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Generalized methods-of-moments estimation and inference for the assessment of multiple imperfect measures of diet and physical activity in validation studies

Banff International Research Station for Mathematical Innovation and Discovery

Accurate and precise measurement of diet and physical activity (PA) in free-living populations is difficult. As a result, key findings in nutritional epidemiology have been controversial, a notable example of this being the relation of dietary fat intake to breast cancer risk. There is a long literature in statistics on methods to adjust relative risk estimates for cancer and other chronic diseases for bias due to measurement error in long-term dietary intake and PA. To use the popular regression calibration method to correct for the bias, the de-attenuation factor needs to be estimated. Popular is used here in the sense that it is virtually the only method for correcting for bias due to exposure measurement error that has been used in applications, and there are hundreds of published instances of this. In this talk, we develop semi-parametric generalized methods of moments estimators for the de-attenuation factor and other quantities of interest, in particular, the correlation of each surrogate measure with the unobserved truth and intra-class correlation coefficients characterizing the random within-person variation around each measurement. The method makes assumptions only about the first two moments of the multivariate distribution between the measures. The robust variance is derived to allow asymptotic inference. We consider a one-step method which is theoretically inefficient, as well as fully efficient methods that are iterative. For some variables of interest, such as total energy intake, protein density, and total PA, there may be unbiased gold standards (X) available and when they are available, they are used. When these are not available and even when so, we consider other objective (W) and subjective measures (Z), such as biased (concentration) biomarkers, self-report, accelerometer and pulse, as means of estimating the de-attenuation factor and other quantities of interest. Measurements denoted W are assumed to have errors uncorrelated with all other measurements, and those denoted Z are allowed to have correlated errors with one or more of the other measures. Harvards Womens Lifestyle Validation Study (WLVS) assessed diet and physical activity over a 1 year period among 777 women. Total physical activity was assessed by doubly labeled water, often considered to be the gold standard for energy expenditure, accelerometer, resting pulse, physical activity questionnaire (PAQ), and ACT24, an on-line PA assessment tool. Thus, dim(X)=1, dim(Z)=2, and dim(W)=2. Using all 5 of these measures, the deattenuation factor (kcal/ MET-hours) for total physical activity assessed by the PAQ was estimated to be 4.09 (95% CI 0.94, 7.24) and for ACT24 5.59 (1.43, 9.75). These de-attenuation factors are calibrating the units of the PAQ and ACT24 from MET-hours/day to kcal/day, as well as adjusting for bias due to measurement error. In addition, using all 5 measures, the respective correlations of PAQ and ACT24 with truth were 0.36 (0.30, 0.41) and 0.32 (0.26, 0.38), respectively, and correlations of the accelerometer and resting pulse with truth were 0.891 (0.887, 0.893) and -0.20 (-0.32, -0.71) respectively. Little gain in efficiency between the one-step and fully iterated estimators was evident in this example. User-friendly publicly available software is under development.

Mathematics; Statistics; Probability theory and stochastic processes

video/mp4

Author affiliation: Harvard T.H. Chan School of Public Health

2017-02-14

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/60575

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/