title: Dose-finding studies, MCP-Mod, model selection, and model averaging: Two applications in the real world.
abstract: BACKGROUND
  Phase II clinical trials are important milestones to determine whether a dose-effect exists and to decide on future doses to use in confirmatory studies. To take into account the overall shape of the dose-response curve, modeling the relationship by linear or non-linear models is preferable to the classical pair-wise comparisons of the effect of each dose versus the placebo or the comparator. The multiple comparisons and modeling approach has been developed within the last 10 years to address this important question in the clinical development of drugs. Despite some recent publications referring to this methodology, few detailed applications have been shown so far and several practical questions remain to be addressed.
  
  
  METHODS
  Starting from a set of candidate models, model selection using classical methods criteria is possible. However, it suffers some limitations, not taking into account the uncertainty of the selection process itself. An attractive solution is to use model averaging, which applies appropriate weights to the parameters (e.g., the minimum effective dose) obtained from each model.
  
  
  RESULTS
  A discussion of the selection criteria is first presented. Through two real examples, how to proceed with model selection and model averaging is presented and discussed.
  
  
  CONCLUSIONS
  The first multiple comparisons and modeling approach papers addressed normal responses. More recently, an extension of this methodology has been proposed to deal with other types of responses, in particular binary, time-to-event and longitudinal data. Questions that remain are concerned with the choice of the candidate models and of their parameters' guesstimates.
  
  
  CONCLUSIONS
  The analysis of clinical dose-finding studies using a modeling of the entire curve offers a promising alternative as compared with the classical multiple comparisons methods, while not compromising the necessary rigor of the analysis.
authors.affiliation: Department of Biostatistics and Programming, Sanofi R&D, Montpellier, France dominique.verrier@sanofi.com.
authors.forename: Dominique
authors.initials: D
authors.lastname: Verrier
authors.affiliation: Centre d'Investigation Clinique - Epidémiologie Clinique Antilles Guyane, Inserm/DGOS CIE 802, Guyane, France.
authors.forename: Sïndou
authors.initials: S
authors.lastname: Sivapregassam
authors.affiliation: Department of Biostatistics and Programming, Sanofi R&D, Paris, France.
authors.forename: Anne-Catherine
authors.initials: AC
authors.lastname: Solente