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dc.contributor.authorThuraisingam, S
dc.contributor.authorDowsey, M
dc.contributor.authorManski-Nankervis, J-A
dc.contributor.authorSpelman, T
dc.contributor.authorChoong, P
dc.contributor.authorGunn, J
dc.contributor.authorChondros, P
dc.date.accessioned2021-01-26T03:18:32Z
dc.date.available2021-01-26T03:18:32Z
dc.date.issued2020-12
dc.identifier.citationThuraisingam, S., Dowsey, M., Manski-Nankervis, J. -A., Spelman, T., Choong, P., Gunn, J. & Chondros, P. (2020). Developing prediction models for total knee replacement surgery in patients with osteoarthritis: Statistical analysis plan. Osteoarthritis and Cartilage Open, 2 (4), pp.100126-100126. https://doi.org/10.1016/j.ocarto.2020.100126.
dc.identifier.issn2665-9131
dc.identifier.urihttp://hdl.handle.net/11343/258820
dc.description.abstractBackground Approximately 12–20% of those with osteoarthritis (OA) in Australia who undergo total knee replacement (TKR) surgery do not report any clinical improvement. There is a need to develop prediction tools for use in general practice that allow early identification of patients likely to undergo TKR and those unlikely to benefit from the surgery. First-line treatment strategies can then be implemented and optimised to delay or prevent the need for TKR. The identification of potential non-responders to TKR may provide the opportunity for new treatment strategies to be developed and help ensure surgery is reserved for those most likely to benefit. This statistical analysis plan (SAP) details the statistical methodology used to develop such prediction tools. Objective To describe in detail the statistical methods used to develop and validate prediction models for TKR surgery in Australian patients with OA for use in general practice. Methods This SAP contains a brief justification for the need for prediction models for TKR surgery in general practice. A description of the data sources that will be linked and used to develop the models, and estimated sample sizes is provided. The planned methodologies for candidate predictor selection, model development, measuring model performance and internal model validation are described in detail. Intended table layouts for presentation of model results are provided. Conclusion Consistent with best practice guidelines, the statistical methodologies outlined in this SAP have been pre-specified prior to data pre-processing and model development.
dc.languageen
dc.publisherElsevier BV
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0
dc.titleDeveloping prediction models for total knee replacement surgery in patients with osteoarthritis: Statistical analysis plan
dc.typeJournal Article
dc.identifier.doi10.1016/j.ocarto.2020.100126
melbourne.affiliation.departmentGeneral Practice
melbourne.affiliation.departmentSurgery (St Vincent's)
melbourne.source.titleOsteoarthritis and Cartilage Open
melbourne.source.volume2
melbourne.source.issue4
melbourne.source.pages100126-100126
melbourne.identifier.nhmrc1116325
melbourne.identifier.nhmrc1122526
melbourne.identifier.nhmrc1154203
dc.rights.licenseCC BY-NC-ND
melbourne.elementsid1481133
melbourne.contributor.authorManski-Nankervis, Jo-Anne
melbourne.contributor.authorChondros, Panagiota
melbourne.contributor.authorDowsey, Michelle
melbourne.contributor.authorGunn, Jane
melbourne.contributor.authorChoong, Peter
melbourne.contributor.authorSpelman, Timothy
melbourne.contributor.authorThuraisingam, Sharmala
melbourne.identifier.fundernameidNHMRC, 1116325
melbourne.identifier.fundernameidNHMRC, 1122526
melbourne.identifier.fundernameidNHMRC, 1154203
melbourne.identifier.fundernameidROYAL AUST COLLEGE OF GENERAL PRACTITIONERS
melbourne.accessrightsOpen Access


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