title: Incorporating calibrated model parameters into sensitivity analyses: deterministic and probabilistic approaches.
abstract: OBJECTIVE
  The aim of this study was to examine how calibration uncertainty affects the overall uncertainty of a mathematical model and to evaluate potential drivers of calibration uncertainty.
  
  
  METHODS
  A lifetime Markov model of the natural history of human papillomavirus (HPV) infection and cervical disease was developed to assess the cost effectiveness of a hypothetical HPV vaccine. Published data on cervical cancer incidence and mortality and prevalence of pre-cursor lesions were used as endpoints to calibrate the age- and HPV-type-specific transition probabilities between health states using the Nelder-Mead simplex method of calibration. A conventional probabilistic sensitivity analysis (PSA) was performed to assess uncertainty in vaccine efficacy, cost and utility estimates. To quantify the uncertainty around calibrated transition probabilities, a second PSA (calibration PSA) was performed using 25 distinct combinations of objective functions and starting simplexes.
  
  
  RESULTS
  The initial calibration produced an incremental cost-effectiveness ratio (ICER) of $US 4300 per QALY for vaccination compared with no vaccination, and the conventional PSA gave a 95% credible interval of dominant to $US 9800 around this estimate (2005 values). The 95% credible interval for the ICERs in the calibration PSA ranged from $US 1000 to $US 37,700.
  
  
  CONCLUSIONS
  Compared with a conventional PSA, the calibration PSA results reveal a greater level of uncertainty in cost-effectiveness results. Sensitivity analyses around model calibration should be performed to account for uncertainty arising from the calibration process.
authors.affiliation: OptumInsight, Medford, MA02155, USA. doug.taylor@optum.com
authors.forename: Douglas C A
authors.initials: DC
authors.lastname: Taylor
authors.forename: Vivek
authors.initials: V
authors.lastname: Pawar
authors.forename: Denise T
authors.initials: DT
authors.lastname: Kruzikas
authors.forename: Kristen E
authors.initials: KE
authors.lastname: Gilmore
authors.forename: Myrlene
authors.initials: M
authors.lastname: Sanon
authors.forename: Milton C
authors.initials: MC
authors.lastname: Weinstein
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D002138:Calibration; D003198:Computer Simulation; D003362:Cost-Benefit Analysis / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D015994:Incidence; D016013:Likelihood Functions; D008390:Markov Chains; D008875:Middle Aged; D008954:Models, Biological; D018803:Models, Economic; D060787:Neoplasm Grading; D030361:Papillomavirus Infections / Q000150:complications* / Q000517:prevention & control*; D053918:Papillomavirus Vaccines / Q000191:economics* / Q000627:therapeutic use*; D015995:Prevalence; D019057:Quality-Adjusted Life Years; D035501:Uncertainty; D002583:Uterine Cervical Neoplasms / Q000191:economics* / Q000453:epidemiology / Q000209:etiology / Q000517:prevention & control*; D055815:Young Adult; D002578:Uterine Cervical Dysplasia / Q000191:economics / Q000453:epidemiology / Q000209:etiology / Q000517:prevention & control
chemical_list: D053918:Papillomavirus Vaccines