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Exposure measurement errors is a problem in nearly all environmental and epidemiological studies. It is a major source of bias and loss of statistical power in standard analysis of data commonly collected in environmental health research. To eliminate or at least reduce the bias, and to increase the statistical power of the analysis, statistical methods will be developed, motivated by application to three environmental epidemiology studies.
These methods should be generalizable to other environmental epidemiology settings as well. Because appropriate use of these methods depends on the assumptions made, we will carefully investigate the validity of the assumptions. When any assumption appears to be violated in the data, we will, when possible, modify the methods to accommodate the departures. If this is not possible, we will, through analytic means and stimulation, explore the sensitivity of the basic methods of assumption violations and develop methods for empirical verification of the assumptions. Three statistical methods will be developed. The first will involve modification of regression calibration to suit the data collected in the three studies. In the second and third, using fully parametric maximum likelihood methods and semi-parametric estimating equations methods, we will fit suitable models which utilize all of the data. These methods permit us to properly account for non-random validation sampling, should there be evidence on this in the data at hand, and the semi-parametric methods will, additionally, allow us to explore the impact of bias induced by mis-specification of the measurement error model. The Berkson measurement error model will be considered, and missing data methodology will be incorporated when relevant.