Estimation of exposure distribution adjusting for association between exposure level and detection limit

Statistics in Medicine
Yuchen YangLi Chen

Abstract

In environmental exposure studies, it is common to observe a portion of exposure measurements to fall below experimentally determined detection limits (DLs). The reverse Kaplan-Meier estimator, which mimics the well-known Kaplan-Meier estimator for right-censored survival data with the scale reversed, has been recommended for estimating the exposure distribution for the data subject to DLs because it does not require any distributional assumption. However, the reverse Kaplan-Meier estimator requires the independence assumption between the exposure level and DL and can lead to biased results when this assumption is violated. We propose a kernel-smoothed nonparametric estimator for the exposure distribution without imposing any independence assumption between the exposure level and DL. We show that the proposed estimator is consistent and asymptotically normal. Simulation studies demonstrate that the proposed estimator performs well in practical situations. A colon cancer study is provided for illustration. Copyright © 2017 John Wiley & Sons, Ltd.

References

Dec 8, 2004·Environmental Health Perspectives·Jay H LubinPatricia Hartge
Feb 5, 2008·Clinical Cancer Research : an Official Journal of the American Association for Cancer Research·Li LiGraham Casey
Aug 12, 2009·International Journal of Epidemiology·Stephen R ColeEnrique F Schisterman
Dec 1, 2011·Journal of Environmental Pathology, Toxicology and Oncology : Official Organ of the International Society for Environmental Toxicology and Cancer·Nancy JohnsonLi Li
Jan 13, 2012·CA: a Cancer Journal for Clinicians·Rebecca SiegelAhmedin Jemal
Sep 1, 2010·Biometrika·Li ChenDonglin Zeng
Jul 20, 2014·Statistical Methods in Medical Research·N D StaplinP J Roderick
Oct 22, 2014·Reviews on Environmental Health·Nurul Hafiza Ab RazakZailina Hashim

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