Likelihood ratio based tests for longitudinal drug safety data

Abstract

This article presents longitudinal likelihood ratio test (LongLRT) methods for large databases with exposure information. These methods are applied to a pooled large longitudinal clinical trial dataset for drugs treating osteoporosis with concomitant use of proton pump inhibitors (PPIs). When the interest is in the evaluation of a signal of an adverse event for a particular drug compared with placebo or a comparator, the special case of the LongLRT, referred to as sequential LRT (SeqLRT), is also presented. The results show that there is some possible evidence of concomitant use of PPIs leading to more adverse events associated with osteoporosis. The performance of the proposed LongLRT and SeqLRT methods is evaluated using simulated datasets and shown to be good in terms of (conditional) power and control of type I error over time. The proposed methods can also be applied to large observational databases with exposure information under the US Food and Drug Administration Sentinel Initiative for active surveillance. Published 2014. This article is a US Government work and is in the public domain in the USA.

An evaluation of statistical approaches to postmarketing surveillance

“…The LRT method has been extended to identify associations between drug class of interest and adverse events of interest . Additional extensions include the case of longitudinal data using a longitudinal LRT(LongLRT), whereas a sequential LRT(SeqLRT) is used when interest centers in evaluating the signal of adverse event for a particular drug compared with placebo or comparator . Other extensions of the LRT method can be found in Huang et al…”

“…That is, “The EudraVigilance Expert Working Group noted that thresholds commonly used to detect signals in spontaneous data are a trade‐off between two conflicting goals: either generating too many false positive signals if the threshold is too low or missing true signals if this threshold is too high. Given this trade‐off, it is important to identify and calibrate methods to strike a reasonable balance between these two parameters.” Although this evaluation was very extensive, it did not include a more recently developed approach using a likelihood ratio test (LRT)‐based method and the FDR‐based extensions of the aforementioned methods. In addition, comparisons among these methods are based on using data from different databases.…”

“…We refer to these outcome measures as the AE‐based and drug‐based outcome measures, respectively. In their evaluation of the LRT method, Huang et al used the AE‐based method but demonstrated good performance in several contexts . Specifically, the LRT method controls both the type I error and the FDR well while retaining good power and sensitivity, and Huang et al compared its performance with the PRR and BCPNN methods.…”

“…Given this trade-off, it is important to identify and calibrate methods to strike a reasonable balance between these two parameters." 16 Although this evaluation was very extensive, it did not include a more recently developed approach using a likelihood ratio test (LRT)-based method [17][18][19] and the FDR-based extensions of the aforementioned methods. In addition, comparisons among these methods are based on using data from different databases.…”

“…In their evaluation of the LRT method, Huang et al 17 used the AE-based method but demonstrated good performance in several contexts. [17][18][19] Specifically, the LRT method controls both the type I error and the FDR well while retaining good power and sensitivity, and Huang et al 17 compared its performance with the PRR and BCPNN methods. The alternative hypothesis of the test is whether a particular AE has a relatively higher frequency than other AEs associated with a given drug, and they addressed the multiple testing issue when multiple potential AEs are under detection.…”