Developing prediction models for total knee replacement surgery in patients with osteoarthritis: Statistical analysis plan. Issue 4 (December 2020)

• Record Type: Journal Article
• Authors: Thuraisingam, Sharmala; Dowsey, Michelle; Manski-Nankervis, Jo-Anne; Spelman, Tim; Choong, Peter; Gunn, Jane; Chondros, Patty
• Is Part Of: Osteoarthritis and cartilage open. Volume 2:Issue 4(2020)
• Publication Date: 2020-12
• Subjects: Prediction models -- Clinical prediction tools -- Statistical analysis plan -- Electronic medical record -- Electronic health record -- Knee replacement -- General practice -- Primary care; 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 -- DQA data quality assessment -- EMR electronic medical record -- GP General Practitioner -- KOS-ADLS Knee Outcome Survey-Activities of Daily Living Subscale -- NDI National Death Index -- NPS National Prescribing Service -- OA osteoarthritis -- OMERACT Outcome Measures in Rheumatology -- OARSI Osteoarthritis Research Society International -- SAP statistical analysis plan -- SF-36 36-Item Short Form Health Survey -- SF-12 12-Item Short Form Survey -- TKR total knee replacement; Osteoarthritis -- Periodicals; Cartilage -- Periodicals; 616.7223005
• DOI: 10.1016/j.ocarto.2020.100126
• Languages: English
• ISSNs: 2665-9131

Abstract

S U M M A R Y: 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.