A Maximized Sequential Probability Ratio Test for Drug and Vaccine Safety Surveillance

Kulldorff, Martin; Davis, Robert L.; Kolczak, Margarette S.; Lewis, Edwin; Lieu, Tracy A.; Platt, Richard

doi:10.1080/07474946.2011.539924

Cited by 201 publications

(238 citation statements)

References 39 publications

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“…We defined a "signal" as occurring when the number of seizures 42 days after vaccination significantly exceeded the number expected according to the maximized sequential probability ratio test 11 ; the threshold was set to a level designed to keep the cumulative chance of making a type I error below 5% over several years of weekly analyses. We assessed the statistical significance of temporal seizure clustering by using SaTScan software.…”

Section: Surveillance and Signal Detectionmentioning

confidence: 99%

“…The VSD has developed a near-real-time vaccine safety surveillance system known as rapid cycle analysis (RCA), [8][9][10] which was designed to monitor for potential associations between specific vaccines and prespecified adverse events by using weekly data and sequential statistical analysis. 11 Beginning in 2007, we used the RCA surveillance system to monitor weekly 6 specific outcomes after MMRV vaccination. On the basis of ϳ43 000 MMRV doses administered between February 2006 and August 2007, we detected a preliminary signal for an approximately twofold increased risk of febrile seizures occurring 7 to 10 days after MMRV vaccination compared with separately administered MMR ϩ varicella vaccination.…”

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confidence: 99%

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Measles-Mumps-Rubella-Varicella Combination Vaccine and the Risk of Febrile Seizures

et al. 2010

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WHAT'S KNOWN ON THIS SUBJECT:We previously alerted the ACIP to preliminary evidence of a twofold increased risk of febrile seizures after MMRV when compared with separate MMR and varicella vaccines after monitoring with the VSD RCA surveillance system. WHAT THIS STUDY ADDS:Using VSD data on twice as many vaccines, we examined the effect of MMRV on risk of seizure and describe here the postvaccination risk interval for increased fever and febrile seizures after vaccination. abstract OBJECTIVE: In February 2008, we alerted the Advisory Committee on Immunization Practices to preliminary evidence of a twofold increased risk of febrile seizures after the combination measles-mumps-rubella-varicella (MMRV) vaccine when compared with separate measles-mumps-rubella (MMR) and varicella vaccines. Now with data on twice as many vaccine recipients, our goal was to reexamine seizure risk after MMRV vaccine. METHODS: Using 2000 -2008Vaccine Safety Datalink data, we assessed seizures and fever visits among children aged 12 to 23 months after MMRV and separate MMR ϩ varicella vaccines. We compared seizure risk after MMRV vaccine to that after MMR ϩ varicella vaccines by using Poisson regression as well as with supplementary regressions that incorporated chart-review results and self-controlled analyses. RESULTS:MMRV vaccine recipients (83 107) were compared with recipients of MMR ϩ varicella vaccines (376 354). Seizure and fever significantly clustered 7 to 10 days after vaccination with all measles-containing vaccines but not after varicella vaccination alone. Seizure risk during days 7 to 10 was higher after MMRV than after MMR ϩ varicella vaccination (relative risk: 1.98 [95% confidence interval: 1.43-2.73]). Supplementary analyses yielded similar results. The excess risk for febrile seizures 7 to 10 days after MMRV compared with separate MMR ϩ varicella vaccination was 4.3 per 10 000 doses (95% confidence interval: 2.6 -5.6). CONCLUSIONS:Among 12-to 23-month-olds who received their first dose of measles-containing vaccine, fever and seizure were elevated 7 to 10 days after vaccination. Vaccination with MMRV results in 1 additional febrile seizure for every 2300 doses given instead of separate MMR ϩ varicella vaccines. Providers who recommend MMRV should communicate to parents that it increases the risk of fever and seizure over that already associated with measles-containing vaccines.

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Section: Surveillance and Signal Detectionmentioning

confidence: 99%

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confidence: 99%

Measles-Mumps-Rubella-Varicella Combination Vaccine and the Risk of Febrile Seizures

et al. 2010

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“…can be obtained through a numerical bisection algorithm. 8 This is the case, for instance, of the most important sequential testing methods, such as Pocock's method, 6 O'Brien & Fleming's test, 7 MaxSPRT, 8 and the modified MaxSPRT. 13 Table 1 presents the test statistics related to each of these methods.…”

Section: Definition 2 (Continuous Sequential Analysis)mentioning

confidence: 99%

“…More recently, new test statistics has been developed specially for monitoring adverse events caused by recently approved drugs. This is the case of the maximized SPRT (MaxSPRT), introduced by Kulldorff et al, 8 and the conditional MaxSPRT, proposed by Li and Kulldorff. 9 Alternatively, in place of a flat threshold given in the scale of a test statistic, sequential hypothesis testing can be designed through a Type I error spending approach.…”

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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“…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%

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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A Maximized Sequential Probability Ratio Test for Drug and Vaccine Safety Surveillance

Cited by 201 publications

References 39 publications

Measles-Mumps-Rubella-Varicella Combination Vaccine and the Risk of Febrile Seizures

Measles-Mumps-Rubella-Varicella Combination Vaccine and the Risk of Febrile Seizures

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

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

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