Current Practices, Challenges, and Statistical Issues With Product Safety Labeling

Abstract

Prescribing health care professionals are dependent on product labels to give them the information they need to appropriately prescribe drugs and monitor patients. A well-constructed label with accurate, meaningful, and easy-to-understand information is important for understanding the benefit-risk profile of the compound. Statisticians are uniquely qualified to help. Historically, statisticians have played a major role in defining the efficacy information provided in the label. Similar leadership is needed to develop safety information for products. Statisticians should play a key role in making sure that the safety information from clinical trials is accurate and provides adequate information to health care professionals regarding the likely and potential effects of a drug. In this regard, giving careful thought to (a) the way safety information is defined and collected, (b) the way in which different adverse events (AEs) are categorized and combined over multiple studies, (c) the statistical methods used to analyze the safety data, and (d) the tables and graphs in which safety information is presented is needed. We have touched on aspects of each of these in this article. Though product labeling has improved greatly in the past 20 years, there are still many issues that need statisticians' leadership and thought processes. In this article, we consider some of the important issues and make recommendations to address them. These include passive versus active elicitation of AEs, grouping of terms for labeling purposes, definitions of treatment-emergent AEs, meta-analytical principles, and analytical methods for estimating incidence and rates. Consideration of these issues can benefit health care professionals and ultimately patients who take medicines.

Matching-adjusted indirect comparison of efficacy in patients with moderate-to-severe plaque psoriasis treated with ixekizumab vs. secukinumab

“…It is also possible that differences existed across trials in terms of how adverse events were collected (e.g. active vs. passive elicitation) and in the definition of ‘treatment‐emergent’ . It is important to note that the PASI results were based on the nonresponder imputation, in which all patients who discontinued the study for any reason including adverse events were counted as nonresponders.…”

“…active vs. passive elicitation) and in the definition of 'treatment-emergent'. 24 It is important to note that the PASI results were based on the nonresponder imputation, in which all patients who discontinued the study for any reason including adverse events were counted as nonresponders. Thus, the PASI results already combine elements of efficacy and tolerability.…”

The FDA's Final Rule on Expedited Safety Reporting: Statistical Considerations

“…Identical events will be classified both by diagnosis and symptom complexes. These difficulties are most problematic for nonserious adverse events (see Crowe et al 2013). One approach to aid investigators is to remind them to look at the way in which they previously reported an event for a specific person.…”

On estimands and the analysis of adverse events in the presence of varying follow‐up times within the benefit assessment of therapies

“…It is the Aalen‐Johansen estimator that generalizes the Kaplan‐Meier estimator to multiple event types and nonparametrically estimates the so‐called cumulative incidence function P(AE in [0, t ]), accounting for both CEs and the usual censoring due to end of follow‐up. The Aalen‐Johansen estimator for the probability of AE occurrence is (Allignol et al and references therein) where T is the time until occurrence of an AE or of a CE, denotes the estimate of the probability of not experiencing an AE or the CE just prior to time u , and n u is the number of patients at risk of observing an AE or a CE just prior to u .…”