Approximate Bayesian inference for case-crossover models

Traffic-Calming Measures and Road Traffic Collisions and Injuries: A Spatiotemporal Analysis

Traffic-calming measures (TCMs) are physical modifications of the road network aimed at making the roads safer. Although researchers have reported reductions in numbers of road crashes and injuries tied to the presence of TCMs, such studies have been criticized for their pre-/post- designs. In this study, we aimed to complement our knowledge of TCMs' effectiveness by assessing their impact using a longitudinal design. The implementation of 8 TCMs, including curb extensions and speed humps, was evaluated at the intersection and census tract levels in Montreal, Quebec, Canada, from 2012 to 2019. The primary outcome was fatal or serious collisions among all road users. Inference was performed using a Bayesian implementation of conditional Poisson regression in which random effects were used to account for the spatiotemporal variation in collisions. TCMs were generally implemented on local roads, although most collisions occurred on arterial roads. Overall, there was weak evidence that TCMs were associated with study outcomes. However, subgroup analyses of intersections on local roads suggested a reduction in collision rates due to TCMs (median incidence rate ratio, 0.31; 95% credible interval: 0.12, 0.86). To improve road safety, effective counterparts of TCMs on arterial roads must be identified and implemented.

Bayesian Approach to Disease Risk Evaluation Based on Air Pollution and Weather Conditions

BACKGROUND

Environmental factors such as meteorological conditions and air pollutants are recognized as important for human health, where mortality and morbidity of certain diseases may be related to abrupt climate change or air pollutant concentration. In the literature, environmental factors have been identified as risk factors for chronic diseases such as ischemic heart disease. However, the likelihood evaluation of the disease occurrence probability due to environmental factors is missing.

METHOD

We defined people aged 51-90 years who were free from ischemic heart disease (ICD9: 410-414) in 1996-2002 as the susceptible group. A Bayesian conditional logistic regression model based on a case-crossover design was utilized to construct a risk information system and applied to data from three databases in Taiwan: air quality variables from the Environmental Protection Administration (EPA), meteorological parameters from the Central Weather Bureau (CWB), and subject information from the National Health Insurance Research Database (NHIRD).

RESULTS

People living in different geographic regions in Taiwan were found to have different risk factors; thus, disease risk alert intervals varied in the three regions.

CONCLUSIONS

Disease risk alert intervals can be a reference for weather bureaus to issue health warnings. With early warnings, susceptible groups can take measures to avoid exacerbation of disease when meteorological conditions and air pollution become hazardous to their health.

Bayesian inference for Cox proportional hazard models with partial likelihoods, nonlinear covariate effects and correlated observations

We propose a flexible and scalable approximate Bayesian inference methodology for the Cox Proportional Hazards model with partial likelihood. The model we consider includes nonlinear covariate effects and correlated survival times. The proposed method is based on nested approximations and adaptive quadrature, and the computational burden of working with the log-partial likelihood is mitigated through automatic differentiation and Laplace approximation. We provide two simulation studies to show the accuracy of the proposed approach, compared with the existing methods. We demonstrate the practical utility of our method and its computational advantages over Markov Chain Monte Carlo methods through the analysis of Kidney infection times, which are paired, and the analysis of Leukemia survival times with a semi-parametric covariate effect and spatial variation.