TY - JOUR

T1 - Assessing the prior event rate ratio method via probabilistic bias analysis on a Bayesian network

AU - Thommes, Edward W.

AU - Mahmud, Salaheddin M.

AU - Young-Xu, Yinong

AU - Snider, Julia Thornton

AU - van Aalst, Robertus

AU - Lee, Jason K. H.

AU - Halchenko, Yuliya

AU - Russo, Ellyn

AU - Chit, Ayman

PY - 2019/12/1

Y1 - 2019/12/1

N2 - 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.

AB - 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.

KW - Bayesian networks

KW - observational studies

KW - probabilistic bias analysis

KW - prior event rate ratio (PERR)

KW - unmeasured confounders

KW - DOSE INFLUENZA VACCINE

KW - ADJUSTMENT

KW - HOSPITALIZATIONS

KW - EFFICACY

KW - FRAILTY

U2 - 10.1002/sim.8435

DO - 10.1002/sim.8435

M3 - Article

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

ER -