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Statistical tests for latent class in censored data due to detection limit.

Authors
  • He, Hua1
  • Tang, Wan2
  • Kelly, Tanika1
  • Li, Shengxu3
  • He, Jiang1
  • 1 Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA.
  • 2 Department of Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA.
  • 3 Children's Minnesota Research Institute, Children's Hospitals and Clinics of Minnesota Medicine, Minneapolis, MN, USA.
Type
Published Article
Journal
Statistical Methods in Medical Research
Publisher
SAGE Publications
Publication Date
Aug 01, 2020
Volume
29
Issue
8
Pages
2179–2197
Identifiers
DOI: 10.1177/0962280219885985
PMID: 31736411
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Measures of substance concentration in urine, serum or other biological matrices often have an assay limit of detection. When concentration levels fall below the limit, the exact measures cannot be obtained. Instead, the measures are censored as only partial information that the levels are under the limit is known. Assuming the concentration levels are from a single population with a normal distribution or follow a normal distribution after some transformation, Tobit regression models, or censored normal regression models, are the standard approach for analyzing such data. However, in practice, it is often the case that the data can exhibit more censored observations than what would be expected under the Tobit regression models. One common cause is the heterogeneity of the study population, caused by the existence of a latent group of subjects who lack the substance measured. For such subjects, the measurements will always be under the limit. If a censored normal regression model is appropriate for modeling the subjects with the substance, the whole population follows a mixture of a censored normal regression model and a degenerate distribution of the latent class. While there are some studies on such mixture models, a fundamental question about testing whether such mixture modeling is necessary, i.e. whether such a latent class exists, has not been studied yet. In this paper, three tests including Wald test, likelihood ratio test and score test are developed for testing the existence of such latent class. Simulation studies are conducted to evaluate the performance of the tests, and two real data examples are employed to illustrate the tests.

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