Bayesian model averaging approach in health effects studies: Sensitivity analyses using PM10 and cardiopulmonary hospital admissions in Allegheny County, Pennsylvania and simulated data

AbstractGeneralized Additive Models (GAMs) with natural cubic splines (NS) as smoothing functions have become a standard analytical tool in time series studies of health effects of air pollution. However, standard model selection procedures ignore the model uncertainty that may lead to biased estimates, in particular those of the lagged effects. We addressed this issue by Bayesian model averaging (BMA) approach which accounts for model uncertainty by combining information from all possible models where GAMs and NS were used. Firstly, we conducted a sensitivity analysis with simulation studies for Bayesian model averaging with different calibrated hyperparameters contained in the posterior model probabilities. Our results indicated the importance of selecting the optimum degree of lagging for variables, based not only on maximizing the likelihood, but also by considering the possible effects of concurvity, consistency of degree of lagging, and biological plausibility. This was illustrated by analyses of the Allegheny County Air Pollution Study (ACAPS) where the quantity of interest was the relative risk of cardiopulmonary hospital admissions for a 20 μg/m3 increase in PM10 values for the current day. Results showed that the posterior means of the relative risk and 95% posterior probability intervals were close to each other under different choices of the prior distributions. Simulation results were consistent with these findings. It was also found that using lag variables in the model when there is only same day effect, may underestimate the relative risk attributed to the same day effect

High-resolution millennial and centennial scale Holocene monsoon variability in the Higher Central Himalayas

Relict lake sediments situated within the transition of the lesser and higher central Himalayas show a persistent millennial to centennial-scale monsoon variability during the Holocene. Based on high resolution geochemical data supported by radiocarbon dating, six phases of enhanced Indian Summer Monsoon (ISM) with varying magnitude have been identified. These are dated between 10,000-9600, 9500-9200, 8600-5800, 5000-4200, 3500-2400 and 1800-1000 cal yr BP. The millennial and multi-centennial-scale phases of enhanced ISM are broadly comparable with the existing continental and marine records from the monsoon dominated region of SE Asia, suggesting sensitivity of the regions to short-term climatic perturbations. Further, the study observed that the phases of weakened ISM largely correlate with the drift-ice record of the northern Atlantic implying a coupling between short-term solar irradiance induced glacial boundary condition in the northern Atlantic and the millennial and multi-centennial scale monsoon variability in the central Himalaya

On the Construction of Cyclic Collineations for Obtaining a Balanced Set of Prime-powered Lattice Designs

12 pages, 1 article*On the Construction of Cyclic Collineations for Obtaining a Balanced Set of Prime-powered Lattice Designs* (Mazumdar, Sati) 12 page

Estimating treatment effects from longitudinal clinical trial data with missing values: comparative analyses using different methods

The selection of a method for estimating treatment effects in an intent-to-treat analysis from clinical trial data with missing values often depends on the field of practice. The last observation carried forward (LOCF) analysis assumes that the responses do not change after dropout. Such an assumption is often unrealistic. Analysis with completers only requires that missing values occur completely at random (MCAR). Ignorable maximum likelihood (IML) and multiple imputation (MI) methods require that data are missing at random (MAR). We applied these four methods to a randomized clinical trial comparing anti-depressant effects in an elderly depressed group of patients using a mixed model to describe the course of the treatment effects. Results from an explanatory approach showed a significant difference between the treatments using LOCF and IML methods. Statistical tests indicate violation of the MCAR assumption favoring the flexible IML and MI methods. IML and MI methods were repeated under the pragmatic approach, using data collected after termination of protocol treatment and compared with previously reported results using piecewise splines and rescue (treatment adjustment) pragmatic analysis. No significant treatment differences were found. We conclude that attention to the missing-data mechanism should be an integral part in analysis of clinical trial data