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.
|Number of pages||12|
|Journal||Statistics in Medicine|
|State||Published - Aug 15 2017|
Bibliographical noteFunding Information:
This research was supported by the National Cancer Institute under grants R03CA179661 and P30CA177558. The colon cancer study was supported by the National Cancer Institute under grant R01CA136726. The authors thank two referees and an associate editor for their helpful comments.
Copyright © 2017 John Wiley & Sons, Ltd.
- detection limits
- environmental exposure
- kernel smoothing
- left-censored data
- nonparametric estimator
ASJC Scopus subject areas
- Statistics and Probability