Planned but ever published? A retrospective analysis of clinical prediction modelling studies registered on clinicaltrials.gov since 2000

Nicole White, Adrian Barnett, Rex Parsons, David N Borg and Gary Collins
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Adrian Barnett: Queensland University of Technology
David N Borg: Queensland University of Technology
Gary Collins: University of Oxford

No nh9sx, OSF Preprints from Center for Open Science

Abstract: Objectives: To describe the characteristics and publication outcomes of clinical prediction model studies registered on clinicaltrials.gov since 2000. Design: Retrospective descriptive analysis. Data sources: Information from clinicaltrials.gov records at the time of download and corresponding web histories, PubMed abstracts and metadata, based on searches by National Clinical Trials (NCT) number. Selection criteria: Observational studies registered on clinicaltrials.gov between 1 January 2000 and 2 March 2022 describing the development of a new clinical prediction model or the validation of an existing model for predicting the individual-level prognostic or diagnostic risk. Data extraction: Eligible clinicaltrials.gov records were classified by modelling study type (development, validation) and the model outcome being predicted (prognostic, diagnostic). Recorded characteristics included study status, sample size information, Medical Subject Headings (MeSH), and plans to share Individual Participant Data. Publications describing results from clinical prediction modelling were searched for each record after their reported study start date (last updated 17 November 2023). Results: 928 records were analysed from a possible 89,896 observational study records. Publications searches found 170 matching peer-reviewed publications for 137 clinicaltrials.gov records. The estimated proportion of records with one or more matching publications after accounting for time since study start was 2.8% at 2 years (95% confidence interval; CI: 1.7% to 3.9%), 12.3% at five years (9.8% to 14.9%) and 27.8% at ten years (22.6% to 33.0%). Stratifying records by study start year indicated that publication proportions improved over time. Records tended to prioritise the development of new prediction models over the validation of existing models (75.9%; 704/928 vs 24.1%; 182/928). At the time of download, 27.2% of records were marked as complete, 35.2% were still recruiting, and 14.7% had unknown status. Only 7.4% of records stated plans to share individual participant data. Conclusions: Published clinical prediction model studies represent a fraction of current and ongoing research efforts, with many studies planned but not completed or published. Improving the uptake of study preregistration and follow-up will increase the visibility of planned research. Introducing additional registry features and guidance may improve the identification of clinical prediction model studies posted to clinical registries.

Date: 2023-12-21
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:nh9sx

DOI: 10.31219/osf.io/nh9sx

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