title: Integrating biomarker information within trials to evaluate treatment mechanisms and efficacy for personalised medicine.
abstract: BACKGROUND
  The development of personalised (stratified) medicine is intrinsically dependent on an understanding of treatment-effect mechanisms (effects on therapeutic targets that mediate the effect of the treatment on clinical outcomes). There is a need for clinical trial data for the joint evaluation of treatment efficacy, the utility of predictive markers as indicators of treatment efficacy, and the mediational mechanisms proposed as the explanation of these effects.
  
  
  OBJECTIVE
  (1) To review the problem of confounding (common causes) for the drawing of valid inferences concerning treatment-effect mechanisms, even when the data have been generated using a randomised controlled trial, and (2) to suggest and illustrate solutions to this problem of confounding.
  
  
  RESULTS
  We illustrate the potential of the predictive biomarker stratified design, together with baseline measurement of all known prognostic markers, to enable us to evaluate both the utility of the predictive biomarker in such a stratification and, perhaps more importantly, to estimate how much of the treatment's effect is actually explained by changes in the putative mediator. The analysis strategy involves the use of instrumental variable (IV) regression, using the treatment by predictive biomarker interaction as an IV - a refined, much more powerful, and (in the present context) subtle use of Mendelian randomisation.
  
  
  CONCLUSIONS
  Personalised (stratified) medicine and treatment-effect mechanisms evaluation are inextricably linked. Stratification without corresponding mechanisms evaluation lacks credibility. In the presence of mediator-outcome confounding, mechanisms evaluation is dependent on stratification for its validity. Both stratification and treatment-effect mediation can be evaluated using a biomarker stratified trial design together with detailed baseline measurement of all known prognostic biomarkers and other prognostic covariates. Direct and indirect (mediated) effects should be estimated through the use of IV methods (the IV being the predictive marker by treatment interaction) together with adjustments for all known prognostic markers (confounders) - the latter adjustments contributing to increased precision (as in a conventional analysis of treatment effects) rather than bias reduction.
authors.affiliation: aCentre for Biostatistics, Institute of Population Health, University of Manchester, Manchester, UK.
authors.forename: Graham
authors.initials: G
authors.lastname: Dunn
authors.forename: Richard
authors.initials: R
authors.lastname: Emsley
authors.forename: Hanhua
authors.initials: H
authors.lastname: Liu
authors.forename: Sabine
authors.initials: S
authors.lastname: Landau
mesh_terms: D000319:Adrenergic beta-Antagonists / Q000627:therapeutic use; D015415:Biomarkers; D001943:Breast Neoplasms / Q000235:genetics; D015984:Causality; D002986:Clinical Trials as Topic / Q000379:methods*; D015986:Confounding Factors, Epidemiologic; D006801:Humans; D006973:Hypertension / Q000188:drug therapy; D057285:Precision Medicine / Q000379:methods*; D011237:Predictive Value of Tests; D016032:Randomized Controlled Trials as Topic / Q000379:methods; D018719:Erb-b2 Receptor Tyrosine Kinases
chemical_list: D000319:Adrenergic beta-Antagonists; D015415:Biomarkers; D018719:Erb-b2 Receptor Tyrosine Kinases; C508053:ERBB2 protein, human