title: Accounting for dropout in xenografted tumour efficacy studies: integrated endpoint analysis, reduced bias and better use of animals.
abstract: OBJECTIVE
  Xenograft studies are commonly used to assess the efficacy of new compounds and characterise their dose-response relationship. Analysis often involves comparing the final tumour sizes across dose groups. This can cause bias, as often in xenograft studies a tumour burden limit (TBL) is imposed for ethical reasons, leading to the animals with the largest tumours being excluded from the final analysis. This means the average tumour size, particularly in the control group, is underestimated, leading to an underestimate of the treatment effect.
  
  
  METHODS
  Four methods to account for dropout due to the TBL are proposed, which use all the available data instead of only final observations: modelling, pattern mixture models, treating dropouts as censored using the M3 method and joint modelling of tumour growth and dropout. The methods were applied to both a simulated data set and a real example.
  
  
  RESULTS
  All four proposed methods led to an improvement in the estimate of treatment effect in the simulated data. The joint modelling method performed most strongly, with the censoring method also providing a good estimate of the treatment effect, but with higher uncertainty. In the real data example, the dose-response estimated using the censoring and joint modelling methods was higher than the very flat curve estimated from average final measurements.
  
  
  CONCLUSIONS
  Accounting for dropout using the proposed censoring or joint modelling methods allows the treatment effect to be recovered in studies where it may have been obscured due to dropout caused by the TBL.
authors.affiliation: Centre for Applied Pharmacokinetic Research, Manchester Pharmacy School, The University of Manchester, Stopford Building 3.32, Oxford Road, Manchester, M13 9PT, UK. Emma.Martin@manchester.ac.uk.
authors.forename: Emma C
authors.initials: EC
authors.lastname: Martin
authors.affiliation: Centre for Applied Pharmacokinetic Research, Manchester Pharmacy School, The University of Manchester, Stopford Building 3.32, Oxford Road, Manchester, M13 9PT, UK.
authors.forename: Leon
authors.initials: L
authors.lastname: Aarons
authors.affiliation: AstraZeneca, Innovative Medicines, Oncology, Modelling and Simulation, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK.
authors.forename: James W T
authors.initials: JW
authors.lastname: Yates
mesh_terms: D000818:Animals; D000970:Antineoplastic Agents / Q000008:administration & dosage*; D015982:Bias; D004305:Dose-Response Relationship, Drug; D023381:Endpoint Determination; D006801:Humans; D051379:Mice; D008962:Models, Theoretical; D009369:Neoplasms / Q000188:drug therapy* / Q000473:pathology; D010352:Patient Dropouts / Q000706:statistics & numerical data*; D012107:Research Design; D047368:Tumor Burden; D035501:Uncertainty; D023041:Xenograft Model Antitumor Assays / Q000379:methods*
chemical_list: D000970:Antineoplastic Agents
keywords: Dropout; Joint models; Tumour growth models; Xenograft