Mahmoud Torabi

Nabilone as an Adjunctive to Gabapentin for Multiple Sclerosis‐Induced Neuropathic Pain: A Randomized Controlled Trial

BACKGROUND Neuropathic pain (NPP) is a chronic syndrome suffered by patients with multiple sclerosis (MS), for which there is no cure. Underlying cellular mechanisms involved in its pathogenesis are multifaceted, presenting significant challenges in its management. METHODS A randomized, double-blind, placebo-controlled study involving 15 relapsing-remitting MS patients with MS-induced NPP was conducted to evaluate nabilone combined with gabapentin (GBP). Eligible patients stabilized on GBP (≥1,800 mg/day) with inadequate pain relief were recruited. Nabilone or placebo was titrated over 4 weeks (0.5 mg/week increase) followed by 5-week maintenance of 1 mg oral nabilone (placebo) twice daily. Primary outcomes included two daily patient-reported measures using a 100-mm visual analog scale (VAS), pain intensity (VASpain), and impact of pain on daily activities (VASimpact). Hierarchical regression modeling was conducted on each outcome to determine if within-person pain trajectory differed across study groups, during 63-day follow-up. RESULTS After adjustment for key patient-level covariates (e.g., age, sex, Expanded Disability Status Scale, duration of MS, baseline pain), a significant group × time(2) interaction term was reported for both the VASpain (P < 0.01) and VASimpact score (P < 0.01), demonstrating the adjusted rate of decrease for both outcomes was statistically greater in nabilone vs placebo study group. No significant difference in attrition rates was noted between treatments. Nabilone was well tolerated, with dizziness/drowsiness most frequently reported. CONCLUSION Nabilone as an adjunctive to GBP is an effective, well-tolerated combination for MS-induced NPP. The results of this study identify a novel therapeutic combination for use in this population of patients predisposed to tolerability issues that may otherwise prevent effective pain management.

An examination of five spatial disease clustering methodologies for the identification of childhood cancer clusters in Alberta, Canada.

Cluster detection is an important part of spatial epidemiology because it may help suggest potential factors associated with disease and thus, guide further investigation of the nature of diseases. Many different methods have been proposed to test for disease clusters. In this paper, we study five popular methods for detecting spatial clusters. These methods are Besag-Newell (BN), circular spatial scan statistic (CSS), flexible spatial scan statistic (FSS), Tangos maximized excess events test (MEET), and Bayesian disease mapping (BYM). We study these five different methods by analyzing a data set of malignant cancer diagnoses in children in the province of Alberta, Canada during 1983-2004. Our results show that the potential clusters are located in the south-central part of the province. Although, all methods performed very well to detect clusters, the BN and MEET methods identified local as well as general clusters.

Geographical variation and factors associated with colorectal cancer mortality in a universal health care system

OBJECTIVE To investigate the geographical variation and small geographical area level factors associated with colorectal cancer (CRC) mortality. METHODS Information regarding CRC mortality was obtained from the population-based Manitoba Cancer Registry, population counts were obtained from Manitobas universal health care plan Registry and characteristics of the area of residence were obtained from the 2001 Canadian census. Bayesian spatial Poisson mixed models were used to evaluate the geographical variation of CRC mortality and Poisson regression models for determining associations with CRC mortality. Time trends of CRC mortality according to income group were plotted using joinpoint regression. RESULTS The southeast (mortality rate ratio [MRR] 1.31 [95% CI 1.12 to 1.54) and southcentral (MRR 1.62 [95% CI 1.35 to 1.92]) regions of Manitoba had higher CRC mortality rates than suburban Winnipeg (Manitobas capital city). Between 1985 and 1996, CRC mortality did not vary according to household income; however, between 1997 and 2009, individuals residing in the highest-income areas were less likely to die from CRC (MRR 0.77 [95% CI 0.65 to 0.89]). Divergence in CRC mortality among individuals residing in different income areas increased over time, with rising CRC mortality observed in the lowest income areas and declining CRC mortality observed in the higher income areas. CONCLUSIONS Individuals residing in lower income neighbourhoods experienced rising CRC mortality despite residing in a jurisdiction with universal health care and should receive increased efforts to reduce CRC mortality. These findings should be of particular interest to the provincial CRC screening programs, which may be able to reduce the disparities in CRC mortality by reducing the disparities in CRC screening participation.

Likelihood inference in generalized linear mixed measurement error models

The generalized linear mixed models (GLMMs) for clustered data are studied when covariates are measured with error. The most conventional measurement error models are based on either linear mixed models (LMMs) or GLMMs. Even without the measurement error, the frequentist analysis of LMM, and particularly of GLMM, is computationally difficult. On the other hand, Bayesian analysis of LMM and GLMM is computationally convenient in both cases without and with the measurement error. Recent introduction of the method of data cloning has made frequentist analysis of mixed models also equally computationally convenient. As an application of data cloning, we conduct a frequentist analysis of GLMM with covariates subject to the measurement error model. The performance of the proposed approach which yields the maximum likelihood estimation is evaluated by two important real data types, Normal and logistic linear mixed measurement error models, and also through simulation studies.

Spatio-temporal modelling using B-spline for disease mapping: analysis of childhood cancer trends

To examine childhood cancer diagnoses in the province of Alberta, Canada during 1983–2004, we construct a generalized additive mixed model for the analysis of geographic and temporal variability of cancer ratios. In this model, spatially correlated random effects and temporal components are adopted. The interaction between space and time is also accommodated. Spatio-temporal models that use conditional autoregressive smoothing across the spatial dimension and B-spline over the temporal dimension are considered. We study the patterns of incidence ratios over time and identify areas with consistently high ratio estimates as areas for potential further investigation. We apply the method of penalized quasi-likelihood to estimate the model parameters. We illustrate this approach using a yearly data set of childhood cancer diagnoses in the province of Alberta, Canada during 1983–2004.

Likelihood inference in small area estimation by combining time-series and cross-sectional data

Using both time-series and cross-sectional data, a linear model incorporating autocorrelated random effects and sampling errors was previously proposed in small area estimation. However, in practice there are many situations that we have time-related counts or proportions in small area estimation; for example a monthly dataset on the number of incidences in small areas. The frequentist analysis of these complex models is computationally difficult. On the other hand, the advent of the Markov chain Monte Carlo algorithm has made the Bayesian analysis of complex models computationally convenient. Recent introduction of the method of data cloning has made frequentist analysis of mixed models also equally computationally convenient. We use data cloning to conduct frequentist analysis of small area estimation for Normal and non-Normal data situations with incorporating cross-sectional and time-series data. Another important feature of the proposed approach is to predict small area parameters by providing prediction intervals. The performance of the proposed approach is evaluated through several simulation studies and also by a real dataset.

Likelihood inference in generalized linear mixed models with two components of dispersion using data cloning

This paper studies generalized linear mixed models (GLMMs) with two components of dispersion. The frequentist analysis of linear mixed model (LMM), and particularly of GLMM, is computationally difficult. On the other hand, the advent of the Markov chain Monte Carlo algorithm has made the Bayesian analysis of LMM and GLMM computationally convenient. The recent introduction of the method of data cloning has made frequentist analysis of mixed models also equally computationally convenient. We use data cloning to conduct frequentist analysis of GLMMs with two components of dispersion based on maximum likelihood estimation (MLE). The resultant estimators of the model parameters are efficient. We discuss the performance of the MLE using the well known salamander mating data, and also through simulation studies.

Spatial modeling using frequentist approach for disease mapping

In this article, a generalized linear mixed model (GLMM) based on a frequentist approach is employed to examine spatial trend of asthma data. However, the frequentist analysis of GLMM is computationally difficult. On the other hand, the Bayesian analysis of GLMM has been computationally convenient due to the advent of Markov chain Monte Carlo algorithms. Recently developed data cloning (DC) method, which yields to maximum likelihood estimate, provides frequentist approach to complex mixed models and equally computationally convenient method. We use DC to conduct frequentist analysis of spatial models. The advantages of the DC approach are that the answers are independent of the choice of the priors, non-estimable parameters are flagged automatically, and the possibility of improper posterior distributions is completely avoided. We illustrate this approach using a real dataset of asthma visits to hospital in the province of Manitoba, Canada, during 2000–2010. The performance of the DC approach in our application is also studied through a simulation study.

Application of three focused cluster detection methods to study geographic variation in the incidence of multiple sclerosis in Manitoba, Canada

Background: Macroscopic geographic variation in the incidence and prevalence of MS is well-recognized. Microscopic geographic variation in the distribution of MS is also recognized, but less well-studied. Most studies have focused on prevalent cases of MS, although studies of variation in disease incidence are more relevant for developing etiologic hypotheses. We aimed to study geographic variation in the incidence of MS using three different methods. Methods: We used population-based administrative (health claims) data to identify 2,290 incident cases of MS in the province of Manitoba, Canada from 1990 to 2006. We applied three focused cluster-detection procedures, including the circular spatial scan statistic (CSS), flexible spatial scan statistic (FSS), and Bayesian disease mapping (BYM), to the dataset. Results: The CSS and FSS methods identified 30 and 26 regions as potential clusters, respectively, although the regions identified differed slightly due to the non-circular shape of some regions in Manitoba. The BYM approach identified 37 regions as potential clusters, again with some differences as compared to the other two methods. Twelve regions were identified as potential clusters by all three methods. All methods identified the western part of the city of Winnipeg as a significant cluster. Using the BYM approach, the incidence of MS was highest among areas of higher socioeconomic status. Conclusions: Two methods CSS and FSS only capture geographical variations and are not able to control for confounders at the same time which may lead to mis-identification of clusters. However, the BYM method can simultaneously identify geographical variations and control for possible confounders.

Estimation of mean squared error of model-based estimators of small area means under a nested error linear regression model

Most of the research on small area estimation has focused on unconditional mean squared error (MSE) estimation under an assumed small area model. Datta et al. (2011) [3] studied conditional MSE estimation of a small area mean under a basic area-level model, conditional on the area-specific direct estimator. In this paper, estimation of a small area mean under a nested error linear regression model is studied, using an empirical best (or Bayes) estimator or a weighted estimator with fixed weights. We derive second-order approximations to unconditional MSE and conditional MSE given the area-specific data and obtain associated second-order correct MSE estimators. The performance of MSE estimators is studied using a simulation experiment as well as a real dataset.