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This review highlights challenges in oncology treatment sequence models, proposing a framework to improve effectiveness estimation for cancer therapies. Current models need enhancement in considering disease progression, treatment sequencing, and indirect comparisons for better patient outcomes.

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Area of Science:

  • Oncology
  • Health Economics and Outcomes Research
  • Biostatistics

Background:

  • Existing oncology treatment sequence decision models face challenges in accurately estimating treatment effectiveness.
  • Patients with cancer often receive multiple lines of therapy (LOTs) sequentially, necessitating robust models for decision-making.

Purpose of the Study:

  • To summarize challenges in current oncology treatment sequence decision models.
  • To introduce a general framework for conceptualizing and improving these models.
  • To explore key aspects for modeling the effectiveness of sequential cancer treatments.

Main Methods:

  • A systematic search of PubMed (20 models) and NICE technology appraisals (26 models) was conducted.
  • Models were assessed for their handling of four methodological aspects: outcome selection, efficacy adjustment for sequence placement, treatment-free interval (TFI) incorporation, and indirect treatment comparison (ITC) utilization.
  • The review analyzed how models define health states, estimate treatment duration, and incorporate data from multiple sources.

Main Results:

  • Most models defined health states by disease progression on different LOTs and estimated treatment duration separately.
  • Few models adjusted efficacy based on patient characteristics or prior therapies, and only six considered TFIs.
  • While 11 models used ITC, most limited its application to a single LOT, indicating limited use for sequence-level comparisons.

Conclusions:

  • There are significant opportunities to enhance the estimation of treatment sequence effectiveness using existing data.
  • Improved modeling frameworks are needed to better account for disease progression, treatment sequencing, TFIs, and ITCs.
  • More sophisticated approaches are required for accurate comparative effectiveness research in sequential oncology treatment decisions.