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

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          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.

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          Effectiveness of influenza vaccine in the community-dwelling elderly.

          Reliable estimates of the effectiveness of influenza vaccine among persons 65 years of age and older are important for informed vaccination policies and programs. Short-term studies may provide misleading pictures of long-term benefits, and residual confounding may have biased past results. This study examined the effectiveness of influenza vaccine in seniors over the long term while addressing potential bias and residual confounding in the results. Data were pooled from 18 cohorts of community-dwelling elderly members of one U.S. health maintenance organization (HMO) for 1990-1991 through 1999-2000 and of two other HMOs for 1996-1997 through 1999-2000. Logistic regression was used to estimate the effectiveness of the vaccine for the prevention of hospitalization for pneumonia or influenza and death after adjustment for important covariates. Additional analyses explored for evidence of bias and the potential effect of residual confounding. There were 713,872 person-seasons of observation. Most high-risk medical conditions that were measured were more prevalent among vaccinated than among unvaccinated persons. Vaccination was associated with a 27% reduction in the risk of hospitalization for pneumonia or influenza (adjusted odds ratio, 0.73; 95% confidence interval [CI], 0.68 to 0.77) and a 48% reduction in the risk of death (adjusted odds ratio, 0.52; 95% CI, 0.50 to 0.55). Estimates were generally stable across age and risk subgroups. In the sensitivity analyses, we modeled the effect of a hypothetical unmeasured confounder that would have caused overestimation of vaccine effectiveness in the main analysis; vaccination was still associated with statistically significant--though lower--reductions in the risks of both hospitalization and death. During 10 seasons, influenza vaccination was associated with significant reductions in the risk of hospitalization for pneumonia or influenza and in the risk of death among community-dwelling elderly persons. Vaccine delivery to this high-priority group should be improved. Copyright 2007 Massachusetts Medical Society.
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            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 the UK general practice research database (GPRD) were used to replicate previously performed randomised controlled trials, to the extent that was feasible aside from randomisation. Studies Six published randomised controlled trials. Main outcome measure Cardiovascular outcomes analysed by hazard ratios calculated with standard biostatistical methods and a new analytical technique, prior event rate ratio (PERR) adjustment. Results In nine of 17 outcome comparisons, there were no significant differences between results of randomised controlled trials and database studies analysed using standard biostatistical methods or PERR analysis. In eight comparisons, Cox adjusted hazard ratios in the database differed significantly from the results of the randomised controlled trials, suggesting unmeasured confounding. In seven of these eight, PERR adjusted hazard ratios differed significantly from Cox adjusted hazard ratios, whereas in five they didn’t differ significantly, and in three were more similar to the hazard ratio from the randomised controlled trial, yielding PERR results more similar to the randomised controlled trial than Cox (P<0.05). Conclusions Although observational studies using databases are subject to unmeasured confounding, our new analytical technique (PERR), applied here to cardiovascular outcomes, worked well to identify and reduce the effects of such confounding. These results suggest that electronic medical record databases can be useful to investigate therapeutic effectiveness.
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              Performance of propensity score calibration--a simulation study.

              Confounding can be a major source of bias in nonexperimental research. The authors recently introduced propensity score calibration (PSC), which combines propensity scores and regression calibration to address confounding by variables unobserved in the main study by using variables observed in a validation study. Here, the authors assess the performance of PSC using simulations in settings with and without violation of the key assumption of PSC: that the error-prone propensity score estimated in the main study is a surrogate for the gold-standard propensity score (i.e., it contains no additional information on the outcome). The assumption can be assessed if data on the outcome are available in the validation study. If data are simulated allowing for surrogacy to be violated, results depend largely on the extent of violation. If surrogacy holds, PSC leads to bias reduction between 32% and 106% (>100% representing overcorrection). If surrogacy is violated, PSC can lead to an increase in bias. Surrogacy is violated when the direction of confounding of the exposure-disease association caused by the unobserved variable(s) differs from that of the confounding due to observed variables. When surrogacy holds, PSC is a useful approach to adjust for unmeasured confounding using validation data.
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                Author and article information

                Contributors
                w.e.henley@exeter.ac.uk
                Journal
                Stat Med
                Stat Med
                10.1002/(ISSN)1097-0258
                SIM
                Statistics in Medicine
                John Wiley and Sons Inc. (Hoboken )
                0277-6715
                1097-0258
                01 August 2016
                10 December 2016
                : 35
                : 28 ( doiID: 10.1002/sim.v35.28 )
                : 5149-5169
                Affiliations
                [ 1 ] Department of Mathematics and Information SciencesNorthumbria University Newcastle upon Tyne NE2 1XEU.K.
                [ 2 ] Health Statistics Group, Institute of Health ResearchUniversity of Exeter Medical School Exeter EX1 2LUU.K.
                Author notes
                [*] [* ] Correspondence to: William Edward Henley, Health Statistics Group, Institute of Health Research, University of Exeter Medical School, Exeter EX1 2LU, U.K.

                E‐mail: w.e.henley@ 123456exeter.ac.uk

                Author information
                http://orcid.org/0000-0001-6273-2619
                Article
                SIM7051 sim.7051
                10.1002/sim.7051
                5111612
                27477530
                64291ea8-7dfb-487c-a835-1902fc489a7d
                © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd

                This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 08 October 2014
                : 23 June 2016
                : 01 July 2016
                Page count
                Figures: 7, Tables: 2, Pages: 21, Words: 15704
                Funding
                Funded by: Medical Research Council
                Award ID: G0902158
                Funded by: National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) for the South West Peninsula
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                sim7051
                sim7051-hdr-0001
                10 December 2016
                Converter:WILEY_ML3GV2_TO_NLMPMC version:4.9.7 mode:remove_FC converted:16.11.2016

                Biostatistics
                prior event rate ratio,pairwise cox model,unmeasured confounding,observational study,treatment effect

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