Near-Real-Time Monitoring of New Drugs: An Application Comparing Prasugrel Versus Clopidogrel

Gagne, Joshua J.; Rassen, Jeremy A.; Choudhry, Niteesh K.; Bohn, Rhonda L.; Patrick, Amanda R.; Sridhar, Gayathri; Daniel, Gregory W.; Li, Jun; Schneeweiß, Sebastian

doi:10.1007/s40264-014-0136-0

Cited by 19 publications

(37 citation statements)

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“…Comparative effectiveness and safety assessments of newly marketed medical products in routine care are now widely conducted using large databases, including administrative claims databases and electronic health record data [ 1 – 3 ]. The numbers of potential confounders available for studies in these databases are large, and the utility of standard multivariable regression approaches can be limited especially when the number of study outcomes is small [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Dimension reduction and shrinkage methods for high dimensional disease risk scores in historical data

Kumamaru

Schneeweiß

Glynn

et al. 2016

Emerg Themes Epidemiol

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BackgroundMultivariable confounder adjustment in comparative studies of newly marketed drugs can be limited by small numbers of exposed patients and even fewer outcomes. Disease risk scores (DRSs) developed in historical comparator drug users before the new drug entered the market may improve adjustment. However, in a high dimensional data setting, empirical selection of hundreds of potential confounders and modeling of DRS even in the historical cohort can lead to over-fitting and reduced predictive performance in the study cohort. We propose the use of combinations of dimension reduction and shrinkage methods to overcome this problem, and compared the performances of these modeling strategies for implementing high dimensional (hd) DRSs from historical data in two empirical study examples of newly marketed drugs versus comparator drugs after the new drugs’ market entry—dabigatran versus warfarin for the outcome of major hemorrhagic events and cyclooxygenase-2 inhibitor (coxibs) versus nonselective non-steroidal anti-inflammatory drugs (nsNSAIDs) for gastrointestinal bleeds.ResultsHistorical hdDRSs that included predefined and empirical outcome predictors with dimension reduction (principal component analysis; PCA) and shrinkage (lasso and ridge regression) approaches had higher c-statistics (0.66 for the PCA model, 0.64 for the PCA + ridge and 0.65 for the PCA + lasso models in the warfarin users) than an unreduced model (c-statistic, 0.54) in the dabigatran example. The odds ratio (OR) from PCA + lasso hdDRS-stratification [OR, 0.64; 95 % confidence interval (CI) 0.46–0.90] was closer to the benchmark estimate (0.93) from a randomized trial than the model without empirical predictors (OR, 0.58; 95 % CI 0.41–0.81). In the coxibs example, c-statistics of the hdDRSs in the nsNSAID initiators were 0.66 for the PCA model, 0.67 for the PCA + ridge model, and 0.67 for the PCA + lasso model; these were higher than for the unreduced model (c-statistic, 0.45), and comparable to the demographics + risk score model (c-statistic, 0.67).ConclusionshdDRSs using historical data with dimension reduction and shrinkage was feasible, and improved confounding adjustment in two studies of newly marketed medications.Electronic supplementary materialThe online version of this article (doi:10.1186/s12982-016-0047-x) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Dimension reduction and shrinkage methods for high dimensional disease risk scores in historical data

Kumamaru

Schneeweiß

Glynn

et al. 2016

Emerg Themes Epidemiol

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“…There is increasing interest in the ability to conduct near real‐time medical product safety surveillance using observational data sources . Data presented here on real‐world uptake of NMEs in the MSDD provide essential information for determining how early in the post‐approval setting potential safety signals might be detected.…”

Section: Discussionmentioning

confidence: 99%

Uptake of new drugs in the early post-approval period in the Mini-Sentinel distributed database

Mott

Toh

Gagne

et al. 2016

Pharmacoepidemiol Drug Saf

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“…The PS adjustment precursor program was also applied to a true prospective safety monitoring scenario in which data accumulated in real time to evaluate the risk of hemorrhagic and ischemic events in patients taking prasugrel versus clopidogrel during the first two years of prasugrel's market availability in the US [40•]. Using the prototype, patients were matched on hd-PS and followed for outcomes using dynamic data that were updated on a bi-monthly basis.…”

Section: Propensity Score Adjusted Analysesmentioning

confidence: 99%

Development and Application of Two Semi-Automated Tools for Targeted Medical Product Surveillance in a Distributed Data Network

et al. 2017

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Purpose of Review An important component of the Food and Drug Administration’s Sentinel Initiative is the active post-market risk identification and analysis (ARIA) system, which utilizes semi-automated, parameterized computer programs to implement propensity-score adjusted and self-controlled risk interval designs to conduct targeted surveillance of medical products in the Sentinel Distributed Database. In this manuscript, we review literature relevant to the development of these programs and describe their application within the Sentinel Initiative. Recent Findings These quality-checked and publicly available tools have been successfully used to conduct rapid, replicable, and targeted safety analyses of several medical products. In addition to speed and reproducibility, use of semi-automated tools allows investigators to focus on decisions regarding key methodological parameters. We also identified challenges associated with the use of these methods in distributed and prospective datasets like the Sentinel Distributed Database, namely uncertainty regarding the optimal approach to estimating propensity scores in dynamic data among data partners of heterogeneous size. Summary Future research should focus on the methodological challenges raised by these applications as well as developing new modular programs for targeted surveillance of medical products.

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Near-Real-Time Monitoring of New Drugs: An Application Comparing Prasugrel Versus Clopidogrel

Abstract: Near-real-time monitoring was feasible and, in contrast to the key pre-marketing trial that demonstrated the efficacy of prasugrel, did not suggest that prasugrel compared with clopidogrel was associated with an increased risk of gastrointestinal and intracranial bleeding.

Cited by 19 publications

References 30 publications

Dimension reduction and shrinkage methods for high dimensional disease risk scores in historical data

Dimension reduction and shrinkage methods for high dimensional disease risk scores in historical data

Uptake of new drugs in the early post-approval period in the Mini-Sentinel distributed database

Development and Application of Two Semi-Automated Tools for Targeted Medical Product Surveillance in a Distributed Data Network

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