The impact of exposure model misspecification on signal detection in prospective pharmacovigilance

Gaalen, Rolina D. van; Abrahamowicz, Michał; Buckeridge, David L.

doi:10.1002/pds.3700

Cited by 6 publications

(5 citation statements)

References 63 publications

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“…The critical value tables presented in this paper has already been used by the Vaccine Safety Datalink project. As new near real-time post-market safety surveillance systems are being developed, it is important to fine-tune and optimize the performance of near-real time safety surveillance systems [16,17,27,32,33,34]. While the improved time to signal is modest compared to the original version of the Poisson based MaxSPRT, there is no reason not to use these better designs.…”

Section: Discussionmentioning

confidence: 99%

Continuous Post-Market Sequential Safety Surveillance with Minimum Events to Signal

Kulldorff,

Silva

2015

Preprint

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The CDC Vaccine Safety Datalink project has pioneered the use of near real-time post-market vaccine safety surveillance for the rapid detection of adverse events. Doing weekly analyses, continuous sequential methods are used, allowing investigators to evaluate the data nearcontinuously while still maintaining the correct overall alpha level. With continuous sequential monitoring, the null hypothesis may be rejected after only one or two adverse events are observed. In this paper, we explore continuous sequential monitoring when we do not allow the null to be rejected until a minimum number of observed events have occurred. We also evaluate continuous sequential analysis with a delayed start until a certain sample size has been attained. Tables with exact critical values, statistical power and the average times to signal are provided. We show that, with the first option, it is possible to both increase the power and reduce the expected time to signal, while keeping the alpha level the same. The second option is only useful if the start of the surveillance is delayed for logistical reasons, when there is a group of data available at the first analysis, followed by continuous or near-continuous monitoring thereafter.

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Section: Discussionmentioning

confidence: 99%

Continuous Post-Market Sequential Safety Surveillance with Minimum Events to Signal

Kulldorff,

Silva

2015

Preprint

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“…12 Mis-specification of the model describing the association between a time-varying exposure and the health outcome may lead to biased estimates and incorrect conclusions, and may affect both the probability and the timeliness of adverse events or therapeutic effects detection. 1,13 Because the impact of past drug use is likely to cumulate over time, 14,15 and because the relative importance of past doses likely depends on their timing, 11,[14][15][16][17][18] the weighted cumulative exposure (WCE) approach has been proposed to assess the impact of various aspects of time-varying exposure.…”

Section: Introductionmentioning

confidence: 99%

Modeling of cumulative effects of time-varying drug exposures on within-subject changes in a continuous outcome

Danieli

Sheppard

Costello

et al. 2020

Stat Methods Med Res

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An accurate assessment of the safety or effectiveness of drugs in pharmaco-epidemiological studies requires defining an etiologically correct time-varying exposure model, which specifies how previous drug use affects the outcome of interest. To address this issue, we develop, and validate in simulations, a new approach for flexible modeling of the cumulative effects of time-varying exposures on repeated measures of a continuous response variable, such as a quantitative surrogate outcome, or a biomarker. Specifically, we extend the linear mixed effects modeling to estimate how past and recent drug exposure affects the way individual values of the outcome change throughout the follow-up. To account for the dosage, duration and timing of past exposures, we rely on a flexible weighted cumulative exposure methodology to model the cumulative effects of past drug use, as the weighted sum of past doses. Weights, modeled with unpenalized cubic regression B-splines, reflect the relative importance of doses taken at different times in the past. In simulations, we evaluate the performance of the model under different assumptions concerning (i) the shape of the weight function, (ii) the sample size, (iii) the number of the longitudinal observations and (iv) the intra-individual variance. Results demonstrate the accuracy of our estimates of the weight function and of the between- and within-patients variances, and good correlation between the observed and predicted longitudinal changes in the outcome. We then apply the proposed method to re-assess the association between time-varying glucocorticoid exposure and weight gain in people living with rheumatoid arthritis.

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“…Usually, criteria for determining prescription durations in pharmacoepidemiology are ad hoc decision rules based on expert insights with some input from observed treatment patterns. Although the topic has received substantial interest in pharmacoepidemiological research, there is a scarcity of validated methods and little agreement on the optimal approach, despite several studies comparing them . In 2013, Pottegård and Hallas suggested to use the waiting time distribution (WTD) to determine durations in an automated manner by estimating a percentile of when a specified percentage of prevalent users had renewed their prescription.…”

Section: Introductionmentioning

confidence: 99%

“…Although the topic has received substantial interest in pharmacoepidemiological research, there is a scarcity of validated methods and little agreement on the optimal approach, despite several studies comparing them. [5][6][7] In 2013, Pottegård and Hallas suggested to use the waiting time distribution (WTD) to determine durations in an automated manner by estimating a percentile of when a specified percentage of prevalent users had renewed their prescription. They did, however, not consider the distinction between the component distribution observed in the WTD, the so-called forward recurrence distribution, and the inter-arrival distribution (IAD).…”

Section: Introductionmentioning

confidence: 99%

Refining estimates of prescription durations by using observed covariates in pharmacoepidemiological databases: an application of the reverse waiting time distribution

Støvring

Pottegård

Hallas

2017

Pharmacoepidemiology and Drug

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The impact of exposure model misspecification on signal detection in prospective pharmacovigilance

Cited by 6 publications

References 63 publications

Continuous Post-Market Sequential Safety Surveillance with Minimum Events to Signal

Continuous Post-Market Sequential Safety Surveillance with Minimum Events to Signal

Modeling of cumulative effects of time-varying drug exposures on within-subject changes in a continuous outcome

Refining estimates of prescription durations by using observed covariates in pharmacoepidemiological databases: an application of the reverse waiting time distribution

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