2010
DOI: 10.1097/ede.0b013e3181ce9f08
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Estimating Population Distributions When Some Data Are Below a Limit of Detection by Using a Reverse Kaplan-Meier Estimator

Abstract: The reverse KM estimator is recommended for estimation of the distribution function and population percentiles in preference to commonly used methods such as substituting LOD/2 or LOD/ radical2 for values below the LOD, assuming a known parametric distribution, or using imputation to replace the left-censored values.

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Cited by 89 publications
(74 citation statements)
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“…2d/2e & Supplemental Fig. 3c, a log-rank test was utilized, thus accounting for the variation in the lower limit of detection between samples similar to what has been done for other assays (3739). For all Mann-Whitney comparisons, a two-tailed test was used.…”
Section: Methodsmentioning
confidence: 99%
“…2d/2e & Supplemental Fig. 3c, a log-rank test was utilized, thus accounting for the variation in the lower limit of detection between samples similar to what has been done for other assays (3739). For all Mann-Whitney comparisons, a two-tailed test was used.…”
Section: Methodsmentioning
confidence: 99%
“…23,24 A more appropriate method to compare distributional differences is the use of the reverse Kaplan-Meier estimator. 24 This estimator is a left-censored analog to the common Kaplan-Meier estimator. This is a nonparametric method that makes no distributional assumptions regarding the trace elements.…”
Section: Methodsmentioning
confidence: 99%
“…It also accounts for the non-detects and has been advocated as a preferred method over other more popular ad hoc methods based on “filling in” a value for the non-detect (e.g., fill in the MDL or ½ × MDL). 24 …”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…The estimates provided by this method were comparable to the restricted ML method and its main advantage is its ease of implementation using Microsoft Excel Solver tool (Flynn, 2010). The Kaplan-Meier (K-M) method, which does not assume any distributional shape, estimates summary statistics by constructing a curve akin to an empirical CDF while accounting for censoring (Kaplan and Meier, 1958;Gillespie et al, 2010).…”
Section: Introductionmentioning
confidence: 99%