title: Sample size determination for health psychology interventions with binomially distributed outcomes.
abstract: Health intervention outcomes are often assessed as binomially distributed variables. In designing such interventions it is important to model the pre-intervention rate of the target behavior when performing sample size calculations. Unfortunately, the majority of sample size programs model post-intervention outcomes only, which results in exaggerated sample size estimates. An exception is Yoo and Spoth's (1993) conditional binomial method of sample size determination. This approach explicitly models pre-intervention behavior by focusing on baserate-adjusted post-intervention outcomes, and always results in smaller sample size estimates than conventional approaches. Advantages of the conditional binomial method are discussed and user-friendly software is presented.
authors.affiliation: Department of Psychology, College of Charleston, Charleston, SC 29424, USA. hittnerj@cofc.edu
authors.forename: James B
authors.initials: JB
authors.lastname: Hittner
authors.forename: Kim
authors.initials: K
authors.lastname: May
authors.forename: N Clayton
authors.initials: NC
authors.lastname: Silver
mesh_terms: D000293:Adolescent; D001524:Behavioral Medicine / Q000706:statistics & numerical data*; D016010:Binomial Distribution; D006293:Health Promotion; D006801:Humans; D010043:Outcome and Process Assessment, Health Care / Q000379:methods / Q000706:statistics & numerical data; D012107:Research Design; D040242:Risk Reduction Behavior; D018401:Sample Size