Journal article
Poor reporting of multivariable prediction model studies: towards a targeted implementation strategy of the TRIPOD statement
BMC medicine, Vol.16(1), pp.120-120
07/19/2018
Handle:
https://hdl.handle.net/2376/108286
PMCID: PMC6052616
PMID: 30021577
Abstract
As complete reporting is essential to judge the validity and applicability of multivariable prediction models, a guideline for the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) was introduced. We assessed the completeness of reporting of prediction model studies published just before the introduction of the TRIPOD statement, to refine and tailor its implementation strategy.
Within each of 37 clinical domains, 10 journals with the highest journal impact factor were selected. A PubMed search was performed to identify prediction model studies published before the launch of TRIPOD in these journals (May 2014). Eligible publications reported on the development or external validation of a multivariable prediction model (either diagnostic or prognostic) or on the incremental value of adding a predictor to an existing model.
We included 146 publications (84% prognostic), from which we assessed 170 models: 73 (43%) on model development, 43 (25%) on external validation, 33 (19%) on incremental value, and 21 (12%) on combined development and external validation of the same model. Overall, publications adhered to a median of 44% (25th-75th percentile 35-52%) of TRIPOD items, with 44% (35-53%) for prognostic and 41% (34-48%) for diagnostic models. TRIPOD items that were completely reported for less than 25% of the models concerned abstract (2%), title (5%), blinding of predictor assessment (6%), comparison of development and validation data (11%), model updating (14%), model performance (14%), model specification (17%), characteristics of participants (21%), model performance measures (methods) (21%), and model-building procedures (24%). Most often reported were TRIPOD items regarding overall interpretation (96%), source of data (95%), and risk groups (90%).
More than half of the items considered essential for transparent reporting were not fully addressed in publications of multivariable prediction model studies. Essential information for using a model in individual risk prediction, i.e. model specifications and model performance, was incomplete for more than 80% of the models. Items that require improved reporting are title, abstract, and model-building procedures, as they are crucial for identification and external validation of prediction models.
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Details
- Title
- Poor reporting of multivariable prediction model studies: towards a targeted implementation strategy of the TRIPOD statement
- Creators
- Pauline Heus - Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands. p.heus@umcutrecht.nlJohanna A A G Damen - Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The NetherlandsRomin Pajouheshnia - Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The NetherlandsRob J P M Scholten - Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The NetherlandsJohannes B Reitsma - Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The NetherlandsGary S Collins - Centre for Statistics in Medicine, NDORMS, Botnar Research Centre, University of Oxford, Oxford, UKDouglas G Altman - Centre for Statistics in Medicine, NDORMS, Botnar Research Centre, University of Oxford, Oxford, UKKarel G M Moons - Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The NetherlandsLotty Hooft - Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Publication Details
- BMC medicine, Vol.16(1), pp.120-120
- Publisher
- England
- Grant note
- ZONMW 918.10.615 and 91208004 / Netherlands Organization for Scientific Research
- Identifiers
- 99900546915501842
- Language
- English
- Resource Type
- Journal article