Clinical prediction models aim to predict outcomes in individuals, to inform diagnosis or prognosis in healthcare. Hundreds of prediction models are published in the medical literature each year, yet many are developed using a dataset that is too small for the total number of participants or outcome events. This leads to inaccurate predictions and consequently incorrect healthcare decisions for some individuals. In this article, the authors provide guidance on how to calculate the sample size required to develop a clinical prediction model.
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abstract = "Clinical prediction models aim to predict outcomes in individuals, to inform diagnosis or prognosis in healthcare. Hundreds of prediction models are published in the medical literature each year, yet many are developed using a dataset that is too small for the total number of participants or outcome events. This leads to inaccurate predictions and consequently incorrect healthcare decisions for some individuals. In this article, the authors provide guidance on how to calculate the sample size required to develop a clinical prediction model.",
author = "Riley,
note = "Funding Information: Contributors: RDR and MVS led the methodology that underpins the methods in this article, with contributions from all authors. RDR wrote the first and updated drafts of the article, with important contributions and revisions from all authors at multiple stages. JE led the development of the pmsampsize packages in Stata and R. RDR is the guarantor. Funding: KGMM receives funding from the Netherlands Organisation for Scientific Research (project 9120.8004 and 918.10.615). For his work on this paper, FEH was supported by CTSA award No UL1 TR002243 from the National Center for Advancing Translational Sciences. Its contents are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences or the US National Institutes of Health. GC is supported by the National Institute for Health Research (NIHR) Biomedical Research Centre, Oxford. KIES is funded by the NIHR School for Primary Care Research. This (publication/paper/report) presents independent research funded by the NIHR. The views expressed are those of the authors and not necessarily those of the National Health Service, NIHR, or Department of Health and Social Care. Competing interests: We have read and understood the BMJ Group policy on declaration of interests and declare we have no competing interests. Provenance and peer review: Not commissioned; peer reviewed. Patient and public involvement: Patients or the public were not involved in the design, conduct, reporting, or dissemination of our research. Dissemination to participants and related patient and public communities: We plan to disseminate the sample size calculations to our Patient and Public Involvement and Engagement team when they are applied in new research projects. The lead author (RDR) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.",
year = "2020", month = mar, doi = "10.1136/bmj.m441", language = "English", volume = "368", pages = "m441", journal = "Bmj", issn = "0959-535X", publisher = "BMJ ",Research output : Contribution to journal › Article › peer-review
T1 - Calculating the sample size required for developing a clinical prediction model
AU - Riley, Richard D
AU - Snell, Kym I E
AU - Harrell, Frank E
AU - Martin, Glen P
AU - Reitsma, Johannes B
AU - Moons, Karel G M
AU - Collins, Gary
AU - Van Smeden, Maarten
N1 - Funding Information: Contributors: RDR and MVS led the methodology that underpins the methods in this article, with contributions from all authors. RDR wrote the first and updated drafts of the article, with important contributions and revisions from all authors at multiple stages. JE led the development of the pmsampsize packages in Stata and R. RDR is the guarantor. Funding: KGMM receives funding from the Netherlands Organisation for Scientific Research (project 9120.8004 and 918.10.615). For his work on this paper, FEH was supported by CTSA award No UL1 TR002243 from the National Center for Advancing Translational Sciences. Its contents are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences or the US National Institutes of Health. GC is supported by the National Institute for Health Research (NIHR) Biomedical Research Centre, Oxford. KIES is funded by the NIHR School for Primary Care Research. This (publication/paper/report) presents independent research funded by the NIHR. The views expressed are those of the authors and not necessarily those of the National Health Service, NIHR, or Department of Health and Social Care. Competing interests: We have read and understood the BMJ Group policy on declaration of interests and declare we have no competing interests. Provenance and peer review: Not commissioned; peer reviewed. Patient and public involvement: Patients or the public were not involved in the design, conduct, reporting, or dissemination of our research. Dissemination to participants and related patient and public communities: We plan to disseminate the sample size calculations to our Patient and Public Involvement and Engagement team when they are applied in new research projects. The lead author (RDR) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.
N2 - Clinical prediction models aim to predict outcomes in individuals, to inform diagnosis or prognosis in healthcare. Hundreds of prediction models are published in the medical literature each year, yet many are developed using a dataset that is too small for the total number of participants or outcome events. This leads to inaccurate predictions and consequently incorrect healthcare decisions for some individuals. In this article, the authors provide guidance on how to calculate the sample size required to develop a clinical prediction model.
AB - Clinical prediction models aim to predict outcomes in individuals, to inform diagnosis or prognosis in healthcare. Hundreds of prediction models are published in the medical literature each year, yet many are developed using a dataset that is too small for the total number of participants or outcome events. This leads to inaccurate predictions and consequently incorrect healthcare decisions for some individuals. In this article, the authors provide guidance on how to calculate the sample size required to develop a clinical prediction model.