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Percentile estimation using variable censored data.

Samuel P Caudill1, Lee-Yang Wong, Wayman E Turner

  • 1Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, 4770 Buford Highway NE, Atlanta, GA 30341, USA. SPCl@cdc.gov

Chemosphere
|February 3, 2007
PubMed
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Estimating environmental data below the limit of detection (LOD) requires assuming a log-normal distribution. This study presents methods for accurate percentile estimation and confidence intervals for such censored environmental measurements.

Area of Science:

  • Environmental Science
  • Biostatistics
  • Chemical Analysis

Background:

  • Estimating summary statistics from environmental data censored below the limit of detection (LOD) is a significant challenge.
  • Common methods like Tobit regression and multiple imputation require assumptions about data distribution.
  • Log-normal distribution is frequently assumed for environmental measurements, but its application with censored data needs careful consideration.

Purpose of the Study:

  • To describe methods for estimating percentiles and confidence intervals for log-normally distributed data with a fraction of results below the LOD.
  • To evaluate the bias and confidence interval coverage probability of proposed estimation methods through simulations.
  • To investigate the impact of variable LODs on multiple imputation for environmental congener measurements.

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Main Methods:

  • Development and description of statistical methods for percentile and confidence interval estimation under log-normal assumptions with left-censored data.
  • Limited simulation studies to assess the performance (bias and coverage) of the proposed estimation techniques.
  • Application of estimation methods to real-world data, specifically 2,3,7,8-tetrachloro dibenzo-p-dioxin (2,3,7,8-TCDD) from a human blood serum exposure study.

Main Results:

  • Simulation results demonstrate the bias of the proposed estimates and the coverage probability of their associated confidence intervals.
  • The study provides insights into the effectiveness of the developed methods for handling censored environmental data.
  • Simulation results also highlight the effect of variable LODs on the multiple imputation process for environmental congener measurements.

Conclusions:

  • The presented methods offer a robust approach for analyzing log-normal environmental data with results below the limit of detection.
  • Accurate estimation of percentiles and confidence intervals is crucial for environmental exposure studies, especially with censored data.
  • Understanding the impact of variable LODs is essential for reliable statistical analysis in environmental monitoring.