A Bayesian Approach for Summarizing and Modeling Time-Series Exposure Data with Left Censoring.

ABSTRACT:

Objective

Direct reading instruments are valuable tools for measuring exposure as they provide real-time measurements for rapid decision making. However, their use is limited to general survey applications in part due to issues related to their performance. Moreover, statistical analysis of real-time data is complicated by autocorrelation among successive measurements, non-stationary time series, and the presence of left-censoring due to limit-of-detection (LOD). A Bayesian framework is proposed that accounts for non-stationary autocorrelation and LOD issues in exposure time-series data in order to model workplace factors that affect exposure and estimate summary statistics for tasks or other covariates of interest.

Method

A spline-based approach is used to model non-station

SUBMITTER: Houseman EA 

PROVIDER: S-EPMC5712446 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature