Abstract
Pre- and postmarket drug safety evaluations usually include an integrated summary of results obtained using data from multiple studies related to a drug of interest. This paper proposes three approaches based on the likelihood ratio test (LRT), called the LRT methods, for drug safety signal detection from large observational databases with multiple studies, with focus on identifying signals of adverse events (AEs) from many AEs associated with a particular drug or inversely for signals of drugs associated with a particular AE. The methods discussed include simple pooled LRT method and its variations such as the weighted LRT that incorporates the total drug exposure information by study. The power and type-I error of the LRT methods are evaluated in a simulation study with varying heterogeneity across studies. For illustration purpose, these methods are applied to Proton Pump Inhibitors (PPIs) data with 6 studies for the effect of concomitant use of PPIs in treating patients with osteoporosis and to Lipiodol (a contrast agent) data with 13 studies for evaluating that drug's safety profiles.
Highlights
Meta-analysis approaches for multiple independent studies have become very popular in medical research
We presented the two examples here for showing the performance of the proposed methods on two different types of datasets
E power in Table 6 is highest for pooled method, and moderate for wLRn and MMLR methods. e MMLR method is more conservative than wLRn. e power values increase with the increase of relative risk values assigned to the 1st row
Summary
Meta-analysis approaches for multiple independent studies have become very popular in medical research. In many observational and/or clinical trial studies, meta-analysis can be performed using the study-level summary measures or patient-level information; for example, the studies can be integrated using a common statistical measure such as the study-level mean or effect size and computing a weighted average of this common measure using a statistical approach such as a fixed-effect model or a random-effects model [1]. E traditional meta-analysis of many large and small clinical trials, published studies, registries, and large clinical and/or observational databases, for thorough evaluation of clinical efficacy endpoints such as the mean change in the weight-loss or blood-pressure and hazard ratio in survival comparison and clinical safety endpoints such as odds ratio, risk ratio, and absolute risk difference, has become a common practice for a modern-day pre- and postmarket clinical/observational studies [1, 2]. It is possible that a signal detected in one study may not be detected in other studies due to variation across studies (in terms of sample sizes, study sites, personnel, patients enrolled, study time, and others)
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