Abstract:
OBJECTIVE : In intervention research, the decision to continue developing
a new program or treatment is dependent on both the change-inducing potential
of a new strategy (i.e., its effect size) and the methods used to measure change,
including the size of samples. This article describes a Bayesian approach to determining
sample sizes in the sequential development of interventions. DESCRIPTION :
Because sample sizes are related to the likelihood of detecting program effects,
large samples are preferred. But in the design and development process that characterizes
intervention research, smaller scale studies are usually required to justify
more costly, larger scale studies. We present 4 scenarios designed to address common
but complex questions regarding sample-size determination and the risk of
observing misleading (e.g., false-positive) findings. From a Bayesian perspective,
this article describes the use of decision rules composed of different target probabilities
and prespecified effect sizes. Monte-Carlo simulations are used to demonstrate
a Bayesian approach—which tends to require smaller samples than the classical
frequentist approach—in the development of interventions from one study to
the next.
