Causal relationships between pain, medical treatments, and knee osteoarthritis: A graphical causal model to guide analyses
Authors' conclusion
We apply this process to the long-term follow-up of a randomized trial and show that expert input provides additional information not obtainable from literature reviews alone.
Study context
- Population
- Individuals with knee osteoarthritis (OA); long-term follow-up of an RCT of treatments for meniscal tears in patients with knee OA
- Outcome
- Covariates potentially related to pain and/or TKR in knee OA
Methodological detailsliterature review combined with expert input, graphical causal model, Post-randomization events considered: loss to follow-up and total knee replacement (TKR), for pain outcomes beyond 5 years
- Literature review combined with expert input
- Graphical causal model
- Post-randomization events considered: loss to follow-up and total knee replacement (TKR), for pain outcomes beyond 5 years
Main finding
As reported in the abstract
Potential covariates identified
94
Identified in literature review
46
Identified by expert panelists
48
Covariates that may require adjustment to estimate long-term effects of knee OA treatments on pain
50
Original publication
Causal relationships between pain, medical treatments, and knee osteoarthritis: A graphical causal model to guide analyses. Osteoarthritis Cartilage. Published 7 November 2023. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →