A Computationally Efficient Method for Probabilistic Parameter Threshold Analysis for Health Economic Evaluations. (July 2020)
- Record Type:
- Journal Article
- Title:
- A Computationally Efficient Method for Probabilistic Parameter Threshold Analysis for Health Economic Evaluations. (July 2020)
- Main Title:
- A Computationally Efficient Method for Probabilistic Parameter Threshold Analysis for Health Economic Evaluations
- Authors:
- Pieters, Zoë
Strong, Mark
Pitzer, Virginia E.
Beutels, Philippe
Bilcke, Joke - Abstract:
- Background . Threshold analysis is used to determine the threshold value of an input parameter at which a health care strategy becomes cost-effective. Typically, it is performed in a deterministic manner, in which inputs are varied one at a time while the remaining inputs are each fixed at their mean value. This approach will result in incorrect threshold values if the cost-effectiveness model is nonlinear or if inputs are correlated.Objective . To propose a probabilistic method for performing threshold analysis, which accounts for the joint uncertainty in all input parameters and makes no assumption about the linearity of the cost-effectiveness model.Methods . Three methods are compared: 1) deterministic threshold analysis (DTA); 2) a 2-level Monte Carlo approach, which is considered the gold standard; and 3) a regression-based method using a generalized additive model (GAM), which identifies threshold values directly from a probabilistic sensitivity analysis sample.Results . We applied the 3 methods to estimate the minimum probability of hospitalization for typhoid fever at which 3 different vaccination strategies become cost-effective in Uganda. The threshold probability of hospitalization at which routine vaccination at 9 months with catchup campaign to 5 years becomes cost-effective is estimated to be 0.060 and 0.061 (95% confidence interval [CI], 0.058–0.064), respectively, for 2-level and GAM. According to DTA, routine vaccination at 9 months with catchup campaign toBackground . Threshold analysis is used to determine the threshold value of an input parameter at which a health care strategy becomes cost-effective. Typically, it is performed in a deterministic manner, in which inputs are varied one at a time while the remaining inputs are each fixed at their mean value. This approach will result in incorrect threshold values if the cost-effectiveness model is nonlinear or if inputs are correlated.Objective . To propose a probabilistic method for performing threshold analysis, which accounts for the joint uncertainty in all input parameters and makes no assumption about the linearity of the cost-effectiveness model.Methods . Three methods are compared: 1) deterministic threshold analysis (DTA); 2) a 2-level Monte Carlo approach, which is considered the gold standard; and 3) a regression-based method using a generalized additive model (GAM), which identifies threshold values directly from a probabilistic sensitivity analysis sample.Results . We applied the 3 methods to estimate the minimum probability of hospitalization for typhoid fever at which 3 different vaccination strategies become cost-effective in Uganda. The threshold probability of hospitalization at which routine vaccination at 9 months with catchup campaign to 5 years becomes cost-effective is estimated to be 0.060 and 0.061 (95% confidence interval [CI], 0.058–0.064), respectively, for 2-level and GAM. According to DTA, routine vaccination at 9 months with catchup campaign to 5 years would never become cost-effective. The threshold probability at which routine vaccination at 9 months with catchup campaign to 15 years becomes cost-effective is estimated to be 0.092 (DTA), 0.074 (2-level), and 0.072 (95% CI, 0.069–0.075) (GAM). GAM is 430 times faster than the 2-level approach.Conclusions . When the cost-effectiveness model is nonlinear, GAM provides similar threshold values to the 2-level Monte Carlo approach and is computationally more efficient. DTA provides incorrect results and should not be used. … (more)
- Is Part Of:
- Medical decision making. Volume 40:Number 5(2020)
- Journal:
- Medical decision making
- Issue:
- Volume 40:Number 5(2020)
- Issue Display:
- Volume 40, Issue 5 (2020)
- Year:
- 2020
- Volume:
- 40
- Issue:
- 5
- Issue Sort Value:
- 2020-0040-0005-0000
- Page Start:
- 669
- Page End:
- 679
- Publication Date:
- 2020-07
- Subjects:
- deterministic sensitivity analysis -- Monte Carlo approach -- probabilistic sensitivity analysis -- probabilistic threshold analysis
Medical policy -- Periodicals
Clinical medicine -- Decision making -- Periodicals
Medicine -- Periodicals
Médecine clinique -- Prise de décision -- Périodiques
362.1 - Journal URLs:
- http://journals.sagepub.com/home/mdm ↗
http://www.ingenta.com/journals/browse/sage/j501 ↗
http://www.sagepublications.com/ ↗
http://firstsearch.oclc.org ↗
http://firstsearch.oclc.org/journal=0272-989x;screen=info;ECOIP ↗ - DOI:
- 10.1177/0272989X20937253 ↗
- Languages:
- English
- ISSNs:
- 0272-989X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 13532.xml