Evidence map›Paper›PMID 35615677›Full record

ArticleFrontiers in physiology2022

Characterization and Valuation of the Uncertainty of Calibrated Parameters in Microsimulation Decision Models.

Fernando Alarid-Escudero, Amy B Knudsen, Jonathan Ozik, Nicholson Collier, Karen M Kuntz

Abstract read
In one paragraph

Article in Frontiers in physiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–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

7 citing papers in PubMed.

  1. Article
  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. Microsimulation Estimates of Decision Uncertainty and Value of Information Are Biased but Consistent.Medical decision making : an international journal of the Society for Medical Decision Making · 2025
    Article
  5. Article
  6. Emulator-Based Bayesian Calibration of the CISNET Colorectal Cancer Models.Medical decision making : an international journal of the Society for Medical Decision Making · 2024
    Article
  7. Emulator-based Bayesian calibration of the CISNET colorectal cancer models.medRxiv : the preprint server for health sciences · 2024
    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

5 authors.

Fernando Alarid-EscuderoDivision of Public Administration, Center for Research and Teaching in Economics (CIDE), Aguascalientes, Mexico.
Amy B KnudsenInstitute for Technology Assessment, Massachusetts General Hospital, Boston, MA, United States.
Jonathan OzikDecision and Infrastructure Sciences Division, Argonne National Laboratory, Argonne, IL, United States.
Nicholson CollierDecision and Infrastructure Sciences Division, Argonne National Laboratory, Argonne, IL, United States.
Karen M KuntzDivision of Health Policy and Management, University of Minnesota School of Public Health, Minneapolis, MN, United States.

Funding

Comparative Modeling of Effective Policies for Colorectal Cancer ControlU01CA253913 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI RUTTER, CAROLYN M, ZAUBER, ANN GRAHAM · 2020 to 2025
$6.5M
Comparative Modeling of Colorectal Cancer: Informing Health Policies and Prioritizing Future ResearchU01CA199335 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI KUNTZ, KAREN M, LANSDORP-VOGELAAR, IRIS · 2015 to 2019
$5.5M
NCI NIH HHS U01 CA199335NCI NIH HHS U01 CA253913
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

Bayesiancalibrationdecision-analytic modelsEMEWShigh-performance computingmicrosimulation modelsuncertainty quantificationvalue of information analysis

Identifiers

PMID35615677
PMCPMC9124835

What OpenQuestion holds

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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.