Anup Dewanji

Time-Series Analyses of Air Pollution and Mortality in the United States: A Subsampling Approach

Background: Hierarchical Bayesian methods have been used in previous papers to estimate national mean effects of air pollutants on daily deaths in time-series analyses. Objectives: We obtained maximum likelihood estimates of the common national effects of the criteria pollutants on mortality based on time-series data from ≤ 108 metropolitan areas in the United States. Methods: We used a subsampling bootstrap procedure to obtain the maximum likelihood estimates and confidence bounds for common national effects of the criteria pollutants, as measured by the percentage increase in daily mortality associated with a unit increase in daily 24-hr mean pollutant concentration on the previous day, while controlling for weather and temporal trends. We considered five pollutants [PM10, ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2)] in single- and multipollutant analyses. Flexible ambient concentration–response models for the pollutant effects were considered as well. We performed limited sensitivity analyses with different degrees of freedom for time trends. Results: In single-pollutant models, we observed significant associations of daily deaths with all pollutants. The O3 coefficient was highly sensitive to the degree of smoothing of time trends. Among the gases, SO2 and NO2 were most strongly associated with mortality. The flexible ambient concentration–response curve for O3 showed evidence of nonlinearity and a threshold at about 30 ppb. Conclusions: Differences between the results of our analyses and those reported from using the Bayesian approach suggest that estimates of the quantitative impact of pollutants depend on the choice of statistical approach, although results are not directly comparable because they are based on different data. In addition, the estimate of the O3-mortality coefficient depends on the amount of smoothing of time trends.

A Poisson process approach for recurrent event data with environmental covariates.

We present a Poisson process formulation for studying the association between environmental covariates and recurrent events. The standard methods, which compare the covariate values (at event times) of the individuals having the event with those of the individuals at risk at that time, cannot accommodate environmental covariates, because they are identical for all individuals at risk. We suggest a flexible parametric model and a conditional likelihood analysis. We illustrate our method through an analysis of data on multiple hospital admissions for chronic respiratory disease in King County in relation to air pollution indices. Copyright

Analysis of spontaneous adverse drug reaction (ADR) reports using supplementary information

Assessment of safety of newly marketed drugs is an important public health issue. Once the drug is in the market, clinicians and/or health professionals are responsible for recognizing and reporting suspected side effects known as adverse drug reaction (ADR). Such reports are collected in a so-called spontaneous reporting (SR) system. The primary purpose of spontaneous ADR reporting is to provide early warnings or suspicions, which have not been recognized prior to marketing of a drug because of limitations of clinical trials. We shall discuss the existing work to analyze the SR database and their drawbacks and also suggest methodologies to tackle these drawbacks by defining a source population and considering the problem of under-reporting, with the help of supplementary data. Unbiased estimate of population odds-ratio has been obtained and the corresponding asymptotic results are derived.

Effect of reporting bias in the analysis of spontaneous reporting data

It is well-known that a spontaneous reporting system suffers from significant under-reporting of adverse drug reactions from the source population. The existing methods do not adjust for such under-reporting for the calculation of measures of association between a drug and the adverse drug reaction under study. Often there is direct and/or indirect information on the reporting probabilities. This work incorporates the reporting probabilities into existing methodologies, specifically to Bayesian confidence propagation neural network and DuMouchels empirical Bayesian methods, and shows how the two methods lead to biased results in the presence of under-reporting. Considering all the cases to be reported, the association measure for the source population can be estimated by using only exposure information through a reference sample from the source population.

Number and size distribution of colorectal adenomas under the multistage clonal expansion model of cancer.

Colorectal cancer (CRC) is believed to arise from mutant stem cells in colonic crypts that undergo a well-characterized progression involving benign adenoma, the precursor to invasive carcinoma. Although a number of (epi)genetic events have been identified as drivers of this process, little is known about the dynamics involved in the stage-wise progression from the first appearance of an adenoma to its ultimate conversion to malignant cancer. By the time adenomas become endoscopically detectable (i.e., are in the range of 1–2 mm in diameter), adenomas are already comprised of hundreds of thousands of cells and may have been in existence for several years if not decades. Thus, a large fraction of adenomas may actually remain undetected during endoscopic screening and, at least in principle, could give rise to cancer before they are detected. It is therefore of importance to establish what fraction of adenomas is detectable, both as a function of when the colon is screened for neoplasia and as a function of the achievable detection limit. To this end, we have derived mathematical expressions for the detectable adenoma number and size distributions based on a recently developed stochastic model of CRC. Our results and illustrations using these expressions suggest (1) that screening efficacy is critically dependent on the detection threshold and implicit knowledge of the relevant stem cell fraction in adenomas, (2) that a large fraction of non-extinct adenomas remains likely undetected assuming plausible detection thresholds and cell division rates, and (3), under a realistic description of adenoma initiation, growth and progression to CRC, the empirical prevalence of adenomas is likely inflated with lesions that are not on the pathway to cancer.

Parametric Estimation of Quality Adjusted Lifetime (QAL) Distribution in Simple Illness-Death Model

We discuss parametric estimation of quality adjusted lifetime distribution in simple illness-death model. Model parameters are estimated by maximum likelihood method. The distribution of QAL is estimated by replacing the parameters by their estimates. A regression model is also considered. A simulation study investigates bias and precision of the estimate of QAL distribution and compares it with an existing nonparametric estimate. Another simulation study investigates the effect of model misspecification. Application of our methodology has been illustrated using the Stanford heart transplant data.

A biologically based model for the analysis of premalignant foci of arbitrary shape

In many animal carcinogenesis experiments, quantitative data on putative premalignant foci are now routinely collected. Moolgavkar et al. [Carcinogenesis 11:1271 (1990)] considered the analysis of such data from a rat hepatocarcinogenesis experiment within the framework of a two-stage model for carcinogenesis using the assumption that the premalignant clones were spherical. This assumption seems questionable in many organs, including the liver. In this paper, it is relaxed and arbitrary shapes are allowed for the clones. The proposed method is illustrated by reanalysis of the data considered in the earlier paper. The new analysis yields parameter estimates that are more plausible biologically than those of the original analysis.

On a likelihood-based approach in nonparametric smoothing and cross-validation☆

A likelihood-based generalization of usual kernel and nearest-neighbor-type smoothing techniques and a related extension of the least-squares leave-one-out cross-validation are explored in a generalized regression set up. Several attractive features of the procedure are discussed and asymptotic properties of the resulting nonparametric function estimate are derived under suitable regularity conditions. Large sample performance of likelihood-based leave-one-out cross validation is investigated by means of certain asymptotic expansions.

Model Selection Approach for Distributed Fault Detection in Wireless Sensor Networks

Sensor networks aim at monitoring their surroundings for event detection and object tracking. But, due to failure or death of sensors, false signal can be transmitted. In this paper, we consider the problems of distributed fault detection in wireless sensor network (WSN). In particular, we consider how to take decision regarding fault detection in a noisy environment as a result of false detection or false response of event by some sensors, where the sensors are placed at the center of regular hexagons and the event can occur at only one hexagon. We propose fault detection schemes that explicitly introduce the error probabilities into the optimal event detection process. We introduce two types of detection probabilities, one for the center node, where the event occurs, and the other one for the adjacent nodes. This second type of detection probability is new in sensor network literature. We develop schemes under the model selection procedure and multiple model selection procedure and use the concept of Bayesian model averaging to identify a set of likely fault sensors and obtain an average predictive error.

Working Status and Anxiety Levels of Urban Educated Women in Calcutta

The primary objective of the present study was to assess the impact of out-of-home employment on anxiety levels of mothers. A study group of working mothers resident in Calcutta (India) was compared with a socioeconomically similar group of non-working mothers with respect to their anxiety level, measured by the Anxiety Scale Questionnaire, in terms of the total anxiety score and its various personality components. The possible relationships between anxiety score and age of these mothers as well as their children were studied. Non-working mothers showed higher anxiety levels than their working counterparts with respect to the total anxiety score as well as its components, although the differences were statistically non-significant. The anxiety scores of non-working mothers showed increasing values with increasing age of children. This trend was absent among the working mothers. The age of these mothers was not related to their anxiety level.