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Statistics in medicine
09 10, 2024;43 (20): 3943-3957.

Latent classification model for censored longitudinal binary outcome.

Jacky C Kuo, Wenyaw Chan, Luis Leon-Novelo, David R Lairson, Armand Brown, Kayo Fujimoto
Author Information
  1. Jacky C Kuo: Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston, Houston, Texas, USA.
  2. Wenyaw Chan: Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston, Houston, Texas, USA. ORCID
  3. Luis Leon-Novelo: Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston, Houston, Texas, USA.
  4. David R Lairson: Department of Management, Policy and Community Health, University of Texas Health Science Center at Houston, Houston, Texas, USA.
  5. Armand Brown: Bureau of Epidemiology, Houston Health Department, Houston, Texas, USA.
  6. Kayo Fujimoto: Department of Health Promotion and Behaviroal Sciences, University of Texas Health Science Center at Houston, Houston, Texas, USA.
PMID: 38951953 DOI: 10.1002/sim.10156

Abstract

Latent classification model is a class of statistical methods for identifying unobserved class membership among the study samples using some observed data. In this study, we proposed a latent classification model that takes a censored longitudinal binary outcome variable and uses its changing pattern over time to predict individuals' latent class membership. Assuming the time-dependent outcome variables follow a continuous-time Markov chain, the proposed method has two primary goals: (1) estimate the distribution of the latent classes and predict individuals' class membership, and (2) estimate the class-specific transition rates and rate ratios. To assess the model's performance, we conducted a simulation study and verified that our algorithm produces accurate model estimates (ie, small bias) with reasonable confidence intervals (ie, achieving approximately 95% coverage probability). Furthermore, we compared our model to four other existing latent class models and demonstrated that our approach yields higher prediction accuracies for latent classes. We applied our proposed method to analyze the COVID-19 data in Houston, Texas, US collected between January first 2021 and December 31st 2021. Early reports on the COVID-19 pandemic showed that the severity of a SARS-CoV-2 infection tends to vary greatly by cases. We found that while demographic characteristics explain some of the differences in individuals' experience with COVID-19, some unaccounted-for latent variables were associated with the disease.

Keywords

censoring continuous‐time Markov chain latent class analysis latent classification longitudinal binary data

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Grants

  1. 75D30121C10133/CDC HHS
  2. 75D30121C10133/CDC HHS

MeSH Term

Humans
COVID-19
Longitudinal Studies
Markov Chains
Latent Class Analysis
Algorithms
Computer Simulation
Models, Statistical
Texas
SARS-CoV-2
Female
Journal Article Research Support, U.S. Gov't, P.H.S.

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