title: The influence of baseline marijuana use on treatment of cocaine dependence: application of an informative-priors bayesian approach.
abstract: BACKGROUND
  Marijuana use is prevalent among patients with cocaine dependence and often non-exclusionary in clinical trials of potential cocaine medications. The dual-focus of this study was to (1) examine the moderating effect of baseline marijuana use on response to treatment with levodopa/carbidopa for cocaine dependence; and (2) apply an informative-priors, Bayesian approach for estimating the probability of a subgroup-by-treatment interaction effect.
  
  
  METHODS
  A secondary data analysis of two previously published, double-blind, randomized controlled trials provided complete data for the historical (Study 1: N = 64 placebo), and current (Study 2: N = 113) data sets. Negative binomial regression evaluated Treatment Effectiveness Scores (TES) as a function of medication condition (levodopa/carbidopa, placebo), baseline marijuana use (days in past 30), and their interaction.
  
  
  RESULTS
  Bayesian analysis indicated that there was a 96% chance that baseline marijuana use predicts differential response to treatment with levodopa/carbidopa. Simple effects indicated that among participants receiving levodopa/carbidopa the probability that baseline marijuana confers harm in terms of reducing TES was 0.981; whereas the probability that marijuana confers harm within the placebo condition was 0.163. For every additional day of marijuana use reported at baseline, participants in the levodopa/carbidopa condition demonstrated a 5.4% decrease in TES; while participants in the placebo condition demonstrated a 4.9% increase in TES.
  
  
  CONCLUSIONS
  The potential moderating effect of marijuana on cocaine treatment response should be considered in future trial designs. Applying Bayesian subgroup analysis proved informative in characterizing this patient-treatment interaction effect.
authors.affiliation: University of Texas Health Sciences Center at Houston Houston, TX, USA.
authors.forename: Charles
authors.initials: C
authors.lastname: Green
authors.forename: Joy
authors.initials: J
authors.lastname: Schmitz
authors.forename: Jan
authors.initials: J
authors.lastname: Lindsay
authors.forename: Claudia
authors.initials: C
authors.lastname: Pedroza
authors.forename: Scott
authors.initials: S
authors.lastname: Lane
authors.forename: Rob
authors.initials: R
authors.lastname: Agnelli
authors.forename: Kimberley
authors.initials: K
authors.lastname: Kjome
authors.forename: F Gerard
authors.initials: FG
authors.lastname: Moeller
keywords: Bayesian; cocaine; marijuana; subgroup analysis; treatment response