Evidence map›Paper›PMID 22149631›Full record

ArticlePharmacoEconomics2012

Incorporating calibrated model parameters into sensitivity analyses: deterministic and probabilistic approaches.

Douglas C A Taylor, Vivek Pawar, Denise T Kruzikas, Kristen E Gilmore, Myrlene Sanon, Milton C Weinstein

Abstract read
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In one paragraph

Article in PharmacoEconomics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. A Scoping Review on Calibration Methods for Cancer Simulation Models.Medical decision making : an international journal of the Society for Medical Decision Making · 2025
    Article
  3. Article
  4. Development and Calibration of a Dynamic HIV Transmission Model for 6 US Cities.Medical decision making : an international journal of the Society for Medical Decision Making · 2020
    Article
  5. Evaluating Parameter Uncertainty in a Simulation Model of Cancer Using Emulators.Medical decision making : an international journal of the Society for Medical Decision Making · 2019
    Article
  6. Nonidentifiability in Model Calibration and Implications for Medical Decision Making.Medical decision making : an international journal of the Society for Medical Decision Making · 2018
    Article
  7. An Overview of R in Health Decision Sciences.Medical decision making : an international journal of the Society for Medical Decision Making · 2017
    Review
  8. Some Health States Are Better Than Others: Using Health State Rank Order to Improve Probabilistic Analyses.Medical decision making : an international journal of the Society for Medical Decision Making · 2016
    Article
  9. Using Active Learning for Speeding up Calibration in Simulation Models.Medical decision making : an international journal of the Society for Medical Decision Making · 2016
    Article
  10. Calibration of complex models through Bayesian evidence synthesis: a demonstration and tutorial.Medical decision making : an international journal of the Society for Medical Decision Making · 2015
    Article
  11. Identifying best-fitting inputs in health-economic model calibration: a Pareto frontier approach.Medical decision making : an international journal of the Society for Medical Decision Making · 2015
    Article
  12. Modeling and calibration for exposure to time-varying, modifiable risk factors: the example of smoking behavior in India.Medical decision making : an international journal of the Society for Medical Decision Making · 2015
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Douglas C A TaylorOptumInsight, Medford, MA02155, USA. doug.taylor@optum.com
Vivek Pawar
Denise T Kruzikas
Kristen E Gilmore
Myrlene Sanon
Milton C Weinstein

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe 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.

methodsA 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.

resultsThe 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.

conclusionsCompared 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.

Indexed as

Models, EconomicAdolescentAdultAgedAged, 80 and overAge FactorsCalibrationComputer SimulationCost-Benefit AnalysisFemaleHumansIncidenceLikelihood FunctionsMarkov ChainsMiddle AgedModels, BiologicalPapillomavirus Vaccines

Identifiers

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.