A nonparametric method to detect increased frequencies of adverse drug reactions over time.

Signal detection is routinely applied to spontaneous report safety databases in the pharmaceutical industry and by regulators. As an example, methods that search for increases in the frequencies of known adverse drug reactions for a given drug are routinely applied, and the results are reported to the health authorities on a regular basis. Such methods need to be sensitive to detect true signals even when some of the adverse drug reactions are rare. The methods need to be specific and account for multiplicity to avoid false positive signals when the list of known adverse drug reactions is long. To apply them as part of a routine process, the methods also have to cope with very diverse drugs (increasing or decreasing number of cases over time, seasonal patterns, very safe drugs versus drugs for life-threatening diseases). In this paper, we develop new nonparametric signal detection methods, directed at detecting differences between a reporting and a reference period, or trends within a reporting period. These methods are based on bootstrap and permutation distributions, and they combine statistical significance with clinical relevance. We conducted a large simulation study to understand the operating characteristics of the methods. Our simulations show that the new methods have good power and control the family-wise error rate at the specified level. Overall, in all scenarios that we explored, the method performs much better than our current standard in terms of power, and it generates considerably less false positive signals as compared to the current standard.

Bayesian Design of Proof-of-Concept Trials.

The proof-of-concept (PoC) decision is a key milestone in the clinical development of an experimental treatment. A decision is taken on whether the experimental treatment is further developed (GO), whether its development is stopped (NO-GO), or whether further information is needed to make a decision. The PoC decision is typically based on a PoC clinical trial in patients comparing the experimental treatment with a control treatment. It is important that the PoC trial be designed such that a GO/NO-GO decision can be made. The present work develops a generic, Bayesian framework for defining quantitative PoC criteria, against which the PoC trial results can be assessed. It is argued that PoC criteria based solely on significance testing versus the control are not appropriate in this decision context. A dual PoC criterion is proposed that includes assessment of superiority over the control and relevance of the effect size and hence better matches clinical decision making. The approach is illustrated for 2 PoC trials in cystic fibrosis and psoriasis.

Guidance on the implementation and reporting of a drug safety Bayesian network meta-analysis.

The Drug Information Association Bayesian Scientific Working Group (BSWG) was formed in 2011 with a vision to ensure that Bayesian methods are well understood and broadly utilized for design and analysis and throughout the medical product development process, and to improve industrial, regulatory, and economic decision making. The group, composed of individuals from academia, industry, and regulatory, has as its mission to facilitate the appropriate use and contribute to the progress of Bayesian methodology. In this paper, the safety sub-team of the BSWG explores the use of Bayesian methods when applied to drug safety meta-analysis and network meta-analysis. Guidance is presented on the conduct and reporting of such analyses. We also discuss different structural model assumptions and provide discussion on prior specification. The work is illustrated through a case study involving a network meta-analysis related to the cardiovascular safety of non-steroidal anti-inflammatory drugs.