Prior Event Rate Ratio Research Articles

Community acquired pneumonia incidence before and after proton pump inhibitor prescription: population based study

Objective To examine the risk of community acquired pneumonia before and after prescription of proton pump inhibitor (PPI) and assess whether unmeasured confounding explains this association.Design Cohort study and self...

Prior event rate ratio adjustment for hidden confounding in observational studies of treatment effectiveness: a pairwise Cox likelihood approach.

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.

Methods to control for unmeasured confounding in pharmacoepidemiology: an overview.

Background Unmeasured confounding is one of the principal problems in pharmacoepidemiologic studies. Several methods have been proposed to detect or control for unmeasured confounding either at the study design phase or the data analysis phase. Aim of the Review To provide an overview of commonly used methods to detect or control for unmeasured confounding and to provide recommendations for proper application in pharmacoepidemiology. Methods/Results Methods to control for unmeasured confounding in the design phase of a study are case only designs (e.g., case-crossover, case-time control, self-controlled case series) and the prior event rate ratio adjustment method. Methods that can be applied in the data analysis phase include, negative control method, perturbation variable method, instrumental variable methods, sensitivity analysis, and ecological analysis. A separate group of methods are those in which additional information on confounders is collected from a substudy. The latter group includes external adjustment, propensity score calibration, two-stage sampling, and multiple imputation. Conclusion As the performance and application of the methods to handle unmeasured confounding may differ across studies and across databases, we stress the importance of using both statistical evidence and substantial clinical knowledge for interpretation of the study results.

Performance of prior event rate ratio adjustment method in pharmacoepidemiology: a simulation study.

Prior event rate ratio (PERR) adjustment method has been proposed to control for unmeasured confounding. We aimed to assess the performance of the PERR method in realistic pharmacoepidemiological settings. Simulation studies were performed with varying effects of prior events on the probability of subsequent exposure and post-events, incidence rates, effects of confounders, and rate of mortality/dropout. Exposure effects were estimated using conventional rate ratio (RR) and PERR adjustment method (i.e. ratio of RR post-exposure initiation and RR prior to initiation of exposure). In the presence of unmeasured confounding, both conventional and the PERR method may yield biased estimates, but PERR estimates appear generally less biased estimates than the conventional method. However, when prior events strongly influence the probability of subsequent exposure, the exposure effect from the PERR method was more biased than the conventional method. For instance, when the effect of prior events on the exposure was RR = 1.60, the effect estimate from the PERR method was RR = 1.13 and from the conventional method was RR = 2.48 (true exposure effect, RR = 2). In all settings, the variation of the estimates was larger for the PERR method than for the conventional method. The PERR adjustment method can be applied to reduce bias as a result of unmeasured confounding. However, only in particular situations, it can completely remove the bias as a result of unmeasured confounding. When applying this method, theoretical justification using available clinical knowledge for assumptions of the PERR method should be provided.

Incidence of Campylobacter and Salmonella Infections Following First Prescription for PPI: A Cohort Study Using Routine Data

To examine the incidence of Campylobacter and Salmonella infection in patients prescribed proton pump inhibitors (PPIs) compared with controls. Retrospective cohort study using anonymous general practitioner (GP) data. Anonymised individual-level records from the Secure Anonymised Information Linkage (SAIL) system between 1990 and 2010 in Wales were selected. Data were available from 1,913,925 individuals including 358,938 prescribed a PPI. The main outcome measures examined included incidence of Campylobacter or Salmonella infection following a prescription for PPI. The rate of Campylobacter and Salmonella infections was already at 3.1-6.9 times that of non-PPI patients even before PPI prescription. The PPI group had an increased hazard rate of infection (after prescription for PPI) of 1.46 for Campylobacter and 1.2 for Salmonella, compared with baseline. However, the non-PPI patients also had an increased hazard ratio with time. In fact, the ratio of events in the PPI group compared with the non-PPI group using the prior event rate ratio was 1.17 (95% CI 0.74-1.61) for Campylobacter and 1.00 (0.5-1.5) for Salmonella. People who go on to be prescribed PPIs have a greater underlying risk of gastrointestinal (GI) infection beforehand and they have a higher prevalence of risk factors before PPI prescription. The rate of diagnosis of infection is increasing with time regardless of PPI use, and there is no evidence that PPI is associated with an increase in diagnosed GI infection. It is likely that factors associated with the demographic profile of the patient are the main contributors to increased rate of GI infection for patients prescribed PPIs.

A new “Comparative Effectiveness” assessment strategy using the THIN database: comparison of the cardiac complications of pioglitazone and rosiglitazone

Examine feasibility of a new strategy to perform Electronic Medical Record database valid Comparative Effectiveness Research (CER), using determination of whether rosiglitazone (ROS) treatment increases Acute myocardial infarction (MI) in comparison to pioglitazone (PIO) as a model question. Using the UK The Health Improvement Network Database, a retrospective cohort design replicated the proactive RCT of diabetics with ischemic cardiovascular disease (CVD). Replication studies using PIO or ROS, as well as expanded studies of subjects not requiring CVD, were performed. MI assessment used multiple analytics comparing ROS and PIO exposed patients including: unexposed subjects, the proactive RCT, and directly between ROS to PIO exposed cohorts. PIO replication studies did not affect MI [HR 0.88 (0.49 to 1.42)], but ROS increased MI, with prior event rate ratio (PERR) adjusted HR (which overcomes unmeasured confounding) results of: [HR 1.31 (0.94 to 1.74)] versus proactive RCT [HR 0.83 (0.65 to 1.06)] (p = 0.02). Direct ROS to PIO exposed cohort comparisons yielded PERR adj HR of 1.55 (0.98 to 2.65). By contrast, expanded studies showed no differences between ROS and PIO exposure. These results provide new insight regarding the effects of ROS and PIO on MI. In a population with established ischemic CVD, ROS increased MI in contrast to PIO; whereas in an unselected population, ROS and PIO have reasonably comparable effects. Most importantly, this study demonstrates the feasibility and advantages of a new strategy to perform reliable "CER" using an EMR database.

Prior event rate ratio adjustment: numerical studies of a statistical method to address unrecognized confounding in observational studies

The purpose of this study was to evaluate a statistical method, prior event rate ratio (PERR) adjustment, and an alternative, PERR-ALT, both of which have the potential to overcome "unmeasured confounding," both analytically and via simulation. Formulae were derived for the target estimates of both PERR methods, which were compared with results from simulations to ensure their validity. In addition to the theoretical insights gained, relative biases of both PERR methods for estimating exposure effects were also investigated via simulation studies and compared empirically with electronic medical record database study results. Theoretical derivations closely matched simulated results. In simulation studies, both PERR methods significantly reduce bias from unmeasured confounding compared with the standard Cox model. When there is no interaction between unmeasured confounders and time intervals, the estimate from PERR-ALT is unbiased, whereas the estimate from PERR has well-controlled relative bias. When interactions exist, relative biases tend to increase but not greatly, especially when the exposure effect is relatively large in comparison with the interaction effects. When the event rate is low and the sample size is limited, PERR is more computationally stable than PERR-ALT. In empiric study comparisons with randomized controlled trials, both PERR methods show potential to reduce bias from the standard Cox model similarly when unmeasured confounding is present. Extensive simulation studies and theoretical derivation show that PERR-based methods may reduce bias from unmeasured confounders when the exposure effect is relatively large in comparison with confounder-exposure interaction. The rare study event situation warrants further investigation.

Methods for developing and analyzing clinically rich data for patient‐centered outcomes research: an overview

Methods for developing and analyzing clinically rich data for patient‐centered outcomes research: an overview

Use of primary care electronic medical record database in drug efficacy research on cardiovascular outcomes: comparison of database and randomised controlled trial findings

Objectives To determine whether observational studies that use an electronic medical record database can provide valid results of therapeutic effectiveness and to develop new methods to enhance validity.Design Data from...

Replicated studies of two randomized trials of angiotensin‐ converting enzyme inhibitors: further empiric validation of the ‘prior event rate ratio’ to adjust for unmeasured confounding by indication

Using data from the United Kingdom General Practice Research Database (GPRD) randomized controlled trials (RCTs) were replicated to determine whether identifiable study characteristics and/or analytic methods influence the validity of observational studies. This study reports GPRD replications of 2 RCTs that investigated whether angiotensin-converting enzyme inhibitors (ACEIs) improve cardiovascular outcomes in patients without congestive heart failure at high risk for cardiovascular disease (heart outcomes prevention evaluation (HOPE) and EUROPA). The GPRD studies replicated to the extent feasible all aspects of these RCTs except for randomization. With adjustment for confounders using conventional biostatistical techniques, both GPRD studies exhibited results divergent from the RCTs. Myocardial infarction, stroke, congestive heart failure, and coronary revascularization were increased in the Exposed group of both GPRD studies; whereas these outcomes either were decreased or unchanged by ACEI therapy in both RCTs. The results also were analyzed with a new method that appears to adjust for both identified and unmeasured confounders. With this methodology that employs the ratio of event rates between the Exposed and Unexposed cohorts prior to study start time to adjust the study hazard ratio (HR), the GPRD results for myocardial infarction, stroke, and coronary revascularization were largely similar to those found in the two RCTs. This study provides additional empiric evidence suggesting that this new analytic methodology, 'prior events rate ratio (PERR)' adjustment, is a promising solution for the vexing problem of 'unmeasured confounding' in observational studies. Additional statistical simulations are needed to fully appreciate the applicability and limitations of this method.