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Estimating Transition Probabilities from Published Evidence: A Tutorial for Decision Modelers.

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Summary
This summary is machine-generated.

This tutorial provides practical guidance for decision modelers on converting published data into transition probabilities for state-transition models. It covers deriving probabilities from various statistics and adjusting them for different model cycle lengths and complexities.

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Area of Science:

  • Health economics
  • Decision modeling
  • Biostatistics

Background:

  • State-transition models are crucial for cost-effectiveness analysis.
  • Deriving accurate transition probabilities from published literature is challenging.
  • Existing guidance is fragmented across various publications.

Purpose of the Study:

  • To consolidate practical guidance for deriving transition probabilities from published data.
  • To serve as a stand-alone resource for decision modelers.
  • To focus on the technical aspects of data manipulation for model inputs.

Main Methods:

  • Explaining the derivation of transition probabilities from relative risks, odds, odds ratios, and rates.
  • Reviewing methods for converting probabilities to match model cycle length for two and three or more health-state transitions.
  • Discussing the use of transition probabilities for population subgroups and handling data from diverse sources.

Main Results:

  • Provides a systematic approach to deriving transition probabilities.
  • Offers methods for adjusting probabilities based on model structure and data type.
  • Addresses practical considerations such as subgroup analysis and data integration.

Conclusions:

  • This tutorial offers a comprehensive resource for decision modelers.
  • It enhances the accuracy and reliability of inputs for state-transition models.
  • Recommendations are provided for optimizing the use of published literature in model development.