c212 : An R package for the detection of safety signals in clinical trials using body-systems (System Organ Classes)

Carragher, Raymond and Robertson, Chris (2020) c212 : An R package for the detection of safety signals in clinical trials using body-systems (System Organ Classes). Journal of Open Source Software, 5 (56). 2706. ISSN 2475-9066 (https://doi.org/10.21105/joss.02706)

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Safety in clinical trials may be characterised by the incidence or occurrence of adverse events. The statistical analysis of this data is complicated by the large number of adverse events recorded, with low event rates, small effect sizes and low power all contributing to the difficulty in determining a robust safety profile for a treatment during the trial process. In addition to end of trial analyses, a number of interim analyses may take place at different time points during the trial lifecycle. These offer the additional statistical challenge of testing accumulating data, with possibly differing recruitment rates on trial arms contributing to a lack of balance in the data. Adverse events are typically defined by medical dictionaries, which provide a common reference terminology for use in and between clinical trials. There are a number of medical dictionaries in current use, all of which provide similar services. One such dictionary is MedDRA (Medical Dictionary for Regulatory Activities), which was developed by the ICH (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use) and is widely used by regulatory bodies, clinical research organisations (CROs), and pharmaceutical companies. WHO-ART (World Health Organisation Adverse Reaction Terminology) is a similar dictionary maintained by the Uppsala Monitoring Centre for the World Health Organisation Collaborating Centre for International Drug Monitoring. MedDRA and WHO-ART have a similar hierarchical structure consisting of System Organ Classes (SOC) and various grouping and descriptor terms. The MedDRA hierarchical structure consists of five levels: System Organ Class (SOC), High Level Group Terms (HLGT), High Level Terms (HLT), Preferred Terms (PT), and Lower Level Terms (LLT). The PT is a single medical description of a symptom or observation while the LLT is how a patient or data recorder would describe a symptom or observation. Each LLT belongs to one PT and, in general, data will be recorded at the LLT level but reported at the PT level (the adverse event). As of 2020 there are 27 SOCs and over 80,000 LLTs. The grouping of adverse events by SOC (or body-system) provides for possible relationships between the adverse events within a SOC. One consequence of this is the possibility that, for treatments which may affect a particular SOC, there may be raised rates for a number of adverse events within that SOC. A number of methods have recently been proposed to address the statistical issues in adverse event analysis by using these groupings of adverse events by body-system or SOC, taking into account the additional information provided by these relationships to increase the power of detecting real adverse event effects. These methods, Hochberg, 1995; Hu et al., 2010; Matthews, 2006; Mehrotra & Adewale, 2012; Yekutieli, 2008), and Bayesian modelling approaches (Amy Xia et al., 2011; Berry & Berry, 2004; Carragher, 2017b), are implemented in the R package c212.