Premium
Assessing the prior event rate ratio method via probabilistic bias analysis on a Bayesian network
Author(s) -
Thommes Edward W.,
Mahmud Salaheddin M.,
YoungXu Yig,
Snider Julia Thornton,
Aalst Robertus,
Lee Jason K.H.,
Halchenko Yuliya,
Russo Ellyn,
Chit Ayman
Publication year - 2019
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.8435
Subject(s) - confounding , observational study , bayesian probability , econometrics , robustness (evolution) , statistics , computer science , population , probabilistic logic , medicine , mathematics , environmental health , biochemistry , chemistry , gene
Background: Unmeasured confounders are commonplace in observational studies conducted using real‐world data. Prior event rate ratio (PERR) adjustment is a technique shown to perform well in addressing such confounding. However, it has been demonstrated that, in some circumstances, the PERR method actually increases rather than decreases bias. In this work, we seek to better understand the robustness of PERR adjustment. Methods: We begin with a Bayesian network representation of a generalized observational study, which is subject to unmeasured confounding. Previous work evaluating PERR performance used Monte Carlo simulation to calculate joint probabilities of interest within the study population. Here, we instead use a Bayesian networks framework. Results: Using this streamlined analytic approach, we are able to conduct probabilistic bias analysis (PBA) using large numbers of combinations of parameters and thus obtain a comprehensive picture of PERR performance. We apply our methodology to a recent study that used the PERR in evaluating elderly‐specific high‐dose (HD) influenza vaccine in the US Veterans Affairs population. That study obtained an HD relative effectiveness of 25% (95% CI: 2%‐43%) against influenza‐ and pneumonia‐associated hospitalization, relative to standard‐dose influenza vaccine. In this instance, we find that the PERR‐adjusted result is more like to underestimate rather than to overestimate the relative effectiveness of the intervention. Conclusions: Although the PERR is a powerful tool for mitigating the effects of unmeasured confounders, it is not infallible. Here, we develop some general guidance for when a PERR approach is appropriate and when PBA is a safer option.