Bhaswati Ganguli

Arsenic exposure induces genomic hypermethylation

Gene‐specific hypermethylation has previously been detected in Arsenic exposed persons. To monitor the level of whole genome methylation in persons exposed to different levels of Arsenic via drinking water, DNA was extracted from peripheral blood mononuclear cells of 64 persons. Uptake of methyl group from 3H labeled S‐Adenosyl Methionine after incubation of DNA with SssI methylase was measured. Results showed statistically significant (P = 0.0004) decrease in uptake of 3H methyl group in the persons exposed to 250–500 μg/L arsenic, indicating genomic hypermethylation.

The Comparison of Alternative Smoothing Methods for Fitting Non-Linear Exposure-Response Relationships with Cox Models in a Simulation Study

We examined the behavior of alternative smoothing methods for modeling environmental epidemiology data. Model fit can only be examined when the true exposure-response curve is known and so we used simulation studies to examine the performance of penalized splines (P-splines), restricted cubic splines (RCS), natural splines (NS), and fractional polynomials (FP). Survival data were generated under six plausible exposure-response scenarios with a right skewed exposure distribution, typical of environmental exposures. Cox models with each spline or FP were fit to simulated datasets. The best models, e.g. degrees of freedom, were selected using default criteria for each method. The root mean-square error (rMSE) and area difference were computed to assess model fit and bias (difference between the observed and true curves). The test for linearity was a measure of sensitivity and the test of the null was an assessment of statistical power. No one method performed best according to all four measures of performance, however, all methods performed reasonably well. The model fit was best for P-splines for almost all true positive scenarios, although fractional polynomials and RCS were least biased, on average.

A review of multivariate longitudinal data analysis

Repeated observation of multiple outcomes is common in biomedical and public health research. Such experiments result in multivariate longitudinal data, which are unique in the sense that they allow the researcher to study the joint evolution of these outcomes over time. Special methods are required to analyse such data because repeated observations on any given response are likely to be correlated over time while multiple responses measured at a given time point will also be correlated. We review three approaches for analysing such data in the light of the associated theory, applications and software. The first method consists of the application of univariate longitudinal tools to a single summary outcome. The second method aims at estimating regression coefficients without explicitly modelling the underlying covariance structure of the data. The third method combines all the outcomes into a single joint multivariate model. We also introduce a multivariate longitudinal dataset and use it to illustrate some of the techniques discussed in the article.

Feature significance in generalized additive models

This paper develops inference for the significance of features such as peaks and valleys observed in additive modeling through an extension of the SiZer-type methodology of Chaudhuri and Marron (1999) and Godtliebsen et al. (2002, 2004) to the case where the outcome is discrete. We consider the problem of determining the significance of features such as peaks or valleys in observed covariate effects both for the case of additive modeling where the main predictor of interest is univariate as well as the problem of studying the significance of features such as peaks, inclines, ridges and valleys when the main predictor of interest is geographical location. We work with low rank radial spline smoothers to allow to the handling of sparse designs and large sample sizes. Reducing the problem to a Generalised Linear Mixed Model (GLMM) framework enables derivation of simulation-based critical value approximations and guards against the problem of multiple inferences over a range of predictor values. Such a reduction also allows for easy adjustment for confounders including those which have an unknown or complex effect on the outcome. A simulation study indicates that our method has satisfactory power. Finally, we illustrate our methodology on several data sets.

Determination of the functional form of the relationship of covariates to the log hazard ratio in a Cox model

In this paper, we review available methods for determination of the functional form of the relation between a covariate and the log hazard ratio for a Cox model. We pay special attention to the detection of influential observations to the extent that they influence the estimated functional form of the relation between a covariate and the log hazard ratio. Our paper is motivated by a data set from a cohort study of lung cancer and silica exposure, where the nonlinear shape of the estimated log hazard ratio for silica exposure plotted against cumulative exposure and hereafter referred to as the exposure–response curve was greatly affected by whether or not two individuals with the highest exposures were included in the analysis. Formal influence diagnostics did not identify these two individuals but did identify the three highest exposed cases. Removal of these three cases resulted in a biologically plausible exposure–response curve.

Deletion diagnostics for the generalised linear mixed model with independent random effects

The Generalised linear mixed model (GLMM) is widely used for modelling environmental data. However, such data are prone to influential observations, which can distort the estimated exposure-response curve particularly in regions of high exposure. Deletion diagnostics for iterative estimation schemes commonly derive the deleted estimates based on a single iteration of the full system holding certain pivotal quantities such as the information matrix to be constant. In this paper, we present an approximate formula for the deleted estimates and Cooks distance for the GLMM, which does not assume that the estimates of variance parameters are unaffected by deletion. The procedure allows the user to calculate standardised DFBETAs for mean as well as variance parameters. In certain cases such as when using the GLMM as a device for smoothing, such residuals for the variance parameters are interesting in their own right. In general, the procedure leads to deleted estimates of mean parameters, which are corrected for the effect of deletion on variance components as estimation of the two sets of parameters is interdependent. The probabilistic behaviour of these residuals is investigated and a simulation based procedure suggested for their standardisation. The method is used to identify influential individuals in an occupational cohort exposed to silica. The results show that failure to conduct post model fitting diagnostics for variance components can lead to erroneous conclusions about the fitted curve and unstable confidence intervals.

An update on the prevalence of low back pain in Africa : a systematic review and meta-analyses

BackgroundLow back pain (LBP) remains a common health problem and one of the most prevalent musculoskeletal conditions found among developed and developing nations. The following paper reports on an updated search of the current literature into the prevalence of LBP among African nations and highlights the specific challenges faced in retrieving epidemiological information in Africa.MethodsA comprehensive search of all accessible bibliographic databases was conducted. Population-based studies into the prevalence of LBP among children/adolescents and adults living in Africa were included. Methodological quality of included studies was appraised using an adapted tool. Meta-analyses, subgroup analyses, sensitivity analyses and publication bias were also conducted.ResultsSixty-five studies were included in this review. The majority of the studies were conducted in Nigeria (n = 31;47%) and South Africa (n = 16;25%). Forty-three included studies (66.2%) were found to be of higher methodological quality. The pooled lifetime, annual and point prevalence of LBP in Africa was 47% (95% CI 37;58); 57% (95% CI 51;63) and 39% (95% CI 30;47), respectively.ConclusionThis review found that the lifetime, annual and point prevalence of LBP among African nations was considerably higher than or comparable to global LBP prevalence estimates reported. Due to the poor methodological quality found among many of the included studies, the over-representation of affluent countries and the difficulty in sourcing and retrieving potential African studies, it is recommended that future African LBP researchers conduct methodologically robust studies and report their findings in accessible resources.Trial registrationThe original protocol of this systematic review was initially registered on PROSPERO with registration number CRD42014010417 on 09 July 2014.

Why do we Attend Refresher Courses? - A Case Study of Preference Data Analysis

Abtsrcat Participants of a refresher course sponsored by the University Grants Commission (UGC) of India were asked to rank six possible reasons for attending the course. They were later also asked to rate the reasons on a five point preference scale. In this paper, our objective is to characterise and quantify the preference for these six reasons. Standard methods for the analysis of contingency tables are not appropriate for ranked preference data so we develop a variant of the y measure of association for ordinal data to examine excess preference in pairwise comparisons. The bootstrap is used to derive p-values and condence intervals for the proposed statistic. Finally, we fit a Bradley-Terry regression model to the rankings and estimate the preference for each reason in terms of ‘worth’ parameters. We repeat this analysis on the ratings data and estimate the worth parameters as functions of covariates. The model is further extended to include dependencies between the ratings. Salient results indicate that career advancement has the highest preference across all covariate classes while the preference for service to society is by far the lowest. However, the magnitude of the preferences is influenced by the number of years for which a participant has taught and whether or not he holds a doctoral degree. There are some differences in conclusions from the analysis of the ratings and rankings datasets.