Abstract:
OBJECTIVE : By presenting a simulation study that compares Bayesian
and classical frequentist approaches to research design, this paper describes and
demonstrates a Bayesian perspective on intervention research. METHOD : Using hypothetical
pilot-study data where an effect size of 0.2 had been observed, we designed
a 2-arm trial intended to compare an intervention with a control condition
(e.g., usual services). We determined the trial sample size by a power analysis with
a Type I error probability of 2.5% (1-sided) at 80% power. Following a Monte-Carlo
computational algorithm, we simulated 1 million outcomes for this study and then
compared the performance of the Bayesian perspective with the performance of
the frequentist analytic perspective. Treatment effectiveness was assessed using a
frequentist t-test and an empirical Bayesian t-test. Statistical power was calculated
as the criterion for comparison of the 2 approaches to analysis. RESULTS : In the simulations,
the classical frequentist t-test yielded 80% power as designed. However,
the Bayesian approach yielded 92% power. CONCLUSION : Holding sample size constant,
a Bayesian analytic approach can improve power in intervention research.
A Bayesian approach may also permit smaller samples holding power constant. Using
a Bayesian analytic perspective could reduce design demands in the developmental
experimentation that typifies intervention research.
