A Bayesian hierarchical approach for multiple outcomes in routinely collected healthcare data

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Clinical trials are the standard approach for evaluating new treatments but may lack the power to assess rare outcomes. Trial results are also necessarily restricted to the population considered in the study. The availability of routinely collected healthcare data provides a source of information on the performance of treatments beyond that offered by clinical trials, but the analysis of this type of data presents a number of challenges. Hierarchical methods which take advantage of known relationships between clinical outcomes, while accounting for bias, may be a suitable statistical approach for the analysis of this data. A study of direct oral anti-coagulants in Scotland is discussed and used to motivate a modelling approach. A Bayesian hierarchical model, which allows a stratification of the population into clusters with similar characteristics, is proposed and applied to the direct oral anti-coagulant study data. A simulation study is used to assess its performance in terms of outcome detection and error rates.
Original languageEnglish
Number of pages16
JournalStatistics in Medicine
Early online date7 May 2020
Publication statusE-pub ahead of print - 7 May 2020



  • Bayesian hierarchy
  • multiple outcomes
  • safety outcomes
  • observational study
  • direct oral anticoagulants

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