Simulation-Based Analysis of Trial Design in Regional Anesthesia

Area of Science:

• Anesthesiology
• Clinical Trials
• Statistical Modeling

Background:

• Regional anesthesia research relies on randomized controlled trials (RCTs) to evaluate novel blocks.
• Systematic reviews often cite small sample sizes in RCTs as a limitation for drawing firm conclusions.
• This study investigates if small sample size is an inherent characteristic of RCTs for novel blocks with typical effect sizes.

Purpose of the Study:

• To statistically simulate the expected sample size and number of large trials in regional anesthesia research for novel blocks.
• To determine if small sample sizes in existing literature are a predictable outcome of trial design.
• To explore potential modifications to trial design to improve data aggregation in meta-analyses.

Main Methods:

• A series of RCTs were simulated, comparing a novel block against a placebo using a continuous outcome.
• Input parameters for the simulation were derived from a systematic literature search of meta-analyses.
• Primary simulation outcomes included the predicted number of large trials (N ≥ 256) and total patient enrollment.

Main Results:

• Simulations predicted 16 RCTs for a novel block, with a median enrollment of 970 patients, and no large trials (N ≥ 256).
• Modifying the significance threshold to p < 0.005 increased patient enrollment in meta-analyses but led to early trial termination due to futility in block-vs-block comparisons.
• The findings suggest small sample sizes are a rational outcome of current trial designs.

Conclusions:

• Small sample sizes are an expected and rational outcome in regional anesthesia RCTs comparing novel blocks to placebo.
• The feasibility of conducting large trials is unlikely to significantly alter conclusions for block-vs-placebo comparisons.
• Current trial designs may inherently limit the conclusiveness of evidence for novel regional anesthesia techniques.