Correcting for bias effects due to exposure uncertainty in community noise exposure-response analyses

Abstract

A method for correcting the relationship-distorting effects of sound level uncertainty on community noise exposure-response investigations is presented. The method employs a “calibration model,” which describes how the true exposure-response relationship becomes distorted (shallower) in the presence of sound level uncertainty and other factors. This model was incorporated in a maximum likelihood estimation procedure that solves for the coefficient values that would have been observed in the absence of sound level measurement error. Three interacting situational variables known to attenuate the slopes of fitting functions were incorporated in the model: the sample population sound level uncertainty, the sound level range, and the distribution of sound levels over that range. The method provided unbiased estimates of the slope and intercept when the calibration parameter values were known exactly. Sensitivity to inexact knowledge of the values was also examined. Although the bias effect could be removed, uncertainty in the solved regression coefficients increased with increasing predictor variable (sound level) uncertainty and with data set displacement from the asymptote.