Methods for Handling Left-Censored Data in Quantitative Microbial Risk Assessment
| Autor: | Amanda M. Wilson, Kelly A. Reynolds, Robert A. Canales, Jennifer Pearce-Walker, Marc P. Verhougstraete |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Uniform distribution (continuous) Mean squared error 030106 microbiology Context (language use) Genome Viral 010501 environmental sciences Risk Assessment 01 natural sciences Applied Microbiology and Biotechnology Standard deviation 03 medical and health sciences Limit of Detection Statistics Environmental Microbiology Humans Computer Simulation Imputation (statistics) Enterovirus 0105 earth and related environmental sciences Mathematics Models Statistical Ecology Public and Environmental Health Microbiology Drinking Water Censoring (statistics) Data set Data Interpretation Statistical Log-normal distribution Water Microbiology Food Science Biotechnology |
| Zdroj: | Applied and Environmental Microbiology. 84 |
| ISSN: | 1098-5336 0099-2240 |
| DOI: | 10.1128/aem.01203-18 |
| Popis: | Data below detection limits, left-censored data, are common in environmental microbiology, and decisions in handling censored data may have implications for quantitative microbial risk assessment (QMRA). In this paper, we utilize simulated data sets informed by real-world enterovirus water data to evaluate methods for handling left-censored data. Data sets were simulated with four censoring degrees (low [10%], medium [35%], high [65%], and severe [90%]) and one real-life censoring example (97%) and were informed by enterovirus data assuming a lognormal distribution with a limit of detection (LOD) of 2.3 genome copies/liter. For each data set, five methods for handling left-censored data were applied: (i) substitution with LOD/ 2 , (ii) lognormal maximum likelihood estimation (MLE) to estimate mean and standard deviation, (iii) Kaplan-Meier estimation (KM), (iv) imputation method using MLE to estimate distribution parameters (MI method 1), and (v) imputation from a uniform distribution (MI method 2). Each data set mean was used to estimate enterovirus dose and infection risk. Root mean square error (RMSE) and bias were used to compare estimated and known doses and infection risks. MI method 1 resulted in the lowest dose and infection risk RMSE and bias ranges for most censoring degrees, predicting infection risks at most 1.17 × 10−2 from known values under 97% censoring. MI method 2 was the next overall best method. For medium to severe censoring, MI method 1 may result in the least error. If unsure of the distribution, MI method 2 may be a preferred method to avoid distribution misspecification. IMPORTANCE This study evaluates methods for handling data with low (10%) to severe (90%) left-censoring within an environmental microbiology context and demonstrates that some of these methods may be appropriate when using data containing concentrations below a limit of detection to estimate infection risks. Additionally, this study uses a skewed data set, which is an issue typically faced by environmental microbiologists. |
| Databáze: | OpenAIRE |
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