Evidence map›Paper›PMID 20936883›Full record

ArticlePharmacoEconomics2010

Methods of model calibration: observations from a mathematical model of cervical cancer.

Douglas C A Taylor, Vivek Pawar, Denise Kruzikas, Kristen E Gilmore, Ankur Pandya, Rowan Iskandar, Milton C Weinstein

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Article in PharmacoEconomics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 2 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

19 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. A Scoping Review on Calibration Methods for Cancer Simulation Models.Medical decision making : an international journal of the Society for Medical Decision Making · 2025
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  5. Microsimulation Model Calibration with Approximate Bayesian Computation in R: A Tutorial.Medical decision making : an international journal of the Society for Medical Decision Making · 2022
    Article
  6. Article
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  9. 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
  10. Article
  11. Article
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  13. Article
  14. An Overview of R in Health Decision Sciences.Medical decision making : an international journal of the Society for Medical Decision Making · 2017
    Review
  15. Review
  16. 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
  17. Article
  18. Performance of a mathematical model to forecast lives saved from HIV treatment expansion in resource-limited settings.Medical decision making : an international journal of the Society for Medical Decision Making · 2015
    Article
  19. 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

7 authors.

Douglas C A Taylori3 Innovus, Medford, Massachusetts 02155, USA.
Vivek Pawar
Denise Kruzikas
Kristen E Gilmore
Ankur Pandya
Rowan Iskandar
Milton C Weinstein

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMathematical models are commonly used to predict future benefits of new therapies or interventions in the healthcare setting. The reliability of model results is greatly dependent on accuracy of model inputs but on occasion, data sources may not provide all the required inputs. Therefore, calibration of model inputs to epidemiological endpoints informed by existing data can be a useful tool to ensure credibility of the results.

objectiveTo compare different computational methods of calibrating a Markov model to US data.

methodsWe developed a Markov model that simulates the natural history of human papillomavirus (HPV) infection and subsequent cervical disease in the US. Because the model consists of numerous transition probabilities that cannot be directly estimated from data, calibration to multiple disease endpoints was required to ensure its predictive validity. Goodness of fit was measured as the mean percentage deviation of model-predicted endpoints from target estimates. During the calibration process we used the manual, random and Nelder-Mead calibration methods.

resultsThe Nelder-Mead and manual calibration methods achieved the best fit, with mean deviations of 7% and 10%, respectively. Nelder-Mead accomplished this result with substantially less analyst time than the manual method, but required more intensive computing capability. The random search method achieved a mean deviation of 39%, which we considered unacceptable despite the ease of implementation of that method.

conclusionsThe Nelder-Mead and manual techniques may be preferable calibration methods based on both performance and efficiency, provided that sufficient resources are available.

Indexed as

Models, BiologicalAdolescentAdultAgedAged, 80 and overAlgorithmsCalibrationComputer SimulationEconomics, PharmaceuticalFemaleHumansMarkov ChainsMiddle AgedNeoplasm StagingPapillomavirus InfectionsUnited States

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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.