Evidence map›Paper›PMID 30815191›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2018

Evaluating the Impact of Uncertainty on Risk Prediction: Towards More Robust Prediction Models.

Panayiotis Petousis, Arash Naeim, Ali Mosleh, William Hsu

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Using Sequential Decision Making to Improve Lung Cancer Screening Performance.IEEE access : practical innovations, open solutions · 2019
    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

4 authors.

Panayiotis PetousisMedical Imaging & Informatics, Department of Radiological Sciences and Bioengineering.
Arash NaeimDepartment of Medicine, David Geffen School of Medicine.
Ali MoslehB. John Garrick Institute for the Risk Sciences, Samueli School of Engineering University of California, Los Angeles, CA.
William HsuMedical Imaging & Informatics, Department of Radiological Sciences and Bioengineering.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Risk prediction models are crucial for assessing the pretest probability of cancer and are applied to stratify patient management strategies. These models are frequently based on multivariate regression analysis, requiring that all risk factors be specified, and do not convey the confidence in their predictions. We present a framework for uncertainty analysis that accounts for variability in input values. Uncertain or missing values are replaced with a range of plausible values. These ranges are used to compute individualized risk confidence intervals. We demonstrate our approach using the Gail model to evaluate the impact of uncertainty on management decisions. Up to 13% of cases (uncertain) had a risk interval that falls within the decision threshold (e.g., 1.67% 5-year absolute risk). A small number of cases changed from low- to high-risk when missing values were present. Our analysis underscores the need for better communication of input assumptions that influence the resulting predictions.

Indexed as

AlgorithmsModels, TheoreticalUncertaintyBreast NeoplasmsDecision MakingEarly Detection of CancerFemaleHumansRisk AssessmentRisk Factors

Identifiers

PMID30815191
PMCPMC6371325

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.