Impact of Stratification on Adverse Drug Reaction Surveillance

Hopstadius, Johan; Norén, G. Niklas; Bate, Andrew; Edwards, I. Ralph

doi:10.2165/00002018-200831110-00008

Cited by 55 publications

(36 citation statements)

References 28 publications

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“…However, stratification requires enumeration of all important covariates, which is computationally intractable to use for safety surveillance. Identifying a fixed set of likely covariates for all drugs can help the analysis, but reduces power when dividing the reports across strata that are not correlated with the outcome (4, 8). In addition, stratification is impossible when the reporting systems do not contain reliable measures of the common covariates.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Prediction of Drug Effects and Interactions

et al. 2012

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Adverse drug events remain a leading cause of morbidity and mortality around the world. Many adverse events are not detected during clinical trials before a drug receives approval for use in the clinic. Fortunately, as part of postmarketing surveillance, regulatory agencies and other institutions maintain large collections of adverse event reports, and these databases present an opportunity to study drug effects from patient population data. However, confounding factors such as concomitant medications, patient demographics, patient medical histories, and reasons for prescribing a drug often are uncharacterized in spontaneous reporting systems, and these omissions can limit the use of quantitative signal detection methods used in the analysis of such data. Here, we present an adaptive data-driven approach for correcting these factors in cases for which the covariates are unknown or unmeasured and combine this approach with existing methods to improve analyses of drug effects using three test data sets. We also present a comprehensive database of drug effects (Offsides) and a database of drug-drug interaction side effects (Twosides). To demonstrate the biological use of these new resources, we used them to identify drug targets, predict drug indications, and discover drug class interactions. We then corroborated 47 (P < 0.0001) of the drug class interactions using an independent analysis of electronic medical records. Our analysis suggests that combined treatment with selective serotonin reuptake inhibitors and thiazides is associated with significantly increased incidence of prolonged QT intervals. We conclude that confounding effects from covariates in observational clinical data can be controlled in data analyses and thus improve the detection and prediction of adverse drug effects and interactions.

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Section: Introductionmentioning

confidence: 99%

“…In addition, stratification is impossible when the reporting systems do not contain reliable measures of the common covariates. These factors limit the benefit of applying stratification routinely (8). …”

Section: Introductionmentioning

confidence: 99%

Data-Driven Prediction of Drug Effects and Interactions

et al. 2012

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“…It showed that using a MGPS stratified by sex and age reduced the number of vaccine–event pairs highlighted compared with the crude MGPS. On the other hand, Hopstadius raised concern about the routine use of too many stratification factors, which may induce overstratification and lead to missed signals. In addition, after more than 5 years of use in GlaxoSmithKline Vaccine of the MGPS in the setting described earlier, the vaccine–event pairs seemed to have been detected lately and in a small number by the MGPS algorithm compared with the other signal detection methods (instream medical review, literature review, pharmacoepidemiological studies, etc.…”

Section: Introductionmentioning

confidence: 99%

Optimization of a quantitative signal detection algorithm for spontaneous reports of adverse events post immunization

Holle

Bauchau²

2012

Pharmacoepidemiology and Drug

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“…Such could be the result of combining several stratification variables or of including excessive number of strata within one variable. 16 This is also the result of the research conducted by Hopstadius et al 11 on the WHO spontaneous reporting database. The authors showed that the observed-to-expected ratios are quite susceptible to the effects of over-stratification and they cautioned that stratification must be carefully designed to avoid very small strata.…”

Section: Discussionmentioning

confidence: 66%

Quantitative signal detection for vaccines: Effects of stratification, background and masking on GlaxoSmithKline’s spontaneous reports database

Zeinoun¹,

Seifert²,

Verstraeten³

2009

Human Vaccines

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Disproportionality analyses are increasingly gaining acceptance as signal detection tools for drug adverse events. Their application to vaccine adverse events has not been well evaluated. Disproportionality analyses based on the Multi-Item Gamma Poisson Shrinkage principle (MGPS) were applied to spontaneous adverse events reports for five vaccines with different reporting profiles. Sensitivity analyses were used to assess the potential impact of changing key parameters. We used the Company's in-house spontaneous adverse events database. We evaluated the impact of stratification, the comparator dataset and the potential for masking. We conducted a semi-quantitative assessment by comparing the changes in the disproportionality scores and the number of vaccine-event pairs that exceeded an arbitrary threshold as a measure of the impact of any of these choices. The results show that stratification by age and region has a significant impact. The effect of the comparator dataset was dependent on the vaccine being evaluated. The potential for a masking effect was only weakly noticeable. In conclusion, we opt to start with a conservative approach in which we will supplement our primary stratification against the vaccine database with unstratified analyses as well as analyses against the entire database. We recommend that similar studies be performed before introducing disproportionality analyses to a new vaccine adverse events database.

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Impact of Stratification on Adverse Drug Reaction Surveillance

Cited by 55 publications

References 28 publications

Data-Driven Prediction of Drug Effects and Interactions

Data-Driven Prediction of Drug Effects and Interactions

Optimization of a quantitative signal detection algorithm for spontaneous reports of adverse events post immunization

Quantitative signal detection for vaccines: Effects of stratification, background and masking on GlaxoSmithKline’s spontaneous reports database

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