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04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Estimating population distributions when some data are below a limit of detection by using a reverse Kaplan-Meier
Brenda W Gillespie1, Qixuan Chen, Heidi Reichert
1Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109-1070, USA. bgillesp@umich.edu
Epidemiology (Cambridge, Mass.)
|April 14, 2010
Summary
The reverse Kaplan-Meier (KM) estimator is recommended for analyzing left-censored data, outperforming common substitution methods for estimating population percentiles. This survival analysis technique offers more accurate distribution function estimates when data falls below the limit of detection (LOD).
Area of Science:
- Biostatistics
- Survival Analysis
- Environmental Health
Background:
- Left-censored data, where values are below the limit of detection (LOD), presents analytical challenges.
- Survival analysis methods, particularly the reverse Kaplan-Meier (KM) estimator, are suitable for handling such data.
- The reverse KM estimator, though effective, is underutilized due to limited software availability.
Purpose of the Study:
- To describe and illustrate the reverse KM estimator for analyzing left-censored data.
- To compare percentile estimates from the reverse KM estimator with traditional substitution methods (LOD/2, LOD/√2).
- To discuss available software for implementing the reverse KM estimator.
Main Methods:
- The reverse KM estimator was applied to serum dioxin data from UMDES and NHANES.
- Percentile estimates were calculated using the reverse KM estimator and compared to methods that substitute values below the LOD.
- The nonparametric maximum likelihood estimation principle underlies the reverse KM approach.
Main Results:
- Different methods yielded varying percentile estimates, especially when LODs were high or censoring was prevalent.
- The reverse KM estimator proved to be the preferred method for estimating distribution functions and percentiles.
- Software packages like JMP, SAS, and Minitab offer direct calculation of the reverse KM estimator via Turnbull estimator routines.
Conclusions:
- The reverse KM estimator is recommended over substitution methods (LOD/2, LOD/√2) or imputation for left-censored data.
- This method provides a more accurate estimation of the distribution function and population percentiles.
- Increased accessibility through software facilitates wider adoption of the reverse KM estimator.
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