Jack Keeler, Ruth Owen, Inês Reis, Georgios Nikolaidis.
In modern medicine, it is becoming more apparent of patient and disease heterogeneity, and this can have consequences in clinical trials that do not take this into consideration. For example, Ellis and Taylor (2002) mention that ACE inhibitors are less effective in African American patients than White patients when treating heart failure. Many trials do not have sufficient information from early phase trials to definitively predict treatment effects for differing subgroups and some trials explore subgroups as exploratory endpoints, but exploratory conclusions are not always considered concrete. A solution for dealing with the differing biomarkers is to use adaptive trial designs, namely, enrichment designs. Survival trials may benefit greatly from such adaptations as survival trials are some of the longest trials conducted. Using Magnusson and Turnbull’s (2013) Group Sequential Design incorporating Subgroup Selection (GSDS), a trial can use the first interim analysis as a means of discontinuing treatments for subgroups who are not experiencing the desired effect from the treatment. This is making the trial ethically sound, as patients in certain subgroups are saved from ineffective treatments. GSDS does not follow the same boundary calculation rules as normal group-sequential designs, due to the selection criteria, but the conduct of the trial is very similar, making it feel familiar to statisticians. Enrichment trials are currently rare, so an example trial, using simulated survival data, will demonstrate how these trials could perform in reality, and examine the advantages and disadvantages of such designs.
Introduction: For many RCTs the efficacy of a new treatment is accompanied by safety concerns. While overall results may demonstrate a favourable risk-benefit trade-off there may be individuals where the harm outweighs the benefit. Methods: We describe methods to predict the individual patient’s absolute benefit and risk based on multivariable models using patient baseline characteristic. Taking account of the relative clinical importance of the respective benefits and harms we develop an algorithm for clinical use whereby rapid decisions can be made on the preferred treatment strategy for each individual patient. Results: We illustrate this approach with findings from three major cardiovascular studies: 1) the SPRINT trial of intensive versus standard blood pressure lowering, where ischaemic benefits are accompanied by some major adverse events 2) the TIMI 50 trial of vorapaxar versus placebo post-myocardial infarction, where ischaemic benefits are accompanied by increased risk of major bleeding 3) a meta-analysis of 7 studies in coronary patients receiving a stent, with the goal of identifying which patients at high risk of bleeding need a shorter duration of effective dual anti-platelet drugs. Conclusions: Our findings illustrate how quantitative methods can help identify those individual patients for whom the risk of harms outweighs the benefits of a new treatment.
Successful and safe devolvement, licensing and marketing of medicines cannot happen without intense cooperation and dialogue between regulators and pharmaceutical companies. On the regulatory side, the Medicines and Healthcare products Regulatory Agency (MHRA) has decades of experience in medicines and medical devices regulation, during many of which statisticians have been deeply involved. Not only at the level of licensing of medicines, but also in the pharmacovigilance and medical device areas, statisticians play an important role in the Agency's activities, not forgetting their involvement in real-world data collection and analysis (CPRD) and characterisation, standardisation and control of biological medicines (NIBSC). In this talk you will learn about the MHRA, how we work, and the statistician's roles in the system, as well as some hints of current hot topics in regulatory statistics such as estimands and real-world data. You will also discover the types of interactions that can be held with regulators at the UK (MHRA) and European (EMA) levels, the different types of regulatory procedures and how statisticians from both sides of the table can contribute to such dialogue, ultimately helping their companies navigate through the regulatory system more smoothly.
Sparse relative effectiveness evidence is a common problem in Health Technology Assessment (HTA). For example, evidence on a paediatric population may be limited. Usually, in HTA, such indirect evidence is either included by ignoring any differences (`lumping`) or is completely disregarded (`splitting`). However, more sophisticated methods exist in the literature which, rather than `lumping` or `splitting`, impose more moderate, perhaps more appropriate, degrees of information-sharing. We developed network-meta analytic methods for the combination of, aggregate-level, binary, direct and indirect evidence. These can be categorized into functional-, exchangeability-based, prior-based and correlation-based relationships. The estimates produced with each method were subsequently used in a case-study that evaluated the cost-effectiveness and value of information of intravenous-immunoglobulin (IVIG) for adults with severe sepsis and septic shock.