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A Bayesian hierarchical approach for multiple outcomes in routinely collected healthcare data
Author(s) -
Carragher Raymond,
Mueller Tanja,
Bennie Marion,
Robertson Chris
Publication year - 2020
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8563
Subject(s) - bayesian probability , bayesian hierarchical modeling , statistical power , computer science , clinical trial , hierarchical database model , population , health care , bayes' theorem , type i and type ii errors , econometrics , data mining , statistics , medicine , artificial intelligence , mathematics , environmental health , pathology , economics , economic growth
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 anticoagulants in Scotland is discussed and used to motivate a modeling 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 anticoagulant study data. A simulation study is used to assess its performance in terms of outcome detection and error rates.