October 2023 CME Activity: Statistical and Methodological Considerations for Randomized Controlled Trial Design in Physical Medicine and Rehabilitation-Statistical and Methodological Considerations for Randomized Controlled Trial Design in Physical Medicine and Rehabilitation

Statistical and Methodological Considerations for Randomized Controlled Trial Design in Physical Medicine and Rehabilitation

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The document provides a detailed examination of the unique statistical and methodological challenges encountered in designing randomized controlled trials (RCTs) within the field of physical medicine and rehabilitation (PM&R). RCTs are vital for generating high-quality evidence in medical interventions, but PM&R trials face specific difficulties due to the complexity of interventions and variable patient treatments.<br /><br />Key issues discussed include:<br />1. **Blinding**: Essential for reducing outcome measure bias, yet challenging in PM&R. Solutions include using sham therapies and blinding assessors, data collectors, and statisticians.<br />2. **Heterogeneity in Treatment and Treatment Effects**: Variations in therapy delivery and patient responses complicate the trials. Addressed through drawing knowledge from previous studies, standardized protocols, and using appropriate statistical models.<br />3. **Patient-Reported Outcome Measures (PROMs)**: Crucial but limited by potential response bias. PROMs validated and relevant to study aims should be selected, with adherence to reporting guidelines like SPIRIT-PRO and CONSORT-PRO.<br />4. **Outcome Scales and Power**: Continuous measures adjusted for baseline status are emphasized for better statistical power. Drawbacks of dichotomizing continuous variables are noted, highlighting the importance of using more nuanced categories or repeated measures.<br />5. **Sample Size Computation**: Accurate estimations are necessary but often challenging due to lack of prior data. Recommended steps include identifying primary outcomes, considering interaction terms, and using pilot studies to inform larger RCTs.<br />6. **Treatment Compliance and Missing Data**: Crucial for maintaining trial validity. Strategies for improving adherence include clear communication and incentives. Missing data should be carefully handled using statistical methods classified by Rubin (MCAR, MAR, MNAR).<br /><br />Statistical models for longitudinal data such as mixed-effects models and generalized estimating equations (GEE) are recommended, with specific suggestions on their application and interpretation. The document also emphasizes the importance and benefits of involving statisticians throughout the study process and suggests the inclusion of data safety monitoring boards to ensure participant rights and safety.<br /><br />Ultimately, the article provides extensive recommendations for addressing the discussed challenges, enhancing the design, analysis, and reporting of RCTs in PM&R to ensure meaningful and generalizable results.

Keywords

randomized controlled trials

physical medicine and rehabilitation

statistical challenges

methodological challenges

blinding

patient-reported outcome measures

sample size computation

treatment compliance

missing data

longitudinal data models