In Silico clinical research: keep it dynamic!
Running clinical trials takes time and it is expensive. One way to select the best strategies to be evaluated in clinical trials is to perform simulations using mathematical models that learnt with all available information from previous clinical trials and observational cohort studies. These simulations lead to in silico trials that should help for defining the next clinical trials to be performed. Rodolphe THIEBAUT will show examples in the context of HIV infection using dynamical models of the response to immune-interventions and antiretroviral therapies.
Cohort studies analyze a group of individuals over time until the development of some events. Even if this study approach is often used to describe the association between exposure to one or more factors and the risk of the event occurring, the interpretation of results from cohort studies is often limited by the presence of one or more biases.
For this reason, randomized controlled trials are believed to provide the most solid evidence when determining causal associations. Nevertheless, with due care in this study approach, the impact of some of these biases cannot be reduced. In this session, Doctor Caroline SABIN will describe some of these biases most commonly present in cohort studies and will discuss approaches to minimize their impact on study results.