title: Bayesian accrual prediction for interim review of clinical studies: open source R package and smartphone application.
abstract: Subject recruitment for medical research is challenging. Slow patient accrual leads to increased costs and delays in treatment advances. Researchers need reliable tools to manage and predict the accrual rate. The previously developed Bayesian method integrates researchers' experience on former trials and data from an ongoing study, providing a reliable prediction of accrual rate for clinical studies.
  
  
  
  In this paper, we present a user-friendly graphical user interface program developed in R. A closed-form solution for the total subjects that can be recruited within a fixed time is derived. We also present a built-in Android system using Java for web browsers and mobile devices.
  
  
  
  Using the accrual software, we re-evaluated the Veteran Affairs Cooperative Studies Program 558- ROBOTICS study. The application of the software in monitoring and management of recruitment is illustrated for different stages of the trial.
  
  
  
  This developed accrual software provides a more convenient platform for estimation and prediction of the accrual process.
authors.affiliation: Division of Epidemiology, Biostatistics, and Environmental Health, School of Public Health, University of Memphis, Memphis, TN, 38152, USA. yjiang4@memphis.edu.|Cooperative Studies Program, VA Connecticut Healthcare System, West Haven, CT, 06516, USA. yjiang4@memphis.edu.
authors.forename: Yu
authors.initials: Y
authors.lastname: Jiang
authors.affiliation: Cooperative Studies Program, VA Connecticut Healthcare System, West Haven, CT, 06516, USA.|Statistical Center for HIV/AIDS Research Prevention, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA.|Department of Biostatistics, School of Public Health, Yale University, New Haven, 06520, USA.
authors.forename: Peter
authors.initials: P
authors.lastname: Guarino
authors.affiliation: Cooperative Studies Program, VA Connecticut Healthcare System, West Haven, CT, 06516, USA.|Department of Biostatistics, School of Public Health, Yale University, New Haven, 06520, USA.
authors.forename: Shuangge
authors.initials: S
authors.lastname: Ma
authors.affiliation: P.Mean Consulting, Leawood, KS, 66224, USA.|Department of Biomedical and Health Informatics, University of Missouri at Kansas City, Kansas City, MO, 64110, USA.
authors.forename: Steve
authors.initials: S
authors.lastname: Simon
authors.affiliation: Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS, 66160, USA.|The University of Kansas Cancer Center, Kansas City, KS, 66160, USA.
authors.forename: Matthew S
authors.initials: MS
authors.lastname: Mayo
authors.affiliation: Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS, 66160, USA.
authors.forename: Rama
authors.initials: R
authors.lastname: Raghavan
authors.affiliation: Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS, 66160, USA.
authors.forename: Byron J
authors.initials: BJ
authors.lastname: Gajewski
mesh_terms: D001499:Bayes Theorem; D003196:Computer Graphics; D003627:Data Interpretation, Statistical; D006801:Humans; D063731:Mobile Applications; D015233:Models, Statistical; D015337:Multicenter Studies as Topic / Q000379:methods / Q000706:statistics & numerical data*; D018579:Patient Selection; D016032:Randomized Controlled Trials as Topic / Q000379:methods / Q000706:statistics & numerical data*; D012371:Robotics; D018401:Sample Size; D000068997:Smartphone; D020521:Stroke / Q000175:diagnosis / Q000503:physiopathology / Q000628:therapy; D000071939:Stroke Rehabilitation / Q000379:methods; D013997:Time Factors; D014584:User-Computer Interface
keywords: Bayesian methods; Smartphone application; Statistical software; Subject accrual