Statistical Power for Postlicensure Medical Product Safety Data-Mining

Maro, Judith C.; Nguyen, Michael; Dashevsky, Inna; Baker, Meghan A.; Kulldorff, Martin

doi:10.13063/2327-9214.1264

Cited by 4 publications

(6 citation statements)

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“…We employed propensity score stratification to control for confounding in the conditional and unconditional Poisson TreeScan analyses. The Poisson version of the tree‐based scan statistic has been described in detail previously and is briefly summarized in the supplementary materials 1,39 . The composite null hypothesis is that the observed count of each exposed outcome follows the expected count as generated from a Poisson distribution.…”

Section: Methodsmentioning

confidence: 99%

Novel methods for pregnancy drug safety surveillance in the FDA Sentinel System

Suarez

Nguyen

Zhang

et al. 2022

Pharmacoepidemiology and Drug

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Purpose: It is a priority of the US Food and Drug Administration (FDA) to monitor the safety of medications used during pregnancy. Pregnancy exposure registries and cohort studies utilizing electronic health record data are primary sources of information but are limited by small sample sizes and limited outcome assessment. TreeS-can™, a statistical data mining tool, can be applied within the FDA Sentinel System to simultaneously identify multiple potential adverse neonatal and infant outcomes after maternal medication exposure. Methods: We implemented TreeScan using the Sentinel analytic tools in a cohort of linked live birth deliveries and infants nested in the IBM MarketScan ® Research Database. As a case study, we compared first trimester fluoroquinolone use and cephalosporin use. We used the Bernoulli and Poisson TreeScan statistics with compatible propensity score-based study designs for confounding control (matching and stratification) and used multiple propensity score models with various strategies for confounding control to inform best practices. We developed a hierarchical outcome tree including major congenital malformations and outcomes of gestational length and birth weight. Results: A total of 1791 fluoroquinolone-exposed and 8739 cephalosporin-exposed mother-infant pairs were eligible for analysis. Both TreeScan analysis methods resulted in single alerts that were deemed to be due to uncontrolled confounding or otherwise not warranting follow-up. Conclusions: In this implementation of TreeScan using Sentinel analytic tools, we did not observe any new safety signals for fluoroquinolone use in the first trimester.TreeScan, with tailored or high-dimensional propensity scores for confounding control, is a valuable tool in addition to current safety surveillance methods for medications used during pregnancy.

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Section: Methodsmentioning

confidence: 99%

Novel methods for pregnancy drug safety surveillance in the FDA Sentinel System

Suarez

Nguyen

Zhang

et al. 2022

Pharmacoepidemiology and Drug

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show abstract

“…Prior simulation work has demonstrated that a conditional Poisson model is generally preferred as it adjusts for large volumes of outcomes related to healthcare utilization practices that are unlikely to be attributable to exposure. 14 For example, infants may be monitored more carefully for all outcomes after birth due to normal healthcare patterns. Prior simulations for the Bernoulli model demonstrated no appreciable difference in power under the two models and the improved confounding control under a 1:1 matched design eliminated the need for further adjustment.…”

Section: Methodsmentioning

confidence: 99%

“…5,7,[10][11][12] The statistical power of TreeScan has been demonstrated previously; however, the scenarios were based on the general adolescent and adult population. 13,14 Power is dependent on the population sample size, the underlying incidence of the outcomes of interest, the effect size, and the size of the outcome tree; therefore, these previously demonstrated scenarios do not apply to a population of mother-infant pairs. To address this gap, we completed multiple simulation analyses to describe the statistical power of TreeScan in the setting of perinatal medication exposures and infant outcomes.…”

mentioning

confidence: 99%

Monitoring Drug Safety in Pregnancy with Scan Statistics: A Comparison of Two Study Designs

et al. 2022

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Background: Traditional surveillance of adverse infant outcomes following maternal medication exposures relies on pregnancy exposure registries, which are often underpowered. We characterize the statistical power of TreeScan, a data mining tool, to identify potential signals in the setting of perinatal medication exposures and infant outcomes. Methods: We used empirical data to inform background incidence of major congenital malformations and other birth conditions. Statistical power was calculated using two probability models compatible with TreeScan, Bernoulli and Poisson, while varying the sample size, magnitude of the risk increase, and incidence of a specified outcome. We also simulated larger referent to exposure matching ratios when using the Bernoulli model in the setting of fixed N:1 propensity score matching. Finally, we assessed the impact of outcome misclassification on power. Results:The Poisson model demonstrated greater power to detect signals than the Bernoulli model across all scenarios and suggested a sample size of 4,000 exposed pregnancies is needed to detect a twofold increase in risk of a common outcome (approximately 8 per 1,000) with 85% power. Increasing the fixed matching ratio with the Bernoulli model did not reliably increase power. An outcome definition with high sensitivity is expected to have somewhat greater power to detect signals than an outcome definition with high positive predictive value. Conclusions: Use of the Poisson model with an outcome definition that prioritizes sensitivity may be optimal for signal detection. TreeScan is a viable method for surveillance of adverse infant outcomes following maternal medication use.

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“…When screening potential multiple outcomes for signal identification, it is critical to control the rate of false positives. TreeScan generates multiplicity‐adjusted p ‐values that accurately reflect the type I error rate in the absence of confounding 10,16,17,20‐23 . That is, if there is not a single outcome with an excess risk, we have a 95% probability of finding zero signals.…”

Section: Methodsmentioning

confidence: 99%

A novel data mining application to detect safety signals for newly approved medications in routine care of patients with diabetes

Fralick

Kulldorff

Redelmeier

et al. 2021

Endocrino Diabet & Metabol

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Background: Clinical trials are often underpowered to detect serious but rare adverse events of a new medication. We applied a novel data mining tool to detect potential adverse events of canagliflozin, the first sodium glucose co-transporter 2 (SGLT2 inhibitor) in the United States, using real-world data from shortly after its market entry and before public awareness of its potential safety concerns.Methods: In a U. S. commercial claims dataset (29 March 2013-30 Sept 2015, two pairwise cohorts of patients over 18 years of age with type 2 diabetes (T2D) who were newly dispensed canagliflozin or an active comparator, that is a dipeptidyl peptidase 4 inhibitor (DPP4) or a glucagon-like peptide 1 receptor agonist (GLP1), were identified and propensity score-matched. We used variable ratio matching with up to four people receiving a DPP4 or GLP1 for each person receiving canagliflozin. We identified potential safety signals using a hierarchical tree-based scan statistic data mining method with the hierarchical outcome tree constructed based on international classification of disease coding. We screened for incident adverse events where there were more outcomes observed among canagliflozin vs. comparator initiators than expected by chance, after adjusting for multiple testing. Results:We identified two pairwise propensity score variable ratio matched cohorts of 44,733 canagliflozin vs. 99,458 DPP4 initiators, and 55,974 canagliflozin vs. 74,727 GLP1 initiators. When we screened inpatient and emergency room diagnoses, diabetic ketoacidosis was the only severe adverse event associated with canagliflozin initiation with p < .05 in both cohorts. When outpatient diagnoses were also considered, signals for female and male genital infections emerged in both cohorts (p < .05). Conclusions and relevance:In a large population-based study, we identified known but no other adverse events associated with canagliflozin, providing reassurance on its safety among adult patients with T2D and suggesting the tree-based scan statistic method is a useful post-marketing safety monitoring tool for newly approved medications.

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Statistical Power for Postlicensure Medical Product Safety Data-Mining

Cited by 4 publications

References 0 publications

Novel methods for pregnancy drug safety surveillance in the FDA Sentinel System

Novel methods for pregnancy drug safety surveillance in the FDA Sentinel System

Monitoring Drug Safety in Pregnancy with Scan Statistics: A Comparison of Two Study Designs

A novel data mining application to detect safety signals for newly approved medications in routine care of patients with diabetes

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