Variance Random Effects Research Articles

Assessing Long-Term Health Effects of Air Pollution with Advanced Spatial Models

Near the end of the last millennium, relatively few studies on the long-term health effects of air pollution had been undertaken. Two studies were effectively being used for setting government regulations designed to protect public health, namely the American Cancer Society Cancer (ACS) Cancer Prevention II Cohort Study and the Harvard 6-Cities Study. These studies came under increasing scrutiny due to their influential role in setting public health regulations in the United States and elsewhere. As a result, the U.S. Congress requested an independent reanalysis of these two studies. The Health Effects Institute administered the study, which was led by Drs. Krewski and Burnett. Working with fellow statistician Dr. Renjun Ma, Dr. Burnett developed a new flexible method of estimating the Cox proportional hazards model with allowance for random effects that essentially captured the residual variation not accounted for by the fixed predictors in the model. This allowed for the development of an iterative and powerful new method for investigating and minimizing confounding from our Cox model predictions because we could assess the size of the residual variation in total with the random effects variance, and we could also map out the random effects to search for spatial patterns that would represent potentially important missing confounders. This led to a 15-year endeavor to refine these models with the development of new random effects software that we used to assess the long-term effects of air pollution in the ACS cohort and subsequently several other cohorts. In this presentation, I will introduce the random effects method and show several examples that illustrate the capacity of this method to defend against residual confounding and to ultimately produce the sound science required to support regulatory standards and health impact assessments.

Examining critical and near-critical paths: an Excel-based classroom exercise

The author’s goals in this exercise were to use an Excel project to teach students about the effects of changing states of nature on critical path emergence as well as compare the impact of the PERT beta and triangular distributions on project completion times. Previous research into the PERT (program evaluation and review technique) beta distribution indicated a tendency for this distribution to overstate the impact of the dominant critical path on project completion times. The author used a Monte Carlo simulation and a new Excel function to explore the effects of random variation on critical path emergence. He used both the triangular distribution and the PERT beta distribution to calculate project completion times. The author found that three of the possible five paths became critical in both the PERT beta and triangular distributions. However, the triangular distribution moderated the PERT beta distribution’s tendency toward the dominate critical path.

The effect of number of clusters and cluster size on statistical power and Type I error rates when testing random effects variance components in multilevel linear and logistic regression models

When using multilevel regression models that incorporate cluster-specific random effects, the Wald and the likelihood ratio (LR) tests are used for testing the null hypothesis that the variance of the random effects distribution is equal to zero. We conducted a series of Monte Carlo simulations to examine the effect of the number of clusters and the number of subjects per cluster on the statistical power to detect a non-null random effects variance and to compare the empirical type I error rates of the Wald and LR tests. Statistical power increased with increasing number of clusters and number of subjects per cluster. Statistical power was greater for the LR test than for the Wald test. These results applied to both the linear and logistic regressions, but were more pronounced for the latter. The use of the LR test is preferable to the use of the Wald test.

Estimating variance of random effects to solve multiple problems simultaneously

The two-level normal hierarchical model (NHM) has played a critical role in statistical theory for the last several decades. In this paper, we propose random effects variance estimator that simultaneously (i) improves on the estimation of the related shrinkage factors, (ii) protects empirical best linear unbiased predictors (EBLUP) [same as empirical Bayes (EB)] of the random effects from the common overshrinkage problem, (iii) avoids complex bias correction in generating strictly positive second-order unbiased mean square error (MSE) (same as integrated Bayes risk) estimator either by the Taylor series or single parametric bootstrap method. The idea of achieving multiple desirable properties in an EBLUP or EB method through a suitably devised random effects variance estimator is the first of its kind and holds promise in providing good inferences for random effects under the EBLUP or EB framework. The proposed methodology is also evaluated using a Monte Carlo simulation study and real data analysis.

The importance of core habitat for a threatened species in changing landscapes

Abstract Habitat loss, fragmentation, and alteration of the landscape matrix are interdependent processes, collectively responsible for most recent species extinctions. Thus, determining the extent to which these landscape processes affect animals is critical for conservation. However, researchers have often assumed that interdependent effects are independently related to animals’ responses, underestimating the importance of one or several landscape processes in driving species declines. We demonstrate how to disentangle the interdependent effects of habitat amount, fragmentation, and edge context on population size by assessing abundance of a rapidly declining grassland songbird species (grasshopper sparrow Ammodramus savannarum) in eastern Kansas (USA). We conducted >7,000 point count surveys at >2,000 sites over two breeding seasons, then modelled the direct, interactive, and indirect effects of landscape factors on abundance within spatial scales (200‐, 400‐, 800‐, and 1,600‐m radii) relevant to our focal species’ dispersal behaviour. Sparrow abundance correlated most strongly with landscape structure within 400‐m radii, increasing nonlinearly with grassland area and decreasing with the proportion of grassland near cropland or woody edges. Sparrows’ negative response to cropland edges was mostly an added, indirect consequence of reduced grassland area, whereas sparrows’ stronger negative response to woody edges was not attributable to variation in grassland area. Fragmentation and edge context mattered most in landscapes comprising c. 50%–80% grassland. Synthesis and applications. In our research, abundance of a threatened grassland songbird was influenced more by core grassland area (a function of total grassland area, fragmentation, and edge context) than total grassland area per se. Moreover, a local extinction threshold of c. 50% grassland indicated that small amounts of habitat were unsuitable for our focal species regardless of habitat configuration or matrix type. Local extinction thresholds in response to habitat area provide clear baseline targets for land managers; above those thresholds, configuration and the matrix can be modified to increase abundance of edge‐sensitive animals. Conflicting evidence in the literature regarding the importance of fragmentation and matrix features could be partially explained by species‐level traits, or methodological issues such as defining landscapes at ecologically arbitrary spatial scales, assessing landscape quality using species richness, and ignoring interactive and indirect effects.

Hepatitis B surface antigen positivity during pregnancy and risk of gestational diabetes mellitus: A systematic review and meta-analysis.

Chronic hepatitis B virus (HBV) infection is a prevalent public health issue worldwide. Its impact on important pregnancy outcomes, such as gestational diabetes mellitus (GDM), has not been clearly established. The findings from published studies are inconsistent. In this systematic review and meta-analysis, we aimed to clarify whether HBV infection manifested during pregnancy is associated with an increased risk of GDM. We searched MEDLINE and EMBASE for cohort studies and case-control studies that investigated the association between maternal hepatitis B surface antigen (HBsAg) positivity and GDM. We pooled adjusted odds ratio (aOR) and unadjusted OR, respectively, using the random-effect generic inverse variance method. We assessed risk of bias using the Quality in Prognosis Studies tool and conducted five pre-specified subgroup analyses. In total, 23 cohort studies involving 3529223 participants were included. The risk of GDM was 6.48% (1868/28829) among HBsAg-positive pregnant women and 3.41% (119283/3500394) among HBsAg-negative pregnant women. Meta-analyses of both unadjusted and adjusted effect estimates showed that HBsAg positivity during pregnancy was associated with higher risk of developing GDM (unadjusted OR 1.35, 95% CI: 1.17 to 1.56, I2 =82.6%; adjusted OR 1.47, 1.22 to 1.76, I2 =62%). Among pre-specified subgroup analysis, significant differences were found among studies with high vs low or moderate risk of bias. The results were robust to sensitivity analyses. In conclusion, HBsAg positivity during pregnancy has a moderate effect on an increased risk of GDM. Given the size of the population with HBV infection worldwide, however, this effect could have substantial impact on pregnancy. Further studies are warranted to investigate whether active infection with HBeAg positivity would further elevate the risk of adverse events during pregnancy.

A cause-specific hazard spatial frailty model for competing risks data

Competing risks data is often grouped into clusters, such as geographic regions. Shared frailty models can be used to model the correlation between individuals within each cluster. For the better fitting of the model in the competing risks data, one random effect can be used for every type of event in each cluster. But the correlation between random effects in each cluster should also be taken into account. On the other hand, there may also be a spatial correlation between random effects during geographic regions. In this paper, we use cause-specific hazard spatial frailty model with multivariate conditional autoregressive distribution for frailties via Bayesian approach. Simulation studies are used to assess the regression coefficient estimators as well as the variance and correlation of random effects within the clusters. We apply the proposed model to the analysis of the gastrointestinal cancer data of Iran’s National Institute of Health Research and the Louisiana breast cancer data from the Surveillance Epidemiology and End Results database of the National Cancer Institute.

Vitamin D Supplementation, Serum 25(OH)D Concentrations and Cardiovascular Disease Risk Factors: A Systematic Review and Meta-Analysis.

Background: Cardiovascular disease (CVD) risk factors are associated with low serum 25 hydroxyvitamin D (25(OH)D) concentrations in observational studies; however, clinical trial findings are inconsistent.Objective: We assessed the effect of vitamin D supplementation and increased serum 25(OH)D concentrations on CVD risk factors in a systemic review and meta-analysis of randomized controlled trials (RCTs).Design: MEDLINE, CINAHL, EMBASE, and Google Scholar were searched for RCTs that evaluated vitamin D supplementation and cardiovascular outcomes [blood pressure, parathyroid hormone (PTH), serum high-sensitivity C-reactive protein (hs-CRP), total cholesterol, high and low density lipoprotein (HDL and LDL, respectively), triglycerides, peak wave velocity (PWV) and Augmentation Index (AI)] from 1992 through 2017. Meta-analysis was based on a random-effects model and inverse variance method to calculate standardized mean difference (SMD) as effect sizes, followed by a leave-one-out method for sensitivity analysis. Risk of publication bias was assessed using Cochrane checklist and Begg funnel plots. The systematic review is registered as CRD42015025346.Results: We identified 2341 studies from which 81 met inclusion criteria. The meta-analysis indicated a significant reduction in systolic blood pressure (SMD = −0.102 ± 0.04 mmHg, 95% confidence interval (CI), −0.20 to −0.03), diastolic blood pressure (SMD = −0.07 ± 0.03 mmHg, 95% CI, −0.14 to −0.006), serum PTH (SMD = −0.66 ± 0.08 ng/L, 95% CI, −0.82 to −0.49), hs-CRP (SMD = −0.20 ± 0.07 mg/L, 95% CI, −0.34 to −0.06), total cholesterol (SMD = −0.15 ± 0.06 mmol/L, 95% CI, −0.25 to −0.04), LDL (SMD = −0.10 ± 0.05 mmol/L, 95% CI, −0.20 to −0.003), triglycerides (SMD = −0.12 ± 0.06 mmol/L, 95% CI, −0.23 to −0.003) and a significant increase in HDL (SMD = 0.09 ± 0.04 mmol/L, 95% CI, 0.00 to 0.17) with vitamin D supplementation. These findings remained significant in sensitivity analyses for blood pressure, lipid profile, serum PTH, and serum hs-CRP. There was no significant effect of vitamin D supplementation on PWV (SMD = −0.20 ± 0.13 m/s, 95% CI, −0.46 to 0.06, p = 0.14) and AI (SMD = −0.09 ± 0.14%, 95% CI, −0.37 to 0.19, p = 0.52) for vitamin D supplemented groups.Conclusion: These findings suggest that vitamin D supplementation may act to protect against CVD through improving risk factors, including high blood pressure, elevated PTH, dyslipidemia, and inflammation.

Modeling Random Effects Using Global–Local Shrinkage Priors in Small Area Estimation

ABSTRACTSmall area estimation is becoming increasingly popular for survey statisticians. One very important program is Small Area Income and Poverty Estimation undertaken by the United States Bureau of the Census, which aims at providing estimates related to income and poverty based on American Community Survey data at the state level and even at lower levels of geography. This article introduces global–local (GL) shrinkage priors for random effects in small area estimation to capture wide area level variation when the number of small areas is very large. These priors employ two levels of parameters, global and local parameters, to express variances of area-specific random effects so that both small and large random effects can be captured properly. We show via simulations and data analysis that use of the GL priors can improve estimation results in most cases. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Spatial-temporal characteristics and the epidemiology of haemorrhagic fever with renal syndrome from 2007 to 2016 in Zhejiang Province, China

Zhejiang Province is one of the six provinces in China that has the highest incidence of haemorrhagic fever with renal syndrome (HFRS). Data on HFRS cases in Zhejiang Province from January 2007 to July 2017 were obtained from the China Information Network System of Disease Prevention and Control. Joinpoint regression analysis was used to observe the trend of the incidence rate of HFRS. The monthly incidence rate was predicted by autoregressive integrated moving average(ARIMA) models. Spatial autocorrelation analysis was performed to detect geographic clusters. A multivariate time series model was employed to analyze heterogeneous transmission of HFRS. There were a total of 4,836 HFRS cases, with 15 fatal cases reported in Zhejiang Province, China in the last decade. Results show that the mean absolute percentage error (MAPE) of the modelling performance and the forecasting performance of the ARIMA model were 27.53% and 16.29%, respectively. Male farmers and middle-aged patients account for the majority of the patient population. There were 54 high-high clusters and 1 high-low cluster identified at the county level. The random effect variance of the autoregressive component is 0.33; the spatio-temporal component is 1.30; and the endemic component is 2.45. According to the results, there was obvious spatial heterogeneity in the endemic component and spatio-temporal component but little spatial heterogeneity in the autoregressive component. A significant decreasing trend in the incidence rate was identified, and obvious clusters were discovered. Spatial heterogeneity in the factors driving HFRS transmission was discovered, which suggested that a targeted preventive effort should be considered in different districts based on their own main factors that contribute to the epidemics.

Generalised quasi‐likelihood inference in a semi‐parametric binary dynamic mixed logit model

AbstractThere exists a recent study where dynamic mixed‐effects regression models for count data have been extended to a semi‐parametric context. However, when one deals with other discrete data such as binary responses, the results based on count data models are not directly applicable. In this paper, we therefore begin with existing binary dynamic mixed models and generalise them to the semi‐parametric context. For inference, we use a new semi‐parametric conditional quasi‐likelihood (SCQL) approach for the estimation of the non‐parametric function involved in the semi‐parametric model, and a semi‐parametric generalised quasi‐likelihood (SGQL) approach for the estimation of the main regression, dynamic dependence and random effects variance parameters. A semi‐parametric maximum likelihood (SML) approach is also used as a comparison to the SGQL approach. The properties of the estimators are examined both asymptotically and empirically. More specifically, the consistency of the estimators is established and finite sample performances of the estimators are examined through an intensive simulation study.

Modeling Count Data from Dependent Clusters with Poisson Mixed Models


 
 
 
 Responses collected from dependent clusters are affected by the dependence among clusters and it should be taken into account in modeling such responses. In this study, a new approach was evaluated to incorporate cluster dependence in generalized linear Poisson mixed models for count responses from dependent clusters. Performance of this approach was evaluated by using a simulation process under three different designs and different covariates. The Marginal Generalized Quasi-likelihood (GQL) method was used for estimation of parameters with the cluster dependence. Monte Carlo likelihood (MCL) and Penalized Quasi-likelihood (PQL) estimates also were obtained for the purpose of comparison. Proposed approach was tested with a real data set also.
 The proposed approach, with the incorporation of cluster dependence, gives better estimates for both fixed effects and variance of random effects with low standard errors with compared to the estimates obtained by ignoring the cluster dependence. Therefore, the proposed approach can be used for modeling count responses from a dependent cluster set up.
 
 
 

Analyzing Complex Longitudinal Data in Educational Research: A Demonstration With Project English Language and Literacy Acquisition (ELLA) Data Using xxM.

When analyzing complex longitudinal data, especially data from different educational settings, researchers generally focus only on the mean part (i.e., the regression coefficients), ignoring the equally important random part (i.e., the random effect variances) of the model. By using Project English Language and Literacy Acquisition (ELLA) data, we demonstrated the importance of taking the complex data structure into account by carefully specifying the random part of the model, showing that not only can it affect the variance estimates, the standard errors, and the tests of significance of the regression coefficients, it also can offer different perspectives of the data, such as information related to the developmental process. We used xxM (Mehta, 2013), which can flexibly estimate different grade-level variances separately and the potential carryover effect from each grade factor to the later time measures. Implications of the findings and limitations of the study are discussed.

The clinical significance of platelet-derived growth factors (PDGFs) and their receptors (PDGFRs) in gastric cancer: A systematic review and meta-analysis.

The overexpression and mutation of platelet-derived growth factors (PDGFs) and their receptors (PDGFRs) are widespread in cancers and have been recognized as attractive oncologic targets with diverse therapeutic targets. Reports of the overexpression of genes, proteins and mutations of PDGFs/PDGFRs in gastric cancer and their associations with clinicopathological features, Western and Asian patients, as well as prognostic role have shown variable outcomes. This study sought to employ meta-analysis to evaluate PDGFs/PDGFRs status prognostic significance and their association with clinicopathological features of gastric cancer. A comprehensive search of PubMed database for studies that investigated the overexpression of mRNA/Protein and mutation of PDGFs/PDGFRs in gastric cancer of Western and Asian patients, their prognostic significance and association with clinicopathological characteristics in May, 2017 or earlier was carried out by two reviewers independently. Pooled odd ratios and hazard ratios at 95% confidence intervals were estimated and summarized using fixed-effect and random-effect Mantel-Haenszel models and Inverse Variance models in Review Manager software version 5.3. Fourteen studies with 16 datasets of 1178 patients were included in meta-analysis. Fourteen studies of 1178 patients with 1446 cases and 7 studies of 1076 patients with 1280 cases were included in meta-analysis of clinicopathological and prognostic significance of high or positive PDGF/PDGFR status respectively. Odd ratio at 95% confidence intervals for different groups of analysis are as follows: males versus females(OR = 1.38, 95% CI: 1.04-1.83, POR = 0.03); ≥T2 stage versus T1 stage(OR = 2.06, 95% CI: 1.22-3.49, POR = 0.007); nodal metastasis versus no nodal metastasis(OR = 2.78, 95% CI: 1.48-5.22, POR = 0.002); TNM stage ≥II versus TNM stage I(OR = 3.55, 95% CI: 1.89-6.69, POR<0.0001). Subgroup analysis of the association of PDGF/PDGFR among Western patients(OR = 0.24 95% CI: 0.10-0.58, POR = 0.002) and association of PDGFs/PDGFRs gene mutation among gastric cancer patients(OR = 0.15, 95% CI: 0.05-0.45, POR = 0.0008) were significant. The association of PDGFs/PDGFRs in young and middle age versus elderly aged, undifferentiated versus well differentiated tumors, large tumor size group(>6 cm) versus small tumor size group(≤6 cm) were insignificant. Subgroup analysis of the association of PDGFs/PDGFRs among Western Asian patients; PDGF/PDGFR mRNA expression and protein expression among gastric cancer patients were insignificant. In addition, PDGF/PDGFR status among gastric cancer patients was insignificant in overall effect analysis PDGF/PDGFR status has shown to predict reduced overall survival(HR = 1.25, 95% CI: 0.49-3.22, PHR = 0.64) and relapse free survival(HR = 0.93, 95% CI: 0.36-2.41, PHR = 0.88) insignificantly. Also, overall prognostic effect analysis(HR = 1.07, 95% CI: 0.58-1.96, PHR = 0.84) was insignificant. PDGFs/PDGFRs status amongst gastric cancer patients plays a key role in clinical variables and nodal metastasis. These insights might be helpful in providing guidelines for diagnosis, molecular target therapy, and prognosis of gastric cancer.

Bayesian inference for network meta-regression using multivariate random effects with applications to cholesterol lowering drugs.

Low-density lipoprotein cholesterol (LDL-C) has been identified as a causative factor for atherosclerosis and related coronary heart disease, and as the main target for cholesterol- and lipid-lowering therapy. Statin drugs inhibit cholesterol synthesis in the liver and are typically the first line of therapy to lower elevated levels of LDL-C. On the other hand, a different drug, Ezetimibe, inhibits the absorption of cholesterol by the small intestine and provides a different mechanism of action. Many clinical trials have been carried out on safety and efficacy evaluation of cholesterol lowering drugs. To synthesize the results from different clinical trials, we examine treatment level (aggregate) network meta-data from 29 double-blind, randomized, active, or placebo-controlled statins +/$-$ Ezetimibe clinical trials on adult treatment-naïve patients with primary hypercholesterolemia. In this article, we propose a new approach to carry out Bayesian inference for arm-based network meta-regression. Specifically, we develop a new strategy of grouping the variances of random effects, in which we first formulate possible sets of the groups of the treatments based on their clinical mechanisms of action and then use Bayesian model comparison criteria to select the best set of groups. The proposed approach is especially useful when some treatment arms are involved in only a single trial. In addition, a Markov chain Monte Carlo sampling algorithm is developed to carry out the posterior computations. In particular, the correlation matrix is generated from its full conditional distribution via partial correlations. The proposed methodology is further applied to analyze the network meta-data from 29 trials with 11 treatment arms.

Application of Stochastic Frontier Production Function to Separate the Effect of Random Variation in Output from Inefficiency in the Agricultural Production of African Countries

Application of Stochastic Frontier Production Function to Separate the Effect of Random Variation in Output from Inefficiency in the Agricultural Production of African Countries

Aromatase inhibitors are associated with a higher fracture risk than tamoxifen: a systematic review and meta-analysis.

In this paper, our aim was to systematically evaluate published evidence of bone fracture risk associated with tamoxifen and aromatase inhibitors in women aged 65 and under, and diagnosed with nonmetastatic breast cancer. We comprehensively searched MEDLINE, EMBASE and CINAHL databases from January 1997 through May 2015, and reference lists of the selected articles to identify English-language randomized controlled trials and cohort studies of fracture risk. Two independent reviewers screened articles and assessed methodological quality using Risk of Bias assessment for randomized controlled trials and the Newcastle-Ottawa Scale for cohort studies. Fracture risk was estimated as pooled risk ratios using a random-effects model and inverse variance method. Of 1926 identified articles, 21 independent studies fulfilled our selection criteria. Similar fracture risk was observed in women treated and not treated with tamoxifen [pooled risk ratio (RR) 0.95; 95% confidence interval (CI) 0.84-1.07]. A 35% (95% CI 1.21-1.51) higher fracture risk was observed in the aromatase inhibitor group compared with the tamoxifen group. A 17% (95% CI 1.07-1.28) higher fracture risk was observed in the aromatase inhibitor group than the no aromatase inhibitor group. Compared with the tamoxifen group, aromatase inhibitor-associated fracture risk increased by 33% (pooled RR 1.33; 95% CI 1.21-1.47) during the tamoxifen/aromatase inhibitor treatment period, but did not increase (pooled RR 0.99; 95% CI 0.72-1.37) during the post-tamoxifen/aromatase inhibitor treatment period. Fracture risk is significantly higher in women treated with aromatase inhibitors, especially during the treatment period. Tamoxifen is not associated with lower fracture risk while tamoxifen could potentially preserve bone mass. Better osteoporosis management programs, especially during the treatment period, are needed for this group of women.

Susceptibility to corticosteroid-induced adrenal suppression: a genome-wide association study

SummaryBackgroundA serious adverse effect of corticosteroid therapy is adrenal suppression. Our aim was to identify genetic variants affecting susceptibility to corticosteroid-induced adrenal suppression.MethodsWe enrolled children with asthma who used inhaled corticosteroids as part of their treatment from 25 sites across the UK (discovery cohort), as part of the Pharmacogenetics of Adrenal Suppression with Inhaled Steroids (PASS) study. We included two validation cohorts, one comprising children with asthma (PASS study) and the other consisting of adults with chronic obstructive pulmonary disorder (COPD) who were recruited from two UK centres for the Pharmacogenomics of Adrenal Suppression in COPD (PASIC) study. Participants underwent a low-dose short synacthen test. Adrenal suppression was defined as peak cortisol less than 350 nmol/L (in children) and less than 500 nmol/L (in adults). A case-control genome-wide association study was done with the control subset augmented by Wellcome Trust Case Control Consortium 2 (WTCCC2) participants. Single nucleotide polymorphisms (SNPs) that fulfilled criteria to be advanced to replication were tested by a random-effects inverse variance meta-analysis. This report presents the primary analysis. The PASS study is registered in the European Genome-phenome Archive (EGA). The PASS study is complete whereas the PASIC study is ongoing.FindingsBetween November, 2008, and September, 2011, 499 children were enrolled to the discovery cohort. Between October, 2011, and December, 2012, 81 children were enrolled to the paediatric validation cohort, and from February, 2010, to June, 2015, 78 adults were enrolled to the adult validation cohort. Adrenal suppression was present in 35 (7%) children in the discovery cohort and six (7%) children and 17 (22%) adults in the validation cohorts. In the discovery cohort, 40 SNPs were found to be associated with adrenal suppression (genome-wide significance p<1 × 10−6), including an intronic SNP within the PDGFD gene locus (rs591118; odds ratio [OR] 7·32, 95% CI 3·15–16·99; p=5·8 × 10−8). This finding for rs591118 was validated successfully in both the paediatric asthma (OR 3·86, 95% CI 1·19–12·50; p=0·02) and adult COPD (2·41, 1·10–5·28; p=0·03) cohorts. The proportions of patients with adrenal suppression by rs591118 genotype were six (3%) of 214 patients with the GG genotype, 15 (6%) of 244 with the AG genotype, and 22 (25%) of 87 with the AA genotype. Meta-analysis of the paediatric cohorts (discovery and validation) and all three cohorts showed genome-wide significance of rs591118 (respectively, OR 5·89, 95% CI 2·97–11·68; p=4·3 × 10−9; and 4·05, 2·00–8·21; p=3·5 × 10−10).InterpretationOur findings suggest that genetic variation in the PDGFD gene locus increases the risk of adrenal suppression in children and adults who use corticosteroids to treat asthma and COPD, respectively.FundingDepartment of Health Chair in Pharmacogenetics.

On waves in a random micropolar conducting magneto-thermo-viscoelastic medium

ABSTRACTEffects of random inhomogeneity on wave propagation in the interacting micropolar conducting magneto-generalized thermo-viscoelastic medium are studied. The couple stress theory relevant to micropolar solids is employed. The analysis is carried out under the smooth perturbation technique amenable to stochastic linear differential equations up to the second perturbation. The perturbing field has been assumed to be weakly conducting and weakly thermal. The generalized thermoelasticity has been used. Six different types of waves have been observed to propagate in the medium. The dispersion equations have been derived. Effects due to random variations of micropolar-elastic, conducting-electromagnetic and generalized thermo-visco-parameters have been computed. Effects of random heterogeneity of the conducting magnetic field are readily available up to first order perturbation. However effects of the generalized thermal field are discernible only in the domain of second order perturbation. Change of phase speed occurs on account of randomness. Attenuation coefficients for types of waves have been computed. A special type of generalized theromomechanical auto- and cross-correlation functions has been used to approximately measure effects of random variations of parameters. Uncoupled problem has been formulated for future investigations.

The Association of Unfavorable Traffic Events and Cannabis Usage: A Meta-Analysis.

Background: In the last years were published many epidemiological articles aiming to link driving under the influence of cannabis (DUIC) with the risk of various unfavorable traffic events (UTEs), with sometimes contradictory results.Aim: The primary objective of this study was to analyze whether there is a significant association between DUIC and UTEs.Materials and Methods: We used two meta-analytical methods to assess the statistical significance of the effect size: random-effects model and inverse variance heterogeneity model.Results: Twenty-four studies were included in the meta-analysis. We obtained significant increases in the effect size for DUIC tested through blood analysis, with an odds ratio (OR) of 1.97 and a confidence interval (CI) between 1.35 and 2.87; death as an outcome, with an OR of 1.56 and a CI between 1.16 and 2.09; and case–control as the type of study, with an OR of 1.99 and a CI between 1.05 and 3.80. Publication bias was very high.Conclusion: Our analysis suggests that the overall effect size for DUIC on UTEs is not statistically significant, but there are significant differences obtained through subgroup analysis. This result might be caused by either methodological flaws (which are often encountered in articles on this topic), the indiscriminate employment of the term “cannabis use,” or an actual absence of an adverse effect. When a driver is found, in traffic, with a positive reaction suggesting cannabis use, the result should be corroborated by either objective data regarding marijuana usage (like blood analyses, with clear cut-off values), or a clinical assessment of the impairment, before establishing his/her fitness to drive.