Developing prediction models for total knee replacement surgery in patients with osteoarthritis: Statistical analysis plan.
ABS, Australian Bureau of Statistics
AIHW, Australian Institute of Health and Welfare
AOANJRR, Australian Orthopaedic Association National Joint Replacement Registry
ATC, Anatomical Therapeutic Chemical
BMI, Body Mass Index
CPT, clinical prediction tool
Clinical prediction tools
DQA, data quality assessment
EMR, electronic medical record
Electronic health record
Electronic medical record
GP, General Practitioner
General practice
KOS-ADLS, Knee Outcome Survey-Activities of Daily Living Subscale
Knee replacement
NDI, National Death Index
NPS, National Prescribing Service
OA, osteoarthritis
OARSI, Osteoarthritis Research Society International
OMERACT, Outcome Measures in Rheumatology
Prediction models
Primary care
SAP, statistical analysis plan
SF-12, 12-Item Short Form Survey
SF-36, 36-Item Short Form Health Survey
Statistical analysis plan
TKR, total knee replacement
Journal
Osteoarthritis and cartilage open
ISSN: 2665-9131
Titre abrégé: Osteoarthr Cartil Open
Pays: England
ID NLM: 101767068
Informations de publication
Date de publication:
Dec 2020
Dec 2020
Historique:
received:
16
07
2020
accepted:
17
11
2020
entrez:
7
12
2022
pubmed:
24
11
2020
medline:
24
11
2020
Statut:
epublish
Résumé
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. 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. 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. Consistent with best practice guidelines, the statistical methodologies outlined in this SAP have been pre-specified prior to data pre-processing and model development.
Sections du résumé
Background
UNASSIGNED
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
UNASSIGNED
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
UNASSIGNED
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
UNASSIGNED
Consistent with best practice guidelines, the statistical methodologies outlined in this SAP have been pre-specified prior to data pre-processing and model development.
Identifiants
pubmed: 36474876
doi: 10.1016/j.ocarto.2020.100126
pii: S2665-9131(20)30126-6
pmc: PMC9718256
doi:
Types de publication
Journal Article
Langues
eng
Pagination
100126Informations de copyright
© 2020 The Authors.
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