Prognostic Targeting Improves Statistical Power and Efficiency in Randomized Controlled Trials in Upper Extremity...

Area of Science:

• Neuroscience
• Rehabilitation Medicine
• Clinical Trials

Background:

• Randomized Controlled Trials (RCTs) are crucial for evaluating interventions for upper extremity capacity post-stroke.
• Heterogeneity in stroke recovery often leads to underpowered RCTs, necessitating larger sample sizes.
• Prognostic targeting offers a strategy to potentially reduce sample sizes while maintaining statistical power.

Purpose of the Study:

• To investigate the impact of prognostic targeting on sample size requirements for early post-stroke Randomized Controlled Trials (RCTs).
• To assess the effect of prognostic targeting on statistical power (70%-90%) in trials measuring upper extremity capacity using the Action Research Arm Test (ARAT).

Main Methods:

• Pooled data from 372 patients across 4 prospective cohort studies, with assessments from 1 week to 6 months post-stroke.
• Generated synthetic 6-month ARAT outcomes and performed cross-sectional and longitudinal analyses.
• Calculated statistical power and trial efficiency with and without prognostic targeting based on a predictive model.

Main Results:

• Prognostic targeting within 3 weeks post-stroke theoretically decreased required sample size by up to 56%.
• Trial efficiency improved by 40-45% for detecting a 6-point ARAT difference at 6 months.
• Targeted trials required 220-360 patients versus 470-820 in non-targeted trials for 70%-90% power, with benefits seen in longitudinal analyses.

Conclusions:

• Prognostic targeting enhances statistical power and efficiency in early post-stroke upper extremity trials using ARAT.
• The study strongly recommends the implementation of prognostic targeting in future stroke rehabilitation and recovery research.
• This approach optimizes resource allocation and accelerates the evaluation of novel interventions.