Approximation de la distribution a posteriori d'un modèle Gamma-Poisson hiérarchique à effets mixtes

La méthode que nous présentons pour modéliser des données dites de "comptage" ou données de Poisson est basée sur la procédure nommée Modélisation multi-niveau et interactive de la régression de Poisson (PRIMM) développée par Christiansen et Morris (1997). Dans la méthode PRIMM, la régression de Poisson ne comprend que des effets fixes tandis que notre modèle intègre en plus des effets aléatoires. De même que Christiansen et Morris (1997), le modèle étudié consiste à faire de l'inférence basée sur des approximations analytiques des distributions a posteriori des paramètres, évitant ainsi d'utiliser des méthodes computationnelles comme les méthodes de Monte Carlo par chaînes de Markov (MCMC). Les approximations sont basées sur la méthode de Laplace et la théorie asymptotique liée à l'approximation normale pour les lois a posteriori. L'estimation des paramètres de la régression de Poisson est faite par la maximisation de leur densité a posteriori via l'algorithme de Newton-Raphson. Cette étude détermine également les deux premiers moments a posteriori des paramètres de la loi de Poisson dont la distribution a posteriori de chacun d'eux est approximativement une loi gamma. Des applications sur deux exemples de données ont permis de vérifier que ce modèle peut être considéré dans une certaine mesure comme une généralisation de la méthode PRIMM. En effet, le modèle s'applique aussi bien aux données de Poisson non stratifiées qu'aux données stratifiées; et dans ce dernier cas, il comporte non seulement des effets fixes mais aussi des effets aléatoires liés aux strates. Enfin, le modèle est appliqué aux données relatives à plusieurs types d'effets indésirables observés chez les participants d'un essai clinique impliquant un vaccin quadrivalent contre la rougeole, les oreillons, la rub\'eole et la varicelle. La régression de Poisson comprend l'effet fixe correspondant à la variable traitement/contrôle, ainsi que des effets aléatoires liés aux systèmes biologiques du corps humain auxquels sont attribués les effets indésirables considérés.We propose a method for analysing count or Poisson data based on the procedure called Poisson Regression Interactive Multilevel Modeling (PRIMM) introduced by Christiansen and Morris (1997). The Poisson regression in the PRIMM method has fixed effects only, whereas our model incorporates random effects. As well as Christiansen and Morris (1997), the model studied aims at doing inference based on adequate analytical approximations of posterior distributions of the parameters. This avoids the use of computationally expensive methods such as Markov chain Monte Carlo (MCMC) methods. The approximations are based on the Laplace's method and asymptotic theory. Estimates of Poisson mixed effects regression parameters are obtained through the maximization of their joint posterior density via the Newton-Raphson algorithm. This study also provides the first two posterior moments of the Poisson parameters involved. The posterior distributon of these parameters is approximated by a gamma distribution. Applications to two datasets show that our model can be somehow considered as a generalization of the PRIMM method since it also allows clustered count data. Finally, the model is applied to data involving many types of adverse events recorded by the participants of a drug clinical trial which involved a quadrivalent vaccine containing measles, mumps, rubella and varicella. The Poisson regression incorporates the fixed effect corresponding to the covariate treatment/control as well as a random effect associated with the biological system of the body affected by the adverse events

Value of Safety (VALOSA) : Final report

Many companies describe safety as their top priority, but does that mean that safety is a value for them? Values are more stable and can be expected to have a more sustainable impact on safety than safety as “just a priority”. Particularly in an era of deregulation, globalization, economic downturn and the ‘changing world of work’, values and culture are more stable than mana- gement systems or priorities. There is often an imbalance between safety values and business values, leading to dilemmas and unsafe situations. By exploring safety values and dilemmas, this report provides insights into more successful mechanisms that have the potential to strengthen and promote safety values

Approximation de la distribution a posteriori d'un modèle Gamma-Poisson hiérarchique à effets mixtes

La méthode que nous présentons pour modéliser des données dites de "comptage" ou données de Poisson est basée sur la procédure nommée Modélisation multi-niveau et interactive de la régression de Poisson (PRIMM) développée par Christiansen et Morris (1997). Dans la méthode PRIMM, la régression de Poisson ne comprend que des effets fixes tandis que notre modèle intègre en plus des effets aléatoires. De même que Christiansen et Morris (1997), le modèle étudié consiste à faire de l'inférence basée sur des approximations analytiques des distributions a posteriori des paramètres, évitant ainsi d'utiliser des méthodes computationnelles comme les méthodes de Monte Carlo par chaînes de Markov (MCMC). Les approximations sont basées sur la méthode de Laplace et la théorie asymptotique liée à l'approximation normale pour les lois a posteriori. L'estimation des paramètres de la régression de Poisson est faite par la maximisation de leur densité a posteriori via l'algorithme de Newton-Raphson. Cette étude détermine également les deux premiers moments a posteriori des paramètres de la loi de Poisson dont la distribution a posteriori de chacun d'eux est approximativement une loi gamma. Des applications sur deux exemples de données ont permis de vérifier que ce modèle peut être considéré dans une certaine mesure comme une généralisation de la méthode PRIMM. En effet, le modèle s'applique aussi bien aux données de Poisson non stratifiées qu'aux données stratifiées; et dans ce dernier cas, il comporte non seulement des effets fixes mais aussi des effets aléatoires liés aux strates. Enfin, le modèle est appliqué aux données relatives à plusieurs types d'effets indésirables observés chez les participants d'un essai clinique impliquant un vaccin quadrivalent contre la rougeole, les oreillons, la rub\'eole et la varicelle. La régression de Poisson comprend l'effet fixe correspondant à la variable traitement/contrôle, ainsi que des effets aléatoires liés aux systèmes biologiques du corps humain auxquels sont attribués les effets indésirables considérés.We propose a method for analysing count or Poisson data based on the procedure called Poisson Regression Interactive Multilevel Modeling (PRIMM) introduced by Christiansen and Morris (1997). The Poisson regression in the PRIMM method has fixed effects only, whereas our model incorporates random effects. As well as Christiansen and Morris (1997), the model studied aims at doing inference based on adequate analytical approximations of posterior distributions of the parameters. This avoids the use of computationally expensive methods such as Markov chain Monte Carlo (MCMC) methods. The approximations are based on the Laplace's method and asymptotic theory. Estimates of Poisson mixed effects regression parameters are obtained through the maximization of their joint posterior density via the Newton-Raphson algorithm. This study also provides the first two posterior moments of the Poisson parameters involved. The posterior distributon of these parameters is approximated by a gamma distribution. Applications to two datasets show that our model can be somehow considered as a generalization of the PRIMM method since it also allows clustered count data. Finally, the model is applied to data involving many types of adverse events recorded by the participants of a drug clinical trial which involved a quadrivalent vaccine containing measles, mumps, rubella and varicella. The Poisson regression incorporates the fixed effect corresponding to the covariate treatment/control as well as a random effect associated with the biological system of the body affected by the adverse events.La méthode que nous présentons pour modéliser des données dites de "comptage" ou données de Poisson est basée sur la procédure nommée Modélisation multi-niveau et interactive de la régression de Poisson (PRIMM) développée par Christiansen et Morris (1997). Dans la méthode PRIMM, la régression de Poisson ne comprend que des effets fixes tandis que notre modèle intègre en plus des effets aléatoires. De même que Christiansen et Morris (1997), le modèle étudié consiste à faire de l'inférence basée sur des approximations analytiques des distributions a posteriori des paramètres, évitant ainsi d'utiliser des méthodes computationnelles comme les méthodes de Monte Carlo par chaînes de Markov (MCMC). Les approximations sont basées sur la méthode de Laplace et la théorie asymptotique liée à l'approximation normale pour les lois a posteriori. L'estimation des paramètres de la régression de Poisson est faite par la maximisation de leur densité a posteriori via l'algorithme de Newton-Raphson. Cette étude détermine également les deux premiers moments a posteriori des paramètres de la loi de Poisson dont la distribution a posteriori de chacun d'eux est approximativement une loi gamma. Des applications sur deux exemples de données ont permis de vérifier que ce modèle peut être considéré dans une certaine mesure comme une généralisation de la méthode PRIMM. En effet, le modèle s'applique aussi bien aux données de Poisson non stratifiées qu'aux données stratifiées; et dans ce dernier cas, il comporte non seulement des effets fixes mais aussi des effets aléatoires liés aux strates. Enfin, le modèle est appliqué aux données relatives à plusieurs types d'effets indésirables observés chez les participants d'un essai clinique impliquant un vaccin quadrivalent contre la rougeole, les oreillons, la rub\'eole et la varicelle. La régression de Poisson comprend l'effet fixe correspondant à la variable traitement/contrôle, ainsi que des effets aléatoires liés aux systèmes biologiques du corps humain auxquels sont attribués les effets indésirables considérés.We propose a method for analysing count or Poisson data based on the procedure called Poisson Regression Interactive Multilevel Modeling (PRIMM) introduced by Christiansen and Morris (1997). The Poisson regression in the PRIMM method has fixed effects only, whereas our model incorporates random effects. As well as Christiansen and Morris (1997), the model studied aims at doing inference based on adequate analytical approximations of posterior distributions of the parameters. This avoids the use of computationally expensive methods such as Markov chain Monte Carlo (MCMC) methods. The approximations are based on the Laplace's method and asymptotic theory. Estimates of Poisson mixed effects regression parameters are obtained through the maximization of their joint posterior density via the Newton-Raphson algorithm. This study also provides the first two posterior moments of the Poisson parameters involved. The posterior distributon of these parameters is approximated by a gamma distribution. Applications to two datasets show that our model can be somehow considered as a generalization of the PRIMM method since it also allows clustered count data. Finally, the model is applied to data involving many types of adverse events recorded by the participants of a drug clinical trial which involved a quadrivalent vaccine containing measles, mumps, rubella and varicella. The Poisson regression incorporates the fixed effect corresponding to the covariate treatment/control as well as a random effect associated with the biological system of the body affected by the adverse events

Approximate Posterior Inference for Multiple Testing using a Hierarchical Mixed-effect Poisson Regression Model

We present an approximate posterior inference  methodology for a Bayesian hierarchical mixed-effect Poisson regression model. The model serves us to address the multiple testing problem in the presence of many group or cluster effects. This is carried out through a specialized Bayesian false discovery rate procedure. The likelihood is simplified by an approximation based on Laplace's approximation for integrals and a trace approximation for the determinants. The posterior marginals are estimated using this approximated likelihood. In particular, we obtain credible regions for the parameters, as well as probability estimates for the difference between risks (Poisson intensities) associated with different groups or clusters, or different levels of the fixed effects. The methodology is illustrated through an application to a vaccine trial

CrypticIBDcheck: an R package for checking cryptic relatedness in nominally unrelated individuals

BACKGROUND: In population association studies, standard methods of statistical inference assume that study subjects are independent samples. In genetic association studies, it is therefore of interest to diagnose undocumented close relationships in nominally unrelated study samples. RESULTS: We describe the R package CrypticIBDcheck to identify pairs of closely-related subjects based on genetic marker data from single-nucleotide polymorphisms (SNPs). The package is able to accommodate SNPs in linkage disequibrium (LD), without the need to thin the markers so that they are approximately independent in the population. Sample pairs are identified by superposing their estimated identity-by-descent (IBD) coefficients on plots of IBD coefficients for pairs of simulated subjects from one of several common close relationships. CONCLUSIONS: The methods implemented in CrypticIBDcheck are particularly relevant to candidate-gene association studies, in which dependent SNPs cluster in a relatively small number of genes spread throughout the genome. The accommodation of LD allows the use of all available genetic data, a desirable property when working with a modest number of dependent SNPs within candidate genes. CrypticIBDcheck is available from the Comprehensive R Archive Network (CRAN)

Crystal methamphetamine initiation among street-involved youth

Background: Although many settings have recently documented a substantial increase in the use of methamphetamine-type stimulants, recent reviews have underscored the dearth of prospective studies that have examined risk factors associated with the initiation of crystal methamphetamine use. Objectives: Our objectives were to examine rates and risk factors for the initiation of crystal methamphetamine use in a cohort of street-involved youth. Methods: Street-involved youth in Vancouver, Canada, were enrolled in a prospective cohort known as the At-Risk Youth Study (ARYS). A total of 205 crystal methamphetamine-naïve participants were assessed semi-annually and Cox regression analyses were used to identify factors independently associated with the initiation of crystal methamphetamine use. Results: Among 205 youth prospectively followed from 2005 to 2012, the incidence density of crystal methamphetamine initiation was 12.2 per 100 person years. In Cox regression analyses, initiation of crystal methamphetamine use was independently associated with previous crack cocaine use (adjusted relative hazard [ARH] = 2.24 [95% CI: 1.20–4.20]) and recent drug dealing (ARH = 1.98 [95% CI: 1.05–3.71]). Those initiating methamphetamine were also more likely to report a recent nonfatal overdose (ARH = 3.63 [95% CI: 1.65–7.98]) and to be male (ARH = 2.12 [95% CI: 1.06–4.25]). Conclusions: We identified high rates of crystal methamphetamine initiation among this population. Males those involved in the drug trade, and those who used crack cocaine were more likely to initiate crystal methamphetamine use. Evidence-based strategies to prevent and treat crystal methamphetamine use are urgently needed.Medicine, Faculty ofOther UBCNon UBCMedicine, Department ofReviewedFacultyResearche

The ‘Stolen Generations' of Mothers and Daughters : Child Apprehension and Enhanced HIV Vulnerabilities for Sex Workers of Aboriginal Ancestry

OBJECTIVES: The number of children in care of the state continues to grow in BC, Canada with a historical legacy of child apprehension among criminalized and marginalized populations, particularly women of Aboriginal ancestry and sex workers. However, there is a paucity of research investigating child apprehension experiences among marginalized mothers. The objective of the current analysis is to examine the prevalence and correlates of child apprehensions among female sex workers in Vancouver, Canada. METHODS: Analyses were drawn from the AESHA (An Evaluation of Sex Workers Health Access, 2010-present), a prospective cohort of street and off-street SWs, through outreach and semi-annual visits to the research office. Bivariate and multivariate logistic regression were used to examine correlates of child apprehension. RESULTS: Of a total of 510 SWs, 350 women who had given birth to at least one child were included in the analyses (median age = 37 yrs: IQR: 31-44 yrs). The prevalence of child apprehension among mothers was 38.3%, with 37.4% reporting having been apprehended themselves by child welfare services. In multivariable analysis, servicing clients in outdoor public spaces (versus formal sex work establishments or informal indoor settings) (adjusted odds ratio, (aOR) = 2.73; 95%CI 1.27-5.90), history of injecting drugs (aOR  = 2.53; 95%CI 1.42-4.49), Aboriginal ancestry (aOR = 1.66; 95%CI 1.01-2.74) were associated with increased odds of child apprehension. DISCUSSION/CONCLUSIONS: Child apprehension rates are high, particularly among the most marginalized sex workers, including sex workers who use drugs and sex workers of Aboriginal ancestry. Structural reforms to child protection are urgently needed, that support family-based care address the historical legacy of colonization affecting Aboriginal peoples.Medicine, Faculty ofOther UBCNon UBCMedicine, Department ofPopulation and Public Health (SPPH), School ofReviewedFacultyResearcherPostdoctoralGraduat