title: Randomized clinical trials to identify optimal antibiotic treatment duration.
abstract: BACKGROUND
  Antibiotic resistance is a major barrier to the continued success of antibiotic treatment. Such resistance is often generated by overly long durations of antibiotic treatment. A barrier to identifying the shortest effective treatment duration is the cost of the sequence of clinical trials needed to determine shortest optimal duration. We propose a new method to identify the optimal treatment duration of an antibiotic treatment regimen.
  
  
  METHODS
  Subjects are randomized to varying treatment durations and the cure proportions of these durations are linked using a logistic regression model, making effective use of information across all treatment duration groups. In this paper, Monte Carlo simulation is used to evaluate performance of such a model.
  
  
  RESULTS
  Using a hypothetical dataset, the logistic regression model is seen to provide increased precision in defining the point estimate and confidence interval (CI) of the cure proportion at each treatment duration. When applied to the determination of non-inferiority, the regression model allows identification of the shortest duration meeting the predefined non-inferiority margin.
  
  
  CONCLUSIONS
  This analytic strategy represents a practical way to develop shortened regimens for tuberculosis and other infectious diseases. Application of this strategy to clinical trials of antibiotic therapy could facilitate decreased antibiotic usage, reduce cost, minimize toxicity, and decrease the emergence of antibiotic resistance.
authors.affiliation: Department of Epidemiology, Boston University School of Public Health, 715 Albany Street, Boston, MA 02118, USA. rhorsbu@bu.edu
authors.forename: C Robert
authors.initials: CR
authors.lastname: Horsburgh
authors.forename: Kimberly M
authors.initials: KM
authors.lastname: Shea
authors.forename: Patrick
authors.initials: P
authors.lastname: Phillips
authors.forename: Michael
authors.initials: M
authors.lastname: Lavalley
mesh_terms: D000900:Anti-Bacterial Agents / Q000008:administration & dosage* / Q000009:adverse effects; D001424:Bacterial Infections / Q000175:diagnosis / Q000188:drug therapy* / Q000382:microbiology; D002985:Clinical Protocols; D003198:Computer Simulation; D016001:Confidence Intervals; D004334:Drug Administration Schedule; D024881:Drug Resistance, Bacterial; D006801:Humans; D016015:Logistic Models; D009010:Monte Carlo Method; D016032:Randomized Controlled Trials as Topic / Q000379:methods* / Q000706:statistics & numerical data; D012074:Remission Induction; D012107:Research Design / Q000706:statistics & numerical data; D013997:Time Factors; D016896:Treatment Outcome
chemical_list: D000900:Anti-Bacterial Agents
references.citation: Int J Tuberc Lung Dis. 1999 Oct;3(10 Suppl 2):S231-79
references.pmid: 10529902
references.citation: Pharmacol Ther. 2000 Oct;88(1):33-58
references.pmid: 11033383
references.citation: J Infect Dis. 2008 Apr 15;197(8):1079-81
references.pmid: 18419525
references.citation: Clin Infect Dis. 2008 Feb 15;46(4):491-6
references.pmid: 18194098
references.citation: JAMA. 2006 Mar 8;295(10):1152-60
references.pmid: 16522836
references.citation: J Infect Dis. 2012 May 15;205 Suppl 2:S241-9
references.pmid: 22448022
references.citation: Tuberculosis (Edinb). 2008 Aug;88 Suppl 1:S93-100
references.pmid: 18762157
references.citation: PLoS Med. 2008 Jul 8;5(7):e150
references.pmid: 18613746