Evidence map›Paper›PMID 35509061›Full record

ArticleDiagnostic and prognostic research2022

Does poor methodological quality of prediction modeling studies translate to poor model performance? An illustration in traumatic brain injury.

Isabel R A Retel Helmrich, Ana Mikolić, David M Kent, Hester F Lingsma, Laure Wynants, Ewout W Steyerberg, David van Klaveren

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diagnostic and prognostic research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 2 pooled it
2.8field-weighted citation impact, top 9% of its field
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

8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 16 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Observational
  5. Article
  6. Review
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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 at 3 institutions in 2 countries.

Isabel R A Retel HelmrichDepartment of Public Health, Center for Medical Decision Making, Erasmus MC-University Medical Center, Rotterdam, the Netherlands. i.retelhelmrich@erasmusmc.nl.
Ana MikolićDepartment of Public Health, Center for Medical Decision Making, Erasmus MC-University Medical Center, Rotterdam, the Netherlands.
David M KentPredictive Analytics and Comparative Effectiveness Center, Institute for Clinical Research and Health Policy Studies/Tufts Medical Center, Boston, USA.
Hester F LingsmaDepartment of Public Health, Center for Medical Decision Making, Erasmus MC-University Medical Center, Rotterdam, the Netherlands.
Laure WynantsDepartment of Epidemiology, School for Public Health and Primary Care, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands.
Ewout W SteyerbergDepartment of Public Health, Center for Medical Decision Making, Erasmus MC-University Medical Center, Rotterdam, the Netherlands.
David van KlaverenDepartment of Public Health, Center for Medical Decision Making, Erasmus MC-University Medical Center, Rotterdam, the Netherlands.
Erasmus MC · NLMaastricht University · NLTufts Medical Center · US

Funding

FP7 Health 602150
6 · The paper itself

Abstract

backgroundPrediction modeling studies often have methodological limitations, which may compromise model performance in new patients and settings. We aimed to examine the relation between methodological quality of model development studies and their performance at external validation.

methodsWe systematically searched for externally validated multivariable prediction models that predict functional outcome following moderate or severe traumatic brain injury. Risk of bias and applicability of development studies was assessed with the Prediction model Risk Of Bias Assessment Tool (PROBAST). Each model was rated for its presentation with sufficient detail to be used in practice. Model performance was described in terms of discrimination (AUC), and calibration. Delta AUC (dAUC) was calculated to quantify the percentage change in discrimination between development and validation for all models. Generalized estimation equations (GEE) were used to examine the relation between methodological quality and dAUC while controlling for clustering.

resultsWe included 54 publications, presenting ten development studies of 18 prediction models, and 52 external validation studies, including 245 unique validations. Two development studies (four models) were found to have low risk of bias (RoB). The other eight publications (14 models) showed high or unclear RoB. The median dAUC was positive in low RoB models (dAUC 8%, [IQR - 4% to 21%]) and negative in high RoB models (dAUC - 18%, [IQR - 43% to 2%]). The GEE showed a larger average negative change in discrimination for high RoB models (- 32% (95% CI: - 48 to - 15) and unclear RoB models (- 13% (95% CI: - 16 to - 10)) compared to that seen in low RoB models.

conclusionLower methodological quality at model development associates with poorer model performance at external validation. Our findings emphasize the importance of adherence to methodological principles and reporting guidelines in prediction modeling studies.

Indexed as

PROBASTPrognostic model studiesTraumatic brain injury

Identifiers

PMID35509061
PMCPMC9068255
OpenAlexW4229064320

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.