Does randomization assert the balance across trial arms? Revisiting Worrall's criticism

Area of Science:

• Biostatistics
• Epidemiology
• Clinical Trials

Background:

• John Worrall's argument challenges the efficacy of randomization in balancing known and unknown confounders.
• This argument is based on the premise of needing perfect balance for point estimates and misunderstands the role of individual confounders versus their collective impact.

Purpose of the Study:

• To critically re-evaluate Worrall's argument against randomization's balancing properties.
• To defend the statistical validity of randomization in mitigating confounder impact.
• To clarify the statistical interpretation of balance in randomized studies.

Main Methods:

• The study analyzes Worrall's argument, identifying its statistical inconsistencies.
• It proposes a statistical framework for understanding randomization's balancing role.
• A review of existing literature and recent arguments supporting randomization's expectancy-based balance is included.

Main Results:

• Worrall's argument is shown to be at odds with statistical theory regarding confounder balance.
• Randomization balances confounders in expectancy, which is sufficient for unbiased causal effect estimation.
• The probability of deviation from balance can be calculated through statistical analysis at design and analysis stages.

Conclusions:

• Randomization effectively balances confounders in expectancy, not necessarily for each individual confounder.
• The average impact of all confounders is the key factor for unbiased estimates in randomized controlled trials (RCTs).
• Statistical methods allow for quantifying the likelihood of deviations from balance, reinforcing the reliability of RCTs.