A new statistical test for latent class in censored data due to detection limit

Stat Med. 2021 Feb 10;40(3):779-798. doi: 10.1002/sim.8802. Epub 2020 Nov 6.

Abstract

Biomarkers of interest in urine, serum, or other biological matrices often have an assay limit of detection. When concentration levels of the biomarkers for some subjects fall below the limit, the measures for those subjects are censored. Censored data due to detection limits are very common in public health and medical research. If censored data from a single exposure group follow a normal distribution or follow a normal distribution after some transformations, Tobit regression models can be applied. Given a Tobit regression model and a detection limit, the proportion of censored data can be determined. However, in practice, it is common that the data can exhibit excessive censored observations beyond what would be expected under a Tobit regression model. One common cause is heterogeneity of the study population, that is, there exists a subpopulation who lack such biomarkers and their values are always under the detection limit, and hence are censored. In this article, we develop a new test for testing such latent class under a Tobit regression model by directly comparing the amount of observed censored data with what would be expected under the Tobit regression model. A closed form of the test statistic as well as its asymptotic properties are derived based on estimating equations. Simulation studies are conducted to investigate the performance of the new test and compare the new one with the existing ones including the Wald test, likelihood ratio test, and score test. Two real data examples are also included for illustrative purpose.

Keywords: Tobit regression model; Wald test; censored normal regression; detection limit; latent class; likelihood ratio test; score test.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Computer Simulation
  • Humans
  • Likelihood Functions
  • Limit of Detection
  • Models, Statistical*