title: Applying Hodges-Lehmann scale parameter estimates to hospital discharge times.
abstract: BACKGROUND
  Clinical trials aimed at shortening the time-to-discharge need to have rational and easily understood effect size estimates for health care management organizations. A natural choice is a scale model, where the distribution of time to discharge on one treatment is assumed to be the same as rho times that on the other. If, for example rho=0.6, then one treatment is associated with a 40% reduction in discharge time. In designing and analyzing these studies, we need to have the capability to accommodate right censored data, as it is plausible that some patients may never meet the discharge criteria, even though they do get discharged.
  
  
  OBJECTIVE
  Utilizing the ideas of Hodges and Lehmann, to provide methods for analysis of trials aimed at shortening hospital discharge times, using point and interval estimates of scale parameters based on the Gehan generalization of the Mann-Whitney-Wilcoxon test, which accommodates right censoring for situations where patients never meet discharge criteria (+infinity).
  
  
  METHODS
  For every value of rho > 0, we shall test the null hypothesis that the distribution of discharge times on one treatment is the same rho times that on the other. The values of rho that we fail to reject form the confidence interval for the true rho.
  
  
  RESULTS
  The methods were developed and applied to a real clinical trial for times to meet the three objective discharge criteria in knee replacement surgery for two post-operative pain control strategies (usual care plus a perineural infusion of either placebo or 0.2% ropivocaine, until the morning following surgery). Based on 48 randomized patients, the point estimate (95% confidence limits) for rho was 0.47 (0.32-0.67), favoring ropivocaine.
  
  
  CONCLUSIONS
  The methods cannot as yet be applied to group sequential designs or studies with more than two treatments.
  
  
  CONCLUSIONS
  This methodology is an effective way to analyze two-arm trials involving continuous hospital discharge time data.
authors.affiliation: Department of Epidemiology and Health Policy Research, University of Florida, Gainesville, Florida 32610-0177, USA. jshuster@biostat.ufl.edu
authors.forename: Jonathan J
authors.initials: JJ
authors.lastname: Shuster
authors.forename: Douglas W
authors.initials: DW
authors.lastname: Theriaque
authors.forename: Brian M
authors.initials: BM
authors.lastname: Ilfeld
mesh_terms: D000577:Amides / Q000627:therapeutic use; D000779:Anesthetics, Local / Q000627:therapeutic use; D019645:Arthroplasty, Replacement, Knee / Q000009:adverse effects; D006801:Humans; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care; D010149:Postoperative Pain / Q000188:drug therapy / Q000209:etiology; D010351:Patient Discharge; D011237:Predictive Value of Tests; D016032:Randomized Controlled Trials as Topic; D000077212:Ropivacaine; D018709:Statistics, Nonparametric; D013997:Time Factors
chemical_list: D000577:Amides; D000779:Anesthetics, Local; D000077212:Ropivacaine