title: Individual patient data meta-analysis of survival data using Poisson regression models.
abstract: BACKGROUND
  An Individual Patient Data (IPD) meta-analysis is often considered the gold-standard for synthesising survival data from clinical trials. An IPD meta-analysis can be achieved by either a two-stage or a one-stage approach, depending on whether the trials are analysed separately or simultaneously. A range of one-stage hierarchical Cox models have been previously proposed, but these are known to be computationally intensive and are not currently available in all standard statistical software. We describe an alternative approach using Poisson based Generalised Linear Models (GLMs).
  
  
  METHODS
  We illustrate, through application and simulation, the Poisson approach both classically and in a Bayesian framework, in two-stage and one-stage approaches. We outline the benefits of our one-stage approach through extension to modelling treatment-covariate interactions and non-proportional hazards. Ten trials of hypertension treatment, with all-cause death the outcome of interest, are used to apply and assess the approach.
  
  
  RESULTS
  We show that the Poisson approach obtains almost identical estimates to the Cox model, is additionally computationally efficient and directly estimates the baseline hazard. Some downward bias is observed in classical estimates of the heterogeneity in the treatment effect, with improved performance from the Bayesian approach.
  
  
  CONCLUSIONS
  Our approach provides a highly flexible and computationally efficient framework, available in all standard statistical software, to the investigation of not only heterogeneity, but the presence of non-proportional hazards and treatment effect modifiers.
authors.affiliation: Centre for Biostatistics and Genetic Epidemiology, Department of Health Sciences, University of Leicester, Adrian Building, University Road, Leicester LE1 7RH, UK.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Crowther
authors.forename: Richard D
authors.initials: RD
authors.lastname: Riley
authors.forename: Jan A
authors.initials: JA
authors.lastname: Staessen
authors.forename: Jiguang
authors.initials: J
authors.lastname: Wang
authors.forename: Francois
authors.initials: F
authors.lastname: Gueyffier
authors.forename: Paul C
authors.initials: PC
authors.lastname: Lambert
mesh_terms: D000704:Analysis of Variance; D001499:Bayes Theorem; D002423:Cause of Death; D003625:Data Collection / Q000706:statistics & numerical data*; D005260:Female; D006305:Health Status Indicators; D006801:Humans; D006973:Hypertension / Q000401:mortality / Q000628:therapy; D057194:Intention to Treat Analysis; D016014:Linear Models; D008297:Male; D015201:Meta-Analysis as Topic; D016012:Poisson Distribution; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic; D012044:Regression Analysis; D016019:Survival Analysis; D016896:Treatment Outcome