Continuous sequential boundaries for vaccine safety surveillance

Li, Rongxia; Stewart, Brock; Weintraub, Eric; McNeil, Michael M.

doi:10.1002/sim.6161

Cited by 9 publications

(8 citation statements)

References 27 publications

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“…Assuming N is the maximum surveillance length in terms of expected number of cases, the stopping boundary, T , for the continuous MaxSPRT is

T = m i n (\hat{T}, N),

where

\hat{T} = i n f \{t > 0 : l n (Λ) > = B (t)\},

which is the time we reject H 0 . B ( t ) is a function of time t , which can be a time‐invariant flat constant proposed in the original MaxSPRT paper or a time‐varying boundary, which changes over time . For a group sequential method, the stopping boundary can be defined as B ( t )= a ( N / Y t ) 1−2 δ…”

Section: Frequentist Sequential Methods In Post‐licensure Safety Survmentioning

confidence: 99%

A Bayesian approach to sequential analysis in post‐licensure vaccine safety surveillance

Stewart

Rose

2019

Pharmaceutical Statistics

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With rapid development of computing technology, Bayesian statistics have increasingly gained more attention in various areas of public health. However, the full potential of Bayesian sequential methods applied to vaccine safety surveillance has not yet been realized, despite acknowledged practical benefits and philosophical advantages of Bayesian statistics. In this paper, we describe how sequential analysis can be performed in a Bayesian paradigm in the field of vaccine safety. We compared the performance of the frequentist sequential method, specifically, Maximized Sequential Probability Ratio Test (MaxSPRT), and a Bayesian sequential method using simulations and a real world vaccine safety example. The performance is evaluated using three metrics: false positive rate, false negative rate, and average earliest time to signal. Depending on the background rate of adverse events, the Bayesian sequential method could significantly improve the false negative rate and decrease the earliest time to signal. We consider the proposed Bayesian sequential approach to be a promising alternative for vaccine safety surveillance.

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“…Assuming N is the maximum surveillance length in terms of expected number of cases, the stopping boundary, T , for the continuous MaxSPRT is

T = m i n (\hat{T}, N),

where

\hat{T} = i n f \{t > 0 : l n (Λ) > = B (t)\},

Section: Frequentist Sequential Methods In Post‐licensure Safety Survmentioning

confidence: 99%

A Bayesian approach to sequential analysis in post‐licensure vaccine safety surveillance

Stewart

Rose

2019

Pharmaceutical Statistics

Self Cite

View full text Add to dashboard Cite

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“…But, any of these methods could promote either concave or convex shapes by strategical usage of time-varying thresholds. For example, in the context of post-market safety surveillance, [14] generalizes MaxSPRT for Poisson data by considering the usage of non-flat critical values according to target Type I error probability spending. By comparing four different critical value functions in terms of statistical power and expected time to signal, they argue that, if an adverse event is rare but can lead to severe harmful consequences, then early rejection of H 0 should be permitted, that is, decreasing signaling thresholds should be preferred.…”

Section: Expected Time To Signalmentioning

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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“…Numerous methods have been used to conduct safety studies with the VAERS database [3,4,5,6,7,8,9,10,11,12,13,14,15]. In these methods, a contingency table is generally created to display counts for all vaccine and adverse event pairs during a specified time period.…”

Section: Introductionmentioning

confidence: 99%

“…Numerous methods have been used to conduct safety studies with the VAERS database. [4][5][6][7][8][9][10][11][12][13][14][15][16] In these methods, a contingency table is generally created to display counts for all vaccine and AE pairs during a specified time period. In this table, each row represents a vaccine and each column represents an AE.…”

Section: Introductionmentioning

confidence: 99%

Vaccine adverse event enrichment tests

Zhao

2021

Statistics in Medicine

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Background: : Vaccination safety is critical for individual and public health. Many existing methods have been used to conduct safety studies with the VAERS (Vaccine Adverse Event Reporting System) database. However, these methods frequently identify many adverse event (AE) signals and they are often hard to interpret in a biological context. The AE ontology introduces biologically meaningful structures to the VAERS database by connecting similar AEs, which provides meaningful interpretation for the underlying safety issues. In this paper, we develop rigorous statistical methods to identify "interesting" AE groups by performing AE enrichment analysis.Results: We extend existing gene enrichment tests to perform AE enrichment analysis. Unlike the continuous gene expression data, AE data are counts. Therefore, AE data has many zeros and ties. We propose two enrichment tests, AEFisher and AEKS. AEFisher is a modified Fisher's exact test based on pre-selected significant AEs, while AEKS is based on a modified Kolmogorov-Smirnov statistic. Both tests incorporate the special features of the AE data. The proposed methods were evaluated using simulation studies and were further illustrated on two studies using VAERS data. Conclusion:By appropriately addressing the issues of ties and excessive zeros in AE count data, our enrichment tests performed well as demonstrated by simulation studies and analyses of VAERS data. The proposed methods were implemented in R package AEenrich and can be installed from the

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Continuous sequential boundaries for vaccine safety surveillance

Cited by 9 publications

References 27 publications

A Bayesian approach to sequential analysis in post‐licensure vaccine safety surveillance

A Bayesian approach to sequential analysis in post‐licensure vaccine safety surveillance

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

Vaccine adverse event enrichment tests

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