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SUMMARY:Bayesian and structural integration of background evidence in the 
 design\, analysis and interpretation of clinical trial data - Fabio Rigat 
 (GSK) & Nicky Best (Imperial College)
DTSTART:20181120T191500Z
DTEND:20181120T213000Z
UID:TALK95869@talks.cam.ac.uk
CONTACT:Peter Watson
DESCRIPTION:Although background evidence always informs clinical study des
 ign\, individual trial data are often first analysed in isolation and then
  meta-analysis is used to synthesise evidence accrued across multiple stud
 ies. However\, in “small population” settings such as rare diseases\, 
 paediatric populations\, or personalised/stratified medicine\, the feasibl
 e sample size that can be recruited in each individual study typically fal
 ls short of that needed for a conventionally-powered trial. To address the
 se limitations\, we assess two avenues for integrated analysis of clinical
  data. First\, we discuss the use of Bayesian informative priors based on 
 relevant external data to increase the power and precision of such trials 
 and propose a range of operating characteristics that can be useful to eva
 luate and compare designs.  Here we present various prior and posterior su
 mmaries of the available historical\, current and total evidence which can
  be used to help sponsors and regulators assess the strength of the prior 
 assumptions and the extent to which the current trial data can influence t
 he final posterior inference. We illustrate these methods through some rec
 ent case studies covering both paediatric settings borrowing efficacy data
  from adults\, and in confirmatory trials in adults borrowing historical c
 ontrols. Second\, we explore the use of genetic annotation for design and 
 analysis of biomarker-rich “large p\, small n” early phase oncology cl
 inical trials. While the primary goal of these trials is to assess safety 
 and preliminary clinical efficacy in a possibly heterogeneous patient popu
 lation\, a key secondary objective is to identify prognostic and predictiv
 e markers for design adaptation and for developing companion diagnostics. 
 Here we focus on the analysis of associations between baseline gene expres
 sion and response to therapy\, comparing the operational characteristics o
 f bottom-up and top-down methods. While bottom-up methods use gene annotat
 ions for interpretation of the analysis results\, top-down methods incorpo
 rate these background data within the structure of a hierarchical model li
 kelihood for estimating pathway-level associations to clinical response. W
 e show that\, while top-down methods benefit from the advantages of parsim
 onious parametric modelling\, they are not less exposed than bottom-up met
 hods to a reliance on a knowledge basis that is both incomplete and in con
 stant evolution.
LOCATION:Statistical Laboratory\, Centre for Mathematical Sciences\, Wilbe
 rforce Road\, Cambridge\, CB3 0WB
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