Sequential generalized likelihood ratio tests for vaccine safety evaluation

Shih, Mei‐Chiung; Lai, Tze Leung; Heyse, Joseph F.; Chen, Jie

doi:10.1002/sim.4036

Cited by 36 publications

(27 citation statements)

References 31 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

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show abstract

“…If LLR t exceeds the critical value, then we reject the null hypothesis; otherwise, the surveillance continues until it reaches the pre‐specified upper limit t * . Note that MaxSPRT can be viewed as a special case of sequential generalized likelihood ratio (GLR) tests defined specifically on Poisson or binomial probability distributions without a lower boundary . Lack of a lower boundary means H 0 cannot be accepted before the surveillance reaches the upper limit t * .…”

Section: Maximized Sequential Probability Ratio Testmentioning

confidence: 99%

“…Lack of a lower boundary means H 0 cannot be accepted before the surveillance reaches the upper limit t * . Compared with the standard GLR tests, MaxSPRT exhibited a larger power but also required a larger sample size regardless of the true parameter value . However, the large sample size requirement is generally not a major concern for the observational databases because the cost of accruing additional data is minimal.…”

Section: Maximized Sequential Probability Ratio Testmentioning

confidence: 99%

Continuous sequential boundaries for vaccine safety surveillance

Stewart

Weintraub

et al. 2014

Statistics in Medicine

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Various recently developed sequential methods have been used to detect signals for post-marketing surveillance in drug and vaccine safety. Among these, the maximized sequential probability ratio test (MaxSPRT) has been used to detect elevated risks of adverse events following vaccination using large healthcare databases. However, a limitation of MaxSPRT is that it only provides a time-invariant flat boundary. In this study, we propose the use of time-varying boundaries for controlling how type I error is distributed throughout the surveillance period. This is especially useful in two scenarios: (i) when we desire generally larger sample sizes before a signal is generated, for example, when early adopters are not representative of the larger population; and (ii) when it is desired for a signal to be generated as early as possible, for example, when the adverse event is considered rare but serious. We consider four specific time-varying boundaries (which we call critical value functions), and we study their statistical power and average time to signal detection. The methodology we present here can be viewed as a generalization or flexible extension of MaxSPRT.

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“…The so-called mastery test is interested in testing whether θ < θ − or θ > θ + . Item response theory usually employs logistic models that fall into the exponential family for which there is a vast literature (Lai and Shih, 2004; Bartroff et al, 2008; Bartroff and Lai, 2008; Shih et al, 2010). However, some more complicated models go beyond exponential family, for which existing results do not apply.…”

Section: Introductionmentioning

confidence: 99%

Generalized Sequential Probability Ratio Test for Separate Families of Hypotheses

Liu

Ying

2014

Sequential Analysis

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In this paper, we consider the problem of testing two separate families of hypotheses via a generalization of the sequential probability ratio test. In particular, the generalized likelihood ratio statistic is considered and the stopping rule is the first boundary crossing of the generalized likelihood ratio statistic. We show that this sequential test is asymptotically optimal in the sense that it achieves asymptotically the shortest expected sample size as the maximal type I and type II error probabilities tend to zero.

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Sequential generalized likelihood ratio tests for vaccine safety evaluation

Cited by 36 publications

References 31 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

Continuous sequential boundaries for vaccine safety surveillance

Generalized Sequential Probability Ratio Test for Separate Families of Hypotheses

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