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

Randomized Controlled Trials (RCTs) are essential to underpin the superiority of novel interventions affecting upper extremity capacity post-stroke. However, many RCTs are underpowered, due to heterogeneity in recovery. Prognostic targeting may help reduce sample sizes while maintaining sufficient power.

This study investigates the effects of prognostic targeting on the required sample size to achieve 70% to 90% power in early post-stroke RCTs with upper extremity capacity measured with the Action Research Arm Test (ARAT) as the outcome.

Serial data from 4 prospective cohort studies (N = 372 stroke patients) were pooled, with assessments from week 1 to 6 months post-stroke. Using this dataset, we generated synthetic 6-month ARAT outcomes and analyzed data cross-sectionally and longitudinally, with and without prognostic targeting based on a pre-existing prognostic model predicting 6-month outcome. We then calculated power for different sample sizes and assessed trial efficiency, determined by the estimated sample size and inclusion rate.

Prognostic targeting within 3 weeks post-stroke theoretically reduced the required sample size by up to 56% and improved trial efficiency by 40 to 45% for detecting a 6-point ARAT difference at 6 months. The targeted trials needed 220, 270, and 360 patients vs. 470, 560, and 820 in non-targeted trials for 70% to 90% power. Benefits persisted in longitudinal analyses.

This study demonstrates the benefits of prognostic targeting for improving power and efficiency in early post-stroke upper extremity trials using ARAT as outcome. We strongly recommend its use in future stroke rehabilitation and recovery studies.