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The Self-Evaluation Maintenance (SEM) model offers a psychological framework to understand how individuals’ self-esteem is influenced by the achievements of others, particularly those with whom they share close personal bonds. The SEM model operates when personal rather than social identity guides individuals. Central to this model is the notion that individuals have an inherent desire to preserve a favorable self-image, which is continuously shaped by interpersonal comparisons and...
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Fibrous joints are a type of joint where the bones are connected by fibrous connective tissue. These joints provide stability and minimal to no movement between the articulating bones. There are three types of fibrous joints.
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Drugs can be classified according to their chemical composition or their intended therapeutic application. For instance, anti-infective agents that possess the ability to eliminate pathogens or suppress their growth and reproduction can be grouped based on the organisms they target or their chemical structure. Furthermore, drugs can be divided into prescription, nonprescription, or controlled substances. Prescription medications, such as antibiotics, require oversight from a licensed healthcare...
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A joint latent-class Bayesian model with application to ALL maintenance studies.

Damitri Kundu1,2, Sevantee Basu1, Manash Pratim Gogoi3

  • 1Statistical Science Division, Indian Statistical Institute, Kolkata, India.

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|February 6, 2026
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This study introduces a novel Bayesian model to analyze childhood Acute Lymphocytic Leukemia (ALL) treatment data. The model identifies distinct patient groups, revealing significant differences in non-relapse probabilities for effective pediatric cancer research.

Keywords:
Acute lymphocytic leukemia (ALL)MCMCjoint modelinglatent class modellongitudinal outcomes

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Area of Science:

  • Biostatistics
  • Pediatric Oncology
  • Computational Biology

Background:

  • Acute Lymphocytic Leukemia (ALL) remains a leading cause of childhood cancer mortality globally.
  • Survival rates for ALL have improved in developed nations but lag in developing countries.
  • Clinical trials require advanced statistical models to analyze complex longitudinal biomarker data.

Purpose of the Study:

  • To develop and validate a joint latent-class Bayesian model for analyzing pediatric ALL patient data.
  • To investigate the relationship between longitudinal biomarkers and treatment outcomes in ALL.
  • To identify distinct patient subgroups based on lymphocyte count trajectories.

Main Methods:

  • A joint latent-class Bayesian model integrating longitudinal biomarkers (lymphocyte, neutrophil, platelet counts) and time-to-event data.
  • Latent-class modeling for lymphocyte count, linear mixed models for other biomarkers.
  • Semi-parametric proportional hazards model for time-to-event, linked via shared Gaussian random effects.

Main Results:

  • The proposed model identified two distinct latent classes for lymphocyte count in pediatric ALL patients.
  • Significant differences in class-specific non-relapse probabilities were observed over the study period.
  • The joint model demonstrated superior accuracy and practical utility compared to traditional methods via simulation.

Conclusions:

  • The joint latent-class Bayesian model provides a powerful tool for analyzing complex ALL data.
  • Identifying distinct patient subgroups can lead to personalized treatment strategies and improved outcomes.
  • This approach offers valuable insights for pediatric cancer research, particularly in resource-limited settings.