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Area of Science:

  • Biostatistics
  • Clinical Research Methodology
  • Statistical Modeling

Background:

  • Disease progression involves complex outcomes like biomarker changes and event occurrence.
  • Heterogeneity in patient populations complicates the analysis of these outcomes.
  • Existing statistical models may not fully capture the interplay between longitudinal measures and time-to-event data.

Purpose of the Study:

  • To provide a practical tutorial on the joint latent class model for clinicians and statisticians.
  • To demonstrate the application of the joint latent class model in R software for clinical questions.
  • To facilitate the interpretation of results from complex statistical models in disease progression studies.

Main Methods:

  • Utilizing a joint latent class model that integrates linear mixed models for longitudinal data and survival models for time-to-event data.
  • Connecting these models through a latent class framework to account for unobserved population heterogeneity.
  • Implementing the model in R software with practical examples and code.

Main Results:

  • The tutorial details model specification in R for concrete clinical questions.
  • It includes exploration, manipulation, and interpretation of model results.
  • A real clinical dataset is used to illustrate the model's application and interpretation.

Conclusions:

  • The joint latent class model offers a robust approach to analyzing multiple disease progression outcomes.
  • This tutorial simplifies the application and interpretation of this complex model for practical clinical research.
  • The R implementation enables researchers to address specific clinical questions using this advanced statistical technique.