Evaluation of statistical methods for left-censored environmental data with nonuniform detection limits.
Environ Toxicol Chem
; 25(9): 2533-40, 2006 Sep.
Article
em En
| MEDLINE
| ID: mdl-16986810
ABSTRACT
Monte Carlo simulations were used to evaluate statistical methods for estimating 95% upper confidence limits of mean constituent concentrations for left-censored data with nonuniform detection limits. Two primary scenarios were evaluated data sets with 15 to 50% nondetected samples and data sets with 51 to 80% nondetected samples. Sample size and the percentage of nondetected samples were allowed to vary randomly to generate a variety of left-censored data sets. All statistical methods were evaluated for efficacy by comparing the 95% upper confidence limits for the left-censored data with the 95% upper confidence limits for the noncensored data and by determining percent coverage of the true mean (micro). For data sets with 15 to 50% nondetected samples, the trimmed mean, Winsorization, Aitchison's, and log-probit regression methods were evaluated. The log-probit regression was the only method that yielded sufficient coverage (99-100%) of micro, as well as a high correlation coefficient (r2 = 0.99) and small average percent residuals (-0.1%) between upper confidence limits for censored versus noncensored data sets. For data sets with 51 to 80% nondetected samples, a bounding method was effective (r2 = 0.96 - 0.99, average residual = -5% to -7%, 95-98% coverage of micro), except when applied to distributions with low coefficients of variation (standard deviation/micro < 0.5). Thus, the following recommendations are supported by this research data sets with 15 to 50% nondetected samples--log-probit regression method and use of Chebyshev theorem to estimate 95% upper confidence limits; data sets with 51 to 80% nondetected samples-bounding method and use of Chebyshev theorem to estimate 95% upper confidence limits.
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Coleções:
01-internacional
Contexto em Saúde:
1_ASSA2030
/
2_ODS3
Base de dados:
MEDLINE
Assunto principal:
Monitoramento Ambiental
/
Método de Monte Carlo
/
Modelos Estatísticos
Tipo de estudo:
Diagnostic_studies
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Health_economic_evaluation
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Risk_factors_studies
Idioma:
En
Revista:
Environ Toxicol Chem
Ano de publicação:
2006
Tipo de documento:
Article