one response variable can be analyzed simultaneously, and these variables are allowed to follow Gaussian, Poisson, multi(bi)nominal, exponential, zero-inflated and censored distributions. A range of variance structures are permitted for the random effects, including interactions with categorical or continuous variables (i.e., random regression), and more complicated variance structures that arise through shared ancestry, either through a pedigree or through a phylogeny. Missing values are permitted in the response variable(s) and data can be known up to some level of measurement error as in meta-analysis. All simu- lation is done in C/ C++ using the CSparse library for sparse linear systems.

https://cran.r-project.org/web//packages/MCMCglmm/vignettes/Overview.pdf

MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package

Meta-analysis in the presence of unexplained heterogeneity is frequently undertaken by using a random-effects model, in which the effects underlying different studies are assumed to be drawn from a normal distribution. Here we discuss the justification and interpretation of such models, by addressing in turn the aims of estimation, prediction and hypothesis testing. A particular issue that we consider is the distinction between inference on the mean of the random-effects distribution and inference on the whole distribution. We suggest that random-effects meta-analyses as currently conducted often fail to provide the key results, and we investigate the extent to which distribution-free, classical and Bayesian approaches can provide satisfactory methods. We conclude that the Bayesian approach has the advantage of naturally allowing for full uncertainty, especially for prediction. However, it is not without problems, including computational intensity and sensitivity to a priori judgements. We propose a simple prediction interval for classical meta-analysis and offer extensions to standard practice of Bayesian meta-analysis, making use of an example of studies of 'set shifting' ability in people with eating disorders.

https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-985X.2008.00552.x

A re-evaluation of random-effects meta-analysis

Missing data in clinical trials can have a major effect on the validity of the inferences that can be drawn from the trial. This article reviews methods for preventing missing data and, failing that, dealing with data that are missing.

/pdf/the-prevention-and-treatment-of-missing-data-in-clinical-gvpp8p81b1.pdf

The Prevention and Treatment of Missing Data in Clinical Trials

Bayesian estimation for 2 groups provides complete distributions of credible values for the effect size, group means and their difference, standard deviations and their difference, and the normality of the data. The method handles outliers. The decision rule can accept the null value (unlike traditional t tests) when certainty in the estimate is high (unlike Bayesian model comparison using Bayes factors). The method also yields precise estimates of statistical power for various research goals. The software and programs are free and run on Macintosh, Windows, and Linux platforms.

/pdf/bayesian-estimation-supersedes-the-t-test-57drp31ai6.pdf

Bayesian Estimation Supersedes the t Test

The Statistics of Canada

Bayesian hierarchical models typically involve specifying prior distributions for one or more variance components. This is rather removed from the observed data, so specification based on expert knowledge can be difficult. While there are suggestions for "default" priors in the literature, often a condi tionally conjugate inverse-gamma specification is used, despite documented drawbacks of this choice. The authors suggest "conservative" prior distributions for variance components, which deliberately give more weight to smaller values. These are appropriate for investigators who are skeptical about the presence of variability in the second-stage parameters (random effects) and want to particularly guard against inferring more structure than is really present. The suggested priors readily adapt to various hierarchical modelling settings, such as fitting smooth curves, modelling spatial variation and combining data from multiple sites. Lois a priori conservatrices pour les parametres de variance de modeles hierarchiques Rgsum6: Les modeles bay6siens hierarchiques comportent g6n6ralement une ou des composantes de va riance que l'on doit doter de lois a priori. Le choix de ces lois est delicat car la variation est un aspect des donn6es difficile a cemer. De toutes les lois a priori "par defaut," une loi conjuguee inverse-gamma con ditionnelle est la plus souvent employ6e, malgr6 ses inconvenients. Les auteurs proposent des lois a priori "conservatrices" pour les composantes de la variance qui privilegient les petites valeurs. Elles conviennent bien aux situations oiu le chercheur s'interroge sur la presence r6elle de variabilit6 dans les parametres de deuxieme degre (effets aleatoires) et qu'il veut eviter d'imposer une structure artificielle. Les lois a priori sugg6rdes s'adaptent A diverses situations propices a la mod6lisation hierarchique, notamment l'ajustement de courbes lisses et la modelisation de variation spatiale ou de donn6es issues de nombreux sites.

Conservative prior distributions for variance parameters in hierarchical models

Community mental healthcare providers’ attitudes and practices related to smoking cessation interventions for people living with severe mental illness

In most epidemiological investigations, the study units are people, the outcome variable (or the response) is a health-related event, and the explanatory variables are usually environmental and/or socio-demographic factors. The fundamental task in such investigations is to quantify the association between the explanatory variables (covariates/exposures) and the outcome variable through a suitable regression model. The accuracy of such quantification depends on how precisely the relevant covariates are measured. In many instances, we cannot measure some of the covariates accurately. Rather, we can measure noisy (mismeasured) versions of them. In statistical terminology, mismeasurement in continuous covariates is known as measurement errors or errors-in-variables. Regression analyses based on mismeasured covariates lead to biased inference about the true underlying response-covariate associations. In this paper, we suggest a flexible parametric approach for avoiding this bias when estimating the response-covariate relationship through a logistic regression model. More specifically, we consider the flexible generalized skew-normal and the flexible generalized skew-t distributions for modeling the unobserved true exposure. For inference and computational purposes, we use Bayesian Markov chain Monte Carlo techniques. We investigate the performance of the proposed flexible parametric approach in comparison with a common flexible parametric approach through extensive simulation studies. We also compare the proposed method with the competing flexible parametric method on a real-life data set. Though emphasis is put on the logistic regression model, the proposed method is unified and is applicable to the other generalized linear models, and to other types of non-linear regression models as well.

Bayesian adjustment for covariate measurement errors: A flexible parametric approach

Aims and objective.  This study examined the correlates of smoking among asthmatic adolescents to gain a better understanding of who is at particular risk.

Background.  Smoking is especially harmful to individuals with asthma. However, smoking is surprisingly prevalent among asthmatic individuals, with prevalence rates similar to or higher than those of the general adult or adolescent populations. Despite this notable finding, there has been little research about factors (i.e. biophysical, psychosocial and behavioural) influencing asthmatic adolescents’ tobacco use patterns.

Design.  A Canadian provincial cross-sectional survey.

Method.  The study about adolescents’ tobacco use and health status was conducted in secondary schools in 2004, 608 asthmatic adolescents participated. Demographic factors, biophysical (body mass index and physical health), psychosocial factors (parents’ and peers’ smoking, environmental tobacco smoke exposure and depression) and behavioural factors (marijuana use, alcohol use and exercise frequency) were explored. Multinomial logistic regression analyses were conducted to identify risk factors associated with tobacco use.

Results and conclusions.  Of the 608 asthmatic adolescents, 17·4% currently smoked and 12·0% formerly smoked. Girls, compared with boys, were more likely to smoke (OR: 3·34, 95% CI: 1·62–6·96) after adjusting for differences in the other demographic, biophysical, psychosocial and behavioural factors. Asthmatic girls who had relatively higher body mass index, were in the higher school grades, used marijuana or alcohol, had minor to severe depressive symptoms, had environmental tobacco smoke exposure in their homes and had friends who smoked or were currently more likely to smoke. The former smokers had similar risk factors including higher body mass index, environmental tobacco smoke exposure at home, friends who smoked and marijuana use.

Relevance to clinical practice.  Despite their health condition, asthmatic adolescents continue currently or formerly to smoke. Gender appropriate prevention and cessation interventions for asthmatic adolescents may need to address important psychosocial and environmental factors that increase the risk of these adolescents initiating and maintaining tobacco use.

Correlates of smoking among adolescents with asthma

Background: Temporal delays in myocardial infarction (MI) treatment have been addressed through patient and physician education, innovations in prehospital fibrinolysis, and improvements to emergency medical services, yet the most significant contributor to delayed treatment is the patient’s ability to recognize and respond to symptoms.

Purpose: To determine whether public health education campaigns have achieved their desired reach by ensuring that all segments of the population recognize the symptoms of MI (“heart attack”).

Methods: 976 men and women, 40+ years of age, randomly selected from Metro Vancouver, Canada completed a telephone survey in English, Punjabi, Mandarin, or Cantonese. Respondents’ knowledge of MI symptoms was assessed; 10 “correct symptoms” were considered to be: chest pain/pressure/tightness/discomfort, arm pain, shortness of breath, nausea/indigestion, sweating/clamminess, shoulder/back pain, dizziness/faintness/light headedness, jaw pain, weakness, and uneasiness/panic/anxiety.

Results: 3.2% of the sample could not identify any correct symptoms and 53.3% were able to describe 3+ symptoms. Significant associations were found between the number of correct symptoms and gender, ethnicity, education, exposure to health professional counseling, and worry about having a heart attack. The least number of correct symptoms were reported by: men (incidence rate ratio (IRR) = 0.87; 95% confidence interval (95% CI): 0.81−0.95), Chinese-Canadian participants (IRR = 0.73; 95% CI: 0.65−0.83; relative to European-Canadian born participants), those with less than high school education (IRR = 0.78; 95% CI: 0.66−0.92; relative to those with more than high school), those with no health professional counseling (IRR = 0.92; 95% CI: 0.84−1.00), and those who did not worry “at all” about having a heart attack (IRR = 0.89; 95% CI: 0.80−0.98; relative to those who worried sometimes/often/almost all the time).

Conclusions: The participants were not well informed about the symptoms of heart attack. It will be challenging to educate the public sufficiently to reduce the time between the onset of symptoms and initiation of treatment for MI.

https://journals.sagepub.com/doi/pdf/10.4137/CMC.S709

Shahadut Hossain

Papers

Conservative prior distributions for variance parameters in hierarchical models

Community mental healthcare providers’ attitudes and practices related to smoking cessation interventions for people living with severe mental illness

Bayesian adjustment for covariate measurement errors: A flexible parametric approach

Correlates of smoking among adolescents with asthma

Knowledge of “Heart Attack” Symptoms in a Canadian Urban Community