title: Simple options for improving signal detection in antidepressant clinical trials.
abstract: OBJECTIVE
  Previous experience with antidepressant studies highlight the difficulties in discriminating an effective drug from placebo. In hopes of improving signal detection, three easy-to-implement methodologies were employed during the development of a recently approved antidepressant.
  
  
  METHODS
  Results from alternative and traditional methods could be compared directly because most studies employed both methods. This database included 11 double-blind, placebo-controlled trials (some with multiple dose arms and/or active comparators) yielding 22 treatment arms of antidepressants at or above the minimally effective dose noted in their U.S. labels.
  
  
  RESULTS
  Results agreed with the previous evidence showing that the performance of a likelihood-based, mixed-effects model repeated measures (MMRM) analysis was superior to that of analysis of covariance with missing values imputed using the last observation carried forward (LOCF) approach; MMRM correctly identified drug as superior to placebo in 14/22 (63.6%) comparisons versus 11/22 (50.0%) for LOCF. In agreement with previous studies, use of subscales of the Hamilton Depression Rating scale (HAMD) improved signal detection compared to the HAMD total score. Using MMRM with HAMD subscales correctly identified drug as superior to placebo in up to 17/22 (77.3%) comparisons. Excluding double-blind, placebo lead-in responders did not increase the frequency of correctly identifying drug-versus-placebo differences.
  
  
  CONCLUSIONS
  The 22 drug-versus-placebo comparisons in this report offer a small amount of evidence and therefore may not be convincing on their own, although results do agree with previous research. Researchers may be able to take advantage of these easy-to-implement methods while we wait for further improvements in other areas.
authors.affiliation: Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana, USA. cmallinc@lilly.com
authors.forename: Craig H
authors.initials: CH
authors.lastname: Mallinckrodt
authors.forename: Adam L
authors.initials: AL
authors.lastname: Meyers
authors.forename: Apurva
authors.initials: A
authors.lastname: Prakash
authors.forename: Douglas E
authors.initials: DE
authors.lastname: Faries
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Detke
mesh_terms: D000928:Antidepressive Agents / Q000008:administration & dosage*; D015283:Citalopram / Q000008:administration & dosage; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D016208:Databases, Factual; D003865:Major Depressive Disorder / Q000175:diagnosis / Q000188:drug therapy* / Q000523:psychology; D004305:Dose-Response Relationship, Drug; D004311:Double-Blind Method; D000068736:Duloxetine Hydrochloride; D005473:Fluoxetine / Q000008:administration & dosage; D016013:Likelihood Functions; D015233:Models, Statistical; D017374:Paroxetine / Q000008:administration & dosage; D010555:Personality Inventory / Q000706:statistics & numerical data; D015990:Placebo Effect; D011594:Psychometrics / Q000706:statistics & numerical data; D015203:Reproducibility of Results; D012107:Research Design; D017603:Signal Detection, Psychological; D013876:Thiophenes / Q000008:administration & dosage; D016896:Treatment Outcome
chemical_list: D000928:Antidepressive Agents; D013876:Thiophenes; D005473:Fluoxetine; D015283:Citalopram; D017374:Paroxetine; D000068736:Duloxetine Hydrochloride