title: Optimizing Clinical Trial Enrollment Methods Through "Goal Programming"
abstract: BACKGROUND
  Clinical trials often fail to reach desired goals due to poor recruitment outcomes, including low participant turnout, high recruitment cost, or poor representation of minorities. At present, there is limited literature available to guide recruitment methodology. This study, conducted by researchers at the University of Wisconsin Center for Tobacco Research and Intervention (UW-CTRI), provides an example of how iterative analysis of recruitment data may be used to optimize recruitment outcomes during ongoing recruitment.
  
  
  METHODS
  UW-CTRI's research team provided a description of methods used to recruit smokers in two randomized trials (n= 196 andn= 175). The trials targeted low socioeconomic status (SES) smokers and involved time-intensive smoking cessation interventions. Primary recruitment goals were to meet required sample size and provide representative diversity while working with limited funds and limited time. Recruitment data was analyzed repeatedly throughout each study to optimize recruitment outcomes.
  
  
  RESULTS
  Estimates of recruitment outcomes based on prior studies on smoking cessation suggested that researchers would be able to recruit 240 low SES smokers within 30 months at a cost of $72,000. With employment of methods described herein, researchers were able to recruit 374 low SES smokers over 30 months at a cost of $36,260.
  
  
  CONCLUSIONS
  Each human subjects study presents unique recruitment challenges with time and cost of recruitment dependent on the sample population and study methodology. Nonetheless, researchers may be able to improve recruitment outcomes though iterative analysis of recruitment data and optimization of recruitment methods throughout the recruitment period.
authors: J M
authors: JM
authors: Davis
authors: A J
authors: AJ
authors: Sandgren
authors: A R
authors: AR
authors: Manley
authors: M A
authors: MA
authors: Daleo
authors: S S
authors: SS
authors: Smith
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