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Model-based imputation approach for data analysis in the presence of non-detects.

Authors
Krishnamoorthy-K; Mallick-A; Mathew-T
Source
Ann Occup Hyg 2009 Apr; 53(3):249-263
NIOSHTIC No.
20044429
Abstract
A model-based multiple imputation approach for analyzing sample data with non-detects is proposed. The imputation approach involves randomly generating observations below the detection limit using the detected sample values and then analyzing the data using complete sample techniques, along with suitable adjustments to account for the imputation. The method is described for the normal case and is illustrated for making inferences for constructing prediction limits, tolerance limits, for setting an upper bound for an exceedance probability and for interval estimation of a log-normal mean. Two imputation approaches are investigated in the paper: one uses approximate maximum likelihood estimates (MLEs) of the parameters and a second approach uses simple ad hoc estimates that were developed for the specific purpose of imputations. The accuracy of the approaches is verified using Monte Carlo simulation. Simulation studies show that both approaches are very satisfactory for small to moderately large sample sizes, but only the MLE-based approach is satisfactory for large sample sizes. The MLE-based approach can be calibrated to perform very well for large samples. Applicability of the method to the log-normal distribution and the gamma distribution (via a cube root transformation) is outlined. Simulation studies also show that the imputation approach works well for constructing tolerance limits and prediction limits for a gamma distribution. The approach is illustrated using a few practical examples.
Keywords
Sampling; Samplers; Statistical-analysis; Environmental-exposure; Exposure-levels; Monitoring-systems; Humans; Men; Women; Mathematical-models; Author Keywords: confidence interval; exceedance probability; left-censored data; prediction limits; quantiles; tolerance limits; Wilson-Hilferty approximation
Contact
K. Krishnamoorthy, Department of Mathematics, University of Louisiana at Lafayette, Lafayette, LA 70508
CODEN
AOHYA3
Publication Date
20090401
Document Type
Journal Article
Email Address
krishna@louisiana.edu
Funding Type
Grant
Fiscal Year
2009
NTIS Accession No.
NTIS Price
Identifying No.
Grant-Number-R01-OH-003628
Issue of Publication
3
ISSN
0003-4878
Source Name
Annals of Occupational Hygiene
State
MD; LA
Performing Organization
University of Maryland, Baltimore
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