Dataset Information

Application of Bayesian Additive Regression Trees for Estimating Daily Concentrations of PM_2.5 Components.

ABSTRACT: Bayesian additive regression tree (BART) is a recent statistical method that combines ensemble learning and nonparametric regression. BART is constructed under a probabilistic framework that also allows for model-based prediction uncertainty quantification. We evaluated the application of BART in predicting daily concentrations of four fine particulate matter (PM_2.5) components (elemental carbon, organic carbon, nitrate, and sulfate) in California during the period 2005 to 2014. We demonstrate in this paper how BART can be tuned to optimize prediction performance and how to evaluate variable importance. Our BART models included, as predictors, a large suite of land-use variables, meteorological conditions, satellite-derived aerosol optical depth parameters, and simulations from a chemical transport model. In cross-validation experiments, BART demonstrated good out-of-sample prediction performance at monitoring locations (R² from 0.62 to 0.73). More importantly, prediction intervals associated with concentration estimates from BART showed good coverage probability at locations with and without monitoring data. In our case study, major PM_2.5 components could be estimated with good accuracy, especially when collocated PM_2.5 total mass observations were available. In conclusion, BART is an attractive approach for modeling ambient air pollution levels, especially for its ability to provide uncertainty in estimates that may be useful for subsequent health impact and health effect analyses.

SUBMITTER: Zhang T

PROVIDER: S-EPMC8315111 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Individualized treatment rules can improve health outcomes by recognizing that patients may respond differently to treatment and assigning therapy with the most desirable predicted outcome for each individual. Flexible and efficient prediction models are desired as a basis for such individualized treatment rules to handle potentially complex interactions between patient factors and treatment. Modern Bayesian semiparametric and nonparametric regression models provide an attractive avenue in this regard as these allow natural posterior uncertainty quantification of patient specific treatment decisions as well as the population wide value of the prediction-based individualized treatment rule. In addition, via the use of such models, inference is also available for the value of the optimal individualized treatment rules. We propose such an approach and implement it using Bayesian Additive Regression Trees as this model has been shown to perform well in fitting nonparametric regression functions to continuous and binary responses, even with many covariates. It is also computationally efficient for use in practice. With Bayesian Additive Regression Trees, we investigate a treatment strategy which utilizes individualized predictions of patient outcomes from Bayesian Additive Regression Trees models. Posterior distributions of patient outcomes under each treatment are used to assign the treatment that maximizes the expected posterior utility. We also describe how to approximate such a treatment policy with a clinically interpretable individualized treatment rule, and quantify its expected outcome. The proposed method performs very well in extensive simulation studies in comparison with several existing methods. We illustrate the usage of the proposed method to identify an individualized choice of conditioning regimen for patients undergoing hematopoietic cell transplantation and quantify the value of this method of choice in relation to the optimal individualized treatment rule as well as non-individualized treatment strategies.

Project description:Predicting long-term spatiotemporal characteristics of fine particulate matter (PM2.5) is important in China to understand historical levels of PM2.5, to support health effects research of both long-term and short-term exposures to PM2.5, and to evaluate the efficacy of air pollution control policies. Satellite-retrieved aerosol optical depth (AOD) provides a unique opportunity to characterize the long-term trends of ground-level PM2.5 at high spatial resolution. However, the missing rate of AOD in Northeastern China (NEC) is very high, especially in winter, and challenges the accuracy of long-term predictions of PM2.5 if left unresolved. Using random forest algorithms, this study developed a gap-filling approach combing satellite AOD, meteorological data, land use parameters, population and visibility in the NEC during 2005-2016. The model, including all predictors, combined with a model without AOD was able to fill the gap of PM2.5 predictions caused by missing AOD at 1-km resolution. The R2 (RMSE) of the full-coverage predictions was 0.81 (18.5 μg/m3) at the daily level. Gap-filled PM2.5 predictions on days with missing AOD reduced the relative prediction error from 28% to 2.5% in winter. The leave-one-year-out-cross-validation R2 (RMSE) of the full-coverage predictions was 0.65 (16.3 μg/m3) at the monthly level, indicating relatively high accuracy of predicted historical PM2.5 concentrations. Our results suggested that AOD helped increase the reliability of historical PM2.5 prediction when ground PM2.5 measurements were unavailable, even though predictions from the AOD model only accounted for approximate 37% of the whole dataset. Predicted PM2.5 level in NEC have increased since 2005, reached its peak during 2013-2015, then saw a major decline in 2016. Our high-resolution predictions also showed a south to north gradient and many pollution hot spots in the city clusters surrounding provincial capitals, as well as within large cities. Overall, by combining predictions from the AOD model with higher accuracy and predictions from the non-AOD model to achieve full coverage, our modeling approach could produce long-term, full-coverage historical PM2.5 levels in high-latitude areas in China, despite the widespread and persistent AOD missingness.

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Application of Bayesian Additive Regression Trees for Estimating Daily Concentrations of PM_2.5 Components.

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Dataset Information

Application of Bayesian Additive Regression Trees for Estimating Daily Concentrations of PM2.5 Components.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Application of Bayesian Additive Regression Trees for Estimating Daily Concentrations of PM_2.5 Components.