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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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A Comparison of Methods for Modeling Multistate Cancer Progression Using Screening Data with Censoring after

Eddymurphy U Akwiwu1, Veerle M H Coupé1, Johannes Berkhof1

  • 1Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Epidemiology and Data Science, Amsterdam Public Health, Amsterdam, The Netherlands.

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|March 13, 2026
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Summary
This summary is machine-generated.

This study compared multistate cancer models for screening and surveillance data. The BayesTSM method demonstrated robust risk estimates, especially when cancer precursor progression hazards were time-dependent.

Keywords:
cancer screening and surveillanceinterval-censored datamultistate modelsnatural historysimulation studysurvival analysis

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Area of Science:

  • Biostatistics
  • Cancer Epidemiology
  • Health Services Research

Background:

  • Accurate cancer screening and surveillance rely on understanding sojourn times and cancer risk from premalignant lesions.
  • Multistate cancer models estimate these parameters but their performance is understudied when precursors are treated, preventing cancer progression (a censoring setting).
  • This research addresses the performance of multistate methods in this specific censoring scenario.

Purpose of the Study:

  • To evaluate the performance of various multistate cancer modeling methods in a setting where cancer precursors are treated upon detection.
  • To identify which methods provide unbiased risk estimates under different hazard assumptions (time-independent vs. time-dependent).

Main Methods:

  • Compared six R software package implementations of multistate models (msm, cthmm, smms, BayesTSM, hmm).
  • Assessed models with time-independent hazards and hazards dependent on time since state entry or process start.
  • Evaluated model performance through simulations and application to colorectal cancer surveillance data (healthy, non-advanced adenoma, advanced neoplasia states).

Main Results:

  • All methods performed well with time-independent hazards in simulations.
  • Only smms and BayesTSM yielded unbiased risk estimates when hazards were dependent on time since state entry.
  • In the colorectal cancer data application, only msm, hmm, and BayesTSM converged; BayesTSM and hmm showed similar non-advanced adenoma risk estimates, while advanced neoplasia risk estimates varied.

Conclusions:

  • The time dependency of hazards significantly impacts multistate cancer model performance, particularly for unobservable precursor-to-cancer transitions.
  • BayesTSM offers robust and unbiased risk estimates, especially in realistic scenarios with time-dependent hazards since state entry.
  • The choice of multistate modeling method is critical to avoid biased parameter estimates in cancer screening and surveillance analyses.