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Developing prediction models for total knee replacement surgery in patients with osteoarthritis: Statistical analysis plan

BACKGROUND: 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 under...

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Autores principales: Thuraisingam, Sharmala, Dowsey, Michelle, Manski-Nankervis, Jo-Anne, Spelman, Tim, Choong, Peter, Gunn, Jane, Chondros, Patty
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9718256/
https://www.ncbi.nlm.nih.gov/pubmed/36474876
http://dx.doi.org/10.1016/j.ocarto.2020.100126
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author Thuraisingam, Sharmala
Dowsey, Michelle
Manski-Nankervis, Jo-Anne
Spelman, Tim
Choong, Peter
Gunn, Jane
Chondros, Patty
author_facet Thuraisingam, Sharmala
Dowsey, Michelle
Manski-Nankervis, Jo-Anne
Spelman, Tim
Choong, Peter
Gunn, Jane
Chondros, Patty
author_sort Thuraisingam, Sharmala
collection PubMed
description BACKGROUND: 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.
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spelling pubmed-97182562022-12-05 Developing prediction models for total knee replacement surgery in patients with osteoarthritis: Statistical analysis plan Thuraisingam, Sharmala Dowsey, Michelle Manski-Nankervis, Jo-Anne Spelman, Tim Choong, Peter Gunn, Jane Chondros, Patty Osteoarthr Cartil Open ORIGINAL PAPER BACKGROUND: 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. Elsevier 2020-11-24 /pmc/articles/PMC9718256/ /pubmed/36474876 http://dx.doi.org/10.1016/j.ocarto.2020.100126 Text en © 2020 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle ORIGINAL PAPER
Thuraisingam, Sharmala
Dowsey, Michelle
Manski-Nankervis, Jo-Anne
Spelman, Tim
Choong, Peter
Gunn, Jane
Chondros, Patty
Developing prediction models for total knee replacement surgery in patients with osteoarthritis: Statistical analysis plan
title Developing prediction models for total knee replacement surgery in patients with osteoarthritis: Statistical analysis plan
title_full Developing prediction models for total knee replacement surgery in patients with osteoarthritis: Statistical analysis plan
title_fullStr Developing prediction models for total knee replacement surgery in patients with osteoarthritis: Statistical analysis plan
title_full_unstemmed Developing prediction models for total knee replacement surgery in patients with osteoarthritis: Statistical analysis plan
title_short Developing prediction models for total knee replacement surgery in patients with osteoarthritis: Statistical analysis plan
title_sort developing prediction models for total knee replacement surgery in patients with osteoarthritis: statistical analysis plan
topic ORIGINAL PAPER
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9718256/
https://www.ncbi.nlm.nih.gov/pubmed/36474876
http://dx.doi.org/10.1016/j.ocarto.2020.100126
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