Probabilistic Analysis and Computationally Expensive Models: Necessary and Required?
Article first published online: 16 MAY 2006
Value in Health
Volume 9, Issue 4, pages 244–252, July/August 2006
How to Cite
Griffin, S., Claxton, K., Hawkins, N. and Sculpher, M. (2006), Probabilistic Analysis and Computationally Expensive Models: Necessary and Required?. Value in Health, 9: 244–252. doi: 10.1111/j.1524-4733.2006.00107.x
- Issue published online: 16 MAY 2006
- Article first published online: 16 MAY 2006
- cost-effectiveness analysis;
- decision uncertainty;
- decision-analytic modeling;
- patient-level simulation;
- probabilistic analysis
Objective: To assess the importance of considering decision uncertainty, the appropriateness of probabilistic sensitivity analysis (PSA), and the use of patient-level simulation (PLS) in appraisals for the National Institute for Health and Clinical Excellence (NICE).
Methods: Decision-makers require estimates of decision uncertainty alongside expected net benefits (NB) of interventions. This requirement may be difficult in computationally expensive models, for example, those employing PLS. NICE appraisals published up until January 2005 were reviewed to identify those where the assessment group utilized a PLS model structure to estimate NB. After identifying PLS models, all appraisals published in the same year were reviewed.
Results: Among models using PLS, one out of six conducted PSA, compared with 16 out of 24 cohort models. Justification for omitting PSA was absent in most cases. Reasons for choosing PLS included treatment switching, sampling patient characteristics and dependence on patient history. Alternative modeling approaches exist to handle these, including semi-Markov models and emulators that eliminate the need for two-level simulation. Stochastic treatment switching and sampling baseline characteristics do not inform adoption decisions. Modeling patient history does not necessitate PLS, and can depend on the software used. PLS addresses nonlinear relationships between patient variability and model outputs, but other options exist. Increased computing power, emulators or closed-form approximations can facilitate PSA in computationally expensive models.
Conclusions: In developing models analysts should consider the dual requirement of estimating expected NB and characterizing decision uncertainty. It is possible to develop models that meet these requirements within the constraints set by decision-makers.