Using SIMEX and regression calibration to correct for mixtures of classical and Berkson measurement error

Abstract

BACKGROUND AND AIM Measurement error in modelled air pollution data is complex consisting of classical and Berkson error components. As part of the MELONS project, we investigated the adequacy of regression calibration and SIMEX in correcting health effect estimates when measurement error contains both components and may also be spatially correlated. METHODS In a simulation study of 5,000 subjects, we considered scenarios based on the amount of total measurement error (either 20% or 40% of the variance in the true annual average pollutant concentrations); the proportion of error that was classical (0%,20%,40%,60%, 80% or 100%); and whether total measurement error was homoscedastic or heteroscedastic. Within each simulation, we obtained a dataset of true, and error prone annual average pollutant concentrations and outcome data linked to the true by a hazard ratio of 1.041 per 10µg/m³. The error prone pollutant concentrations were then used as the exposure variable in a Cox regression, and for error correction, a 5% hypothetical validation sub-sample of the data were randomly selected and used to estimate the classical and Berkson error variances. Based on 1,600 simulations per scenario we investigated mean percent bias in the estimated log hazard ratio and the ability of regression calibration and SIMEX to correct for this bias. RESULTS Mean percent bias in the uncorrected log hazard ratio was typically negative (i.e., towards the null) ranging from -27.4% to +2.0% across scenarios. Mean percent bias in the corrected estimates ranged from -4.0% to +5.7% for regression calibration and -6.0% to +4.2% for SIMEX. CONCLUSIONS Across a range of scenarios varying the mix of classical and Berkson error and allowing total measurement error to vary spatially, SIMEX and regression calibration produced corrected log hazard ratios with relatively small mean percent bias. KEYWORDS Measurement Error, SIMEX, Regression Calibration