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Related Experiment Videos

Probabilistic analysis and computationally expensive models: Necessary and required?

Susan Griffin1, Karl Claxton, Neil Hawkins

  • 1Center for Health Economics, University of York, York, UK. scg3@york.ac.uk

Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research
|August 15, 2006
PubMed
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Probabilistic sensitivity analysis (PSA) is underutilized in health technology appraisals using patient-level simulation (PLS). Models should balance estimating net benefits with characterizing decision uncertainty for better health economic evaluations.

Area of Science:

  • Health economics
  • Decision science
  • Mathematical modeling

Background:

  • National Institute for Health and Clinical Excellence (NICE) appraisals require estimates of decision uncertainty alongside expected net benefits (NB).
  • Computationally intensive models, such as patient-level simulation (PLS), can pose challenges in estimating this uncertainty.

Purpose of the Study:

  • To evaluate the necessity of decision uncertainty in NICE appraisals.
  • To assess the appropriateness of probabilistic sensitivity analysis (PSA) in conjunction with patient-level simulation (PLS).

Main Methods:

  • A review of NICE appraisals published up to January 2005 was conducted.
  • Appraisals utilizing PLS models to estimate NB were identified and analyzed.
  • A comparison was made between the use of PSA in PLS models versus cohort models.

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Main Results:

  • Only one out of six PLS models incorporated PSA, compared to 16 out of 24 cohort models.
  • Justification for omitting PSA was frequently absent.
  • While PLS has specific applications, alternative modeling approaches and advancements in computing power can facilitate PSA in complex models.

Conclusions:

  • Model developers must prioritize both estimating expected NB and characterizing decision uncertainty.
  • It is feasible to create models that satisfy decision-maker constraints while meeting these dual requirements.