Safety assessment of niacin in the US Food and Drug Administration's mini-sentinel system

A review on the efficacy and safety of lipid-lowering drugs in neurodegenerative disease

There is a train of thought that lipid therapies may delay or limit the impact of neuronal loss and poor patient outcomes of neurodegenerative diseases (NDDs). A variety of medicines including lipid lowering modifiers (LLMs) are prescribed in NDDs. This paper summarizes the findings of clinical and observational trials including systematic reviews and meta-analyses relating to LLM use in NDDs published in the last 15 years thus providing an up-to-date evidence pool. Three databases were searched PubMed, CINAHL, and Web of Science using key terms relating to the review question. The findings confirm the benefit of LLMs in hyperlipidemic patients with or without cardiovascular risk factors due to their pleotropic effects. In NDDs LLMs are proposed to delay disease onset and slow the rate of progression. Clinical observations show that LLMs protect neurons from α-synuclein, tau, and Aβ toxicity, activation of inflammatory processes, and ultimately oxidative injury. Moreover, current meta-analyses and clinical trials indicated low rates of adverse events with LLMs when used as monotherapy. LLMs appear to have favorable safety and tolerability profiles with few patients stopping treatment due to severe adverse effects. Our collated evidence thus concludes that LLMs have a role in NDDs but further work is needed to understand the exact mechanism of action and reach more robust conclusions on where and when it is appropriate to use LLMs in NDDs in the clinic.

Outcome misclassification: Impact, usual practice in pharmacoepidemiology database studies and an online aid to correct biased estimates of risk ratio or cumulative incidence

PURPOSE

It is well documented that outcome misclassification can bias a point estimate. We aimed to understand current practice in addressing this bias in pharmacoepidemiology database studies and to develop an open source application (app) from existing methodology to demonstrate the impact and mechanism of this bias on results.

METHODS

Studies of an exposure and a clinical outcome were selected from all Pharmacoepidemiology and Drug Safety publications during 2017 and any reference to outcome misclassification described. An app to correct risk ratio (RR) and cumulative incidence for outcome misclassification was developed from a published methodology and used to demonstrate the impact of correction on point estimates.

RESULTS

Eight (19%) of 43 papers selected reported estimates of outcome ascertainment accuracy with positive predictive value (PPV) the most commonly reported measure (7 of 8 studies). Three studies (7%) corrected for the bias, 1 by exposure strata, and 5 (12%) restricted analyses to confirmed cases. The app (app http://apps.p-95.com/ISPE/) uses values of PPV and sensitivity (or a range of possible values) in each exposure strata and returns corrected point estimates and confidence intervals. The app demonstrates that small differences between comparison groups in PPV or sensitivity can introduce bias even when accuracy estimates are high.

CONCLUSIONS

Outcome misclassification is not usually corrected in pharmacoepidemiology database studies although correction methods using routinely measured indices are available. Error indices are needed for each comparison group to correct RR estimates for these errors. The app should encourage understanding of this bias and increase adjustment.

Evaluating the Use of Nonrandomized Real-World Data Analyses for Regulatory Decision Making

The analysis of longitudinal healthcare data outside of highly controlled parallel-group randomized trials, termed real-world evidence (RWE), has received increasing attention in the medical literature. In this paper, we discuss the potential role of RWE in drug regulation with a focus on the analysis of healthcare databases. We present several cases in which RWE is already used and cases in which RWE could potentially support regulatory decision making. We summarize key issues that investigators and regulators should consider when designing or evaluating such studies, and we propose a structured process for implementing analyses that facilitates regulatory review. We evaluate the empirical evidence base supporting the validity, transparency, and reproducibility of RWE from analysis of healthcare databases and discuss the work that still needs to be done to ensure that such analyses can provide decision-ready evidence on the effectiveness and safety of treatments.