
This study outlines a process for synthesizing expert knowledge to improve covariate selection for long-term follow-up in randomized controlled trials (RCTs). Using the example of an RCT for meniscal tears in knee osteoarthritis patients, the researchers identified post-randomization events impacting pain outcomes beyond 5 years, such as loss to follow-up and total knee replacement. They conducted literature searches and combined findings with expert input, identifying 94 potential covariates related to pain and total knee replacement in knee osteoarthritis. The process demonstrated that expert input adds valuable information for covariate selection in long-term RCT follow-up.
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