Abstract
Observational studies provide a rich source of information for assessing effectiveness of treatment interventions in many situations where it is not ethical or practical to perform randomized controlled trials. However, such studies are prone to bias from hidden (unmeasured) confounding. A promising approach to identifying and reducing the impact of unmeasured confounding is prior event rate ratio (PERR) adjustment, a quasi‐experimental analytic method proposed in the context of electronic medical record database studies. In this paper, we present a statistical framework for using a pairwise approach to PERR adjustment that removes bias inherent in the original PERR method. A flexible pairwise Cox likelihood function is derived and used to demonstrate the consistency of the simple and convenient alternative PERR (PERR‐ALT) estimator. We show how to estimate standard errors and confidence intervals for treatment effect estimates based on the observed information and provide R code to illustrate how to implement the method. Assumptions required for the pairwise approach (as well as PERR) are clarified, and the consequences of model misspecification are explored. Our results confirm the need for researchers to consider carefully the suitability of the method in the context of each problem. Extensions of the pairwise likelihood to more complex designs involving time‐varying covariates or more than two periods are considered. We illustrate the application of the method using data from a longitudinal cohort study of enzyme replacement therapy for lysosomal storage disorders. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
Highlights
Observational studies, based on routinely collected patient data or data from population-based cohorts, offer a rich source of information for evaluating real-world effectiveness of medical treatments [1]
Yu et al [6] present a simple and convenient formula for prior event rate ratio (PERR)-ALT adjustment. We extend their approach by deriving a flexible pairwise Cox likelihood function and using this to show that the PERR-ALT method is consistent, under relevant assumptions
Building on the work of Tannen and colleagues, we set out a general framework for quasi-experimental analysis using the PERR approach and derived a flexible pairwise Cox likelihood function that can be used to estimate unbiased treatment estimates, under appropriate assumptions
Summary
Observational studies, based on routinely collected patient data or data from population-based cohorts, offer a rich source of information for evaluating real-world effectiveness of medical treatments [1]. Similar definitions apply to the study start point, study period, and the variables Ts∗, Ts+, Ts, Δs for the study event, censoring and observed times, and censoring indicator, respectively Note that it is generally not appropriate when using the pairwise and PERR methods to measure Ts from the time of receiving treatment as this is often not the time when an individual starts to be under the risk of a study event (see later). The time origins are defined as the start of the 2013 and 2014 influenza seasons because these are the points at which patients first become at risk for a prior or study event, respectively.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
