Predicting the real world effectiveness and safety of medical interventions

Predicting the real world effectiveness and safety of medical interventions

For many diseases, there are multiple available medical interventions to choose from when treating a patient. The comparison of competing treatments is usually performed in randomized controlled trials (RCTs). Despite their many merits, RCTs usually employ very strict inclusion criteria. Thus, their generalizability to less controlled, ‘real-world' clinical settings might be limited. Moreover, a treatment that performs very well on average might not be very effective for specific types of patients, and choosing the best treatment for each patient is of crucial importance. Currently available approaches to personalizing the choice of treatment have not fully explored how different types of evidence (e.g. data from RCTs, patient registries, etc) can be jointly used to answer this question. This project aims to fill this gap. We aim to develop novel statistical methods for personalizing the choice of treatment. These new methods will utilize information on the individual characteristics of patients found in every-day clinical practice. We will include evidence from multiple randomized clinical trials, as well as data collected under real-life clinical conditions. Our objective is to develop a range of methods for making patient-specific predictions about the effects of medical interventions, in order to enhance the decision-making process in every-day clinical settings.Our work will help clinical practice move away from the one-size-fits-all approach to treating patients, by using evidence-based methods to choose the best treatment for each particular patient. The output of this project is expected to be of high importance to research scientists and methodologists, but also to health-care professionals, patients, guideline developers and the pharmaceutical industry.

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