Modeling observations with a detection limit using a truncated normal distribution with censoring.

ABSTRACT:

Background

When data are collected subject to a detection limit, observations below the detection limit may be considered censored. In addition, the domain of such observations may be restricted; for example, values may be required to be non-negative.

Methods

We propose a method for estimating population mean and variance from censored observations that accounts for known domain restriction. The method finds maximum likelihood estimates assuming an underlying truncated normal distribution.

Results

We show that our method, tcensReg, has lower bias, Type I error rates, and mean squared error than other methods commonly used for data with detection limits such as Tobit regression and single imputation under a range of simulation settings from mild to heavy censoring and

SUBMITTER: Williams JR 

PROVIDER: S-EPMC7322891 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature