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Longitudinal latent overall toxicity (LOTox) profiles in osteosarcoma: a new taxonomy based on latent Markov models

Author

Listed:
  • Marta Spreafico

    (Leiden University
    Leiden University Medical Center)

  • Francesca Ieva

    (Politecnico di Milano
    Human Technopole)

  • Marta Fiocco

    (Leiden University
    Leiden University Medical Center
    Princess Máxima Center for Pediatric Oncology)

Abstract

Due to the presence of multiple types of adverse events (AEs) with different levels of severity, the analysis of longitudinal toxicity data is a difficult task in cancer research. The current literature primarily relies on descriptive-based methods and lacks models that can effectively quantify the overall toxic burden experienced by patients over treatment without losing details of the impact of each AE. In this work, a novel taxonomy based on latent Markov models and compositional data techniques is proposed to model the Latent Overall Toxicity (LOTox) condition of each patient over cycles of treatment. Starting from observed categories of severity of multiple toxicities, the goal is to delineate distinct LOTox conditions and retrieve patients’ probabilities of being in a specific condition at a given cycle, as well as their risk of experiencing “worse" overall toxicity statuses compared to a reference “good" toxic condition. The proposed approach is applied to longitudinal toxicity data from the MRC BO06/EORTC 80931 randomized controlled trial for patients with osteosarcoma. The population of interest includes 377 patients who had successfully completed the six-cycle treatment. Personal characteristics and observed information on six toxicities are used to infer the unobserved LOTox status over the six cycles of chemotherapy. Provided that longitudinal toxicity data are available, the developed procedure is a flexible approach that can be adapted and applied to other cancer studies.

Suggested Citation

  • Marta Spreafico & Francesca Ieva & Marta Fiocco, 2024. "Longitudinal latent overall toxicity (LOTox) profiles in osteosarcoma: a new taxonomy based on latent Markov models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(5), pages 1451-1482, November.
    • Handle: RePEc:spr:stmapp:v:33:y:2024:i:5:d:10.1007_s10260-024-00767-9
    DOI: 10.1007/s10260-024-00767-9
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    References listed on IDEAS

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    1. Bekele, B. Nebiyou & Thall, Peter F., 2004. "Dose-Finding Based on Multiple Toxicities in a Soft Tissue Sarcoma Trial," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 26-35, January.
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    3. F. Bartolucci & A. Farcomeni & F. Pennoni, 2014. "Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 433-465, September.
    4. S. Bacci & S. Pandolfi & F. Pennoni, 2014. "A comparison of some criteria for states selection in the latent Markov model for longitudinal data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(2), pages 125-145, June.
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