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Examining statistical methods to allow comprehensive evaluation and presentation of adverse event data in randomised controlled trials

Examining statistical methods to allow comprehensive evaluation and presentation of adverse event data in randomised controlled trials

Rachel Phillips

Imperial College London

4 Mar 2019

BackgroundClinical trials look at new interventions (such as drugs) for the treatment of diseases, infections or disorders. We consider randomised controlled trials (RCTs) the best method to examine the performance of these new treatments. RCTs compare new treatments to existing treatments or dummy treatments called "placebos". We randomly allocate patients that agree to participate in RCTs to one of the treatments and compare their outcomes between treatment groups. We compare the treatments to help us decide whether to use the new treatment in clinical practice. A lot of work has gone into finding the best method to analyse how well these new treatments perform in terms of improving patients' outcomes. But the methods to analyse the harmful side effects are not as well developed. We call the harmful side effects experienced by patients participating in RCTs adverse events. Adverse events can include things such as headaches, vomiting, as well as effects that are more serious. The cause of these harmful effects may not be related to the new treatment. Events that are known or suspected to be related to the new treatment are called adverse drug reactions.Current practiceWe typically report side-effect data as the number of adverse events by treatment group. When comparing treatments, as there are often many adverse events, identifying important side effects involves reviewing a large amount of data. This process relies on either subjective evaluation or statistical approaches to test the data. Using a subjective approach has obvious weaknesses and current statistical methods are not appropriate for side-effect data. Statistical methods can fail to pick up important differences when present and flag more signals than desired when not present. Therefore, current statistical methods may result in more patients taking a treatment where the risk-benefit profile is unbalanced, or erroneously stopping a trial too early. In the last 15 years, statisticians have proposed a handful of alternative statistical methods to identify signals for adverse drug reactions but these methods are not used in clinical trials. We do not currently know why these methods are not being used but we do know they are complex to implement and have a number of methodological limitations. We have developed new statistical methods that are easy to implement in standard statistical software and can be used to monitor all adverse events in clinical trials. We already know that the time an adverse event occurs with respect to the start of treatment is a useful indicator of causality and this is incorporated to improve performance in several of the methods. Some of the methods also incorporate existing knowledge on the adverse event profile to improve performance further. We have tried these methods out on simulated data but we do not know how they work in practice. ObjectivesTo evaluate statistical methods for the analysis of adverse event and continuous safety data, and explore alternative approaches to the presentation of complex adverse event information using real-world clinical trial data.Output If we confirm these methods work well in real world clinical trial data, they would provide a useful tool for future studies. The use of appropriate methods have the potential to improve outcomes for future trial participants by identifying adverse drug reactions earlier than conventional methods and allowing better follow-up and monitoring of these events in later phase studies. In the longer term, improved analysis and presentation of this information will lead to more accurate risk-benefit assessments for patients and prescribers.

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Statistical Analysis Plan