2014
DOI: 10.1002/pds.3724
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Performance of prior event rate ratio adjustment method in pharmacoepidemiology: a simulation study

Abstract: 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.

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Cited by 27 publications
(36 citation statements)
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References 20 publications
(87 reference statements)
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“…However, events were relatively few compared to other outcomes, in particular pre-treatment in those exposed to antipsychotic medication (especially atypicals). The higher risk associated with atypical compared to conventional antipsychotic medication may be explained by clinicians selecting one drug over another in ‘at risk’ patients; when prior events influence the probability of drug use, then bias may occur when using the PERR method [30]. …”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…However, events were relatively few compared to other outcomes, in particular pre-treatment in those exposed to antipsychotic medication (especially atypicals). The higher risk associated with atypical compared to conventional antipsychotic medication may be explained by clinicians selecting one drug over another in ‘at risk’ patients; when prior events influence the probability of drug use, then bias may occur when using the PERR method [30]. …”
Section: Discussionmentioning
confidence: 99%
“…The method assumes that the confounding effects are constant across the prior and post exposure periods and that there is no confounder by treatment interactions [30, 31]. The PERR cannot be applied for terminal events and so was used to examine acute cardiac events, venous thromboembolism (DVT and PE), stroke and hip fracture—but not mortality.…”
Section: Methodsmentioning
confidence: 99%
“…[29][30][31] In the adjusted Cox regression analysis, we considered the first occurrence of community acquired pneumonia after the index date. We used Cox proportional hazards modelling to estimate the hazard ratio for pneumonia in people receiving PPIs, adjusted for potential confounders, with 95% confidence intervals.…”
Section: Cohort Studymentioning
confidence: 99%
“…[29][30][31] This method requires that both exposed and unexposed cohorts did not receive the study drug before the index date-that is, the date at which the exposed cohort first received the prescription and the matched date for the unexposed patients. The method relies on a before and after design and is based on the assumption that the differences in outcomes between exposed and unexposed patients before receiving the treatment reflect the combined effect of confounders independently of any effect of the study drug.…”
Section: Cohort Studymentioning
confidence: 99%
See 1 more Smart Citation