Real-Time Vaccine Safety Surveillance for the Early Detection of Adverse Events

Lieu, Tracy A.; Kulldorff, Martin; Davis, Robert L.; Lewis, Edwin; Weintraub, Eric; Yih, Katherine; Yin, Ruihua; Brown, Jeffrey S.; Platt, Richard

doi:10.1097/mlr.0b013e3180616c0a

Cited by 197 publications

(174 citation statements)

References 10 publications

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“…The group sequential likelihood ratio test (LRT) approach is a method that has been used in the Vaccine Safety Data Link project to monitor vaccine safety for a single time vaccine exposure. 3,6,7,14 The approach uses exposure matching with a fixed matching ratio (1:M) to control for confounding and then computes a LRT statistic. The most commonly used method is the Binomial maxSPRT, 14 which assumes continuous monitoring (i.e., after each matched set of exposed and unexposed individuals come into the dataset, the test statistic is compared with the monitoring boundary).…”

Section: Group Sequential Statistical Methodsmentioning

confidence: 99%

“…RR AtoB 5 TestStat 6 Test statistic boundary 7 Signal 1 Y cum is the total cumulative events observed at and before look t.…”

Section: (Person-days)mentioning

confidence: 99%

“…6 TestStat is the observed test statistic calculated at each look and is the Wald-based test for GS LD and score-based test for GS EE. 7 Test Statistic Boundary is the critical boundary in which the test statistic is compared to indicate if a given look has signaled. 1 Y cum is the total cumulative events observed at and before look t. 2 Y cum,D = A is the total cumulative events observed at or before look t for those on drug A.…”

Section: (Person-days)mentioning

confidence: 99%

“…There have been several examples of signal refinement studies published, but this is still a relatively new area. [3][4][5][6][7] Statistical methods used to address hypotheses within postmarket safety evaluation designs must be able to detect both rare and common adverse events, control for confounding, and maintain the overall type I error across multiple tests. This manuscript describes the current state of statistical methods developed to conduct sequential analysis of prospective cohort data for medical product safety.…”

Section: Introductionmentioning

confidence: 99%

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Statistical approaches to group sequential monitoring of postmarket safety surveillance data: current state of the art for use in the Mini‐Sentinel pilot

Cook

Tiwari

Wellman

et al. 2012

Pharmacoepidemiology and Drug

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Purpose This manuscript describes the current statistical methodology available for active postmarket surveillance of pre-specified safety outcomes using a prospective incident user concurrent control cohort design with existing electronic healthcare data. Methods Motivation of the active postmarket surveillance setting is provided using the Food and Drug Administration's Mini-Sentinel Pilot as an example. Four sequential monitoring statistical methods are presented including the Lan-Demets error spending approach, a matched likelihood ratio test statistic approach with the binomial MaxSPRT as a special case, the conditional sequential sampling procedure with stratification, and a generalized estimating equation regression approach using permutation. Information on the assumptions, limitations, and advantages of each approach is provided, including how each method defines sequential monitoring boundaries, what test statistic is used, and how robust it is to settings of rare events or frequent testing. Results A hypothetical example of how the approaches could be applied to data comparing a medical product of interest, drug A, to a concurrent control drug, drug B, is presented including providing the type of information one would have available for monitoring such drugs. Summary We have described the current state of methodology for postmarket surveillance of pre-specified safety outcomes. We describe the limitations and advantages of the approaches while acknowledging areas for future development.

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Section: Group Sequential Statistical Methodsmentioning

confidence: 99%

“…RR AtoB 5 TestStat 6 Test statistic boundary 7 Signal 1 Y cum is the total cumulative events observed at and before look t.…”

Section: (Person-days)mentioning

confidence: 99%

Section: (Person-days)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Statistical approaches to group sequential monitoring of postmarket safety surveillance data: current state of the art for use in the Mini‐Sentinel pilot

Cook

Tiwari

Wellman

et al. 2012

Pharmacoepidemiology and Drug

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show abstract

“…When a new drug, or vaccine, is commercialized, fast identification of potential risks from serious adverse events is a central goal for the population's safety. [1][2][3][4] The conventional approach for sequential analysis is based on monitoring a real-valued test statistic in comparison to a flat signaling threshold at each single test. The analysis is interrupted as soon as the test statistic becomes greater than such threshold.…”

Section: Introductionmentioning

confidence: 99%

Type I error probability spending for post–market drug and vaccine safety surveillance with binomial data

Silva

2017

Statistics in Medicine

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Type I error probability spending functions are commonly used for designing sequential analysis of binomial data in clinical trials, but it is also quickly emerging for near-continuous sequential analysis of post-market drug and vaccine safety surveillance. It is well known that, for clinical trials, when the null hypothesis is not rejected, it is still important to minimize the sample size. Unlike, in post-market drug and vaccine safety surveillance, that is not important. In post-market safety surveillance, specially when the surveillance involves identification of potential signals, the meaningful statistical performance measure to be minimized is the expected sample size when the null hypothesis is rejected. The present paper shows that, instead of the convex Type I error spending shape conventionally used in clinical trials, a concave shape is more indicated for post-market drug and vaccine safety surveillance. This is shown for both, continuous and group sequential analysis.

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Risk of Intussusception Following Administration of a Pentavalent Rotavirus Vaccine in US Infants

Shui¹,

Baggs²,

Patel³

et al. 2012

JAMA

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128

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N 1999, THE RHESUS TETRAVALENT rotavirus vaccine (RRV, Rotashield, Wyeth) was withdrawn from the US market due to a significantly increased risk of intussusception following vaccination. The largest increased risk (Ͼ30-fold) of intussusception was observed during the 3 to 7 days following the first dose of the vaccine. [1][2][3] Since then, 2 vaccines to prevent rotavirus infection have been licensed for use in the United States: a pentavalent rotavirus vaccine (RV5, RotaTeq, Merck) in 2006 and a monovalent rotavirus vaccine (RV1, Rotarix, GlaxoSmithKline Biologicals) in 2008. 4,5 The pentavalent rotavirus vaccine is a 3-dose series that, according to the Advisory Committee on Immunization Practices, should be given at ages 2, 4, and 6 months; RV1 is a 2-dose series to be

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Real-Time Vaccine Safety Surveillance for the Early Detection of Adverse Events

Abstract: Real-time surveillance combining dynamic data files, aggregation of data, and sequential analysis methods offers a useful and highly adaptable approach to early detection of adverse events after the introduction of new vaccines.

Cited by 197 publications

References 10 publications

Statistical approaches to group sequential monitoring of postmarket safety surveillance data: current state of the art for use in the Mini‐Sentinel pilot

Statistical approaches to group sequential monitoring of postmarket safety surveillance data: current state of the art for use in the Mini‐Sentinel pilot

Type I error probability spending for post–market drug and vaccine safety surveillance with binomial data

Risk of Intussusception Following Administration of a Pentavalent Rotavirus Vaccine in US Infants

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