Clinical Trials in Orthopaedics Research. Part III. Overcoming Operational Challenges in the Design and Conduct of Randomized Clinical Trials in Orthopaedic Surgery
Elena Losina, PhD; James Wright, MD, MPH; Jeffrey N. Katz, MD, MSc

As we aspire to implement evidence-based medicine in the daily practice of orthopaedics, it is critical to understand the level of evidence that guides routine clinical decisions. Randomized controlled trials (RCTs) offer methodology for delivering the highest level of evidence1. Whereas the first RCTs were conducted half a century ago to test the efficacy of new pharmacologic agents, such trials are now being used increasingly to evaluate therapies other than medications, including surgery and behavioral interventions. Surgical RCTs involve additional complexity with regard to several key methodological components of the RCT: the willingness of patients to enroll, implementation of blinding (masking), and the impact of crossovers from one trial arm to another. The American Academy of Orthopaedic Surgeons (AAOS) and the Orthopaedic Research Society (ORS) sponsored a symposium on clinical trials in orthopaedic research in May 2009 at which each of these challenges was discussed2,3. Discussions during the symposium focused on offering suggestions regarding how to address these methodological issues during the design and implementation phases of a study. This paper summarizes key discussion points highlighted during the AAOS-ORS Symposium and comments on related issues, including compliance with nonsurgical regimens in studies comparing surgery with nonoperative treatment and the appropriate use of subgroup analysis. The paper concludes with suggestions regarding cost-effectiveness analysis in orthopaedic research.

Planning Trials and Performing Sample Size Calculations

For an RCT to make a difference in the clinical management of musculoskeletal disorders, it must be planned carefully. In addition to detailed specification of the treatment regimen for each arm in the trial, it is critical to perform a careful analysis of the sample size required to conduct a trial that is conclusive (positive or negative). A study with an insufficient sample size may fail to detect a statistically significant difference between the arms of the study even …

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