An abstract for Simulo
Background: Clinical trial simulation is a powerful tool in the optimization of study designs and for making model-based inference. However, including all various aspects of the model, the target population and the experimental design (e.g. model uncertainty, demographic differences, dose adjustments, compliance, dropout, multiple biomarkers and therapeutic/adverse clinical outcome) may be complex and time-consuming. Adequate post-processing, integration and visualisation of the simulated data, needed to convince decision-makers, is not always a trivial task.
Aim: Simulo was developed to offer a clear framework and user-friendly interface with the ability to simulate and visualise the likely outcome of clinical studies or dosing scenarios, using public or custom-developed nonlinear mixed-effects models.
Methods: Simulo is a Java-based application running on an R backend with a graphical user interface. Simulations are performed after automatic translation of a user defined model and study protocol to R code, followed by efficient execution of the code. The model can be described through algebraic, ordinary and/or delayed differential equations. Model parameters can include different levels of variability (uncertainty around population parameters, covariate distributions, IIV & IOV). This variability can be sampled from non-correlated parametric (e.g. constant, uniform, normal, lognormal, logit, logistic, Poisson, negative binomial), correlated parametric and discrete distributions. Parameters can even be bootstrapped from existing databases (sequentially or at random, with or without replacement, independently or jointly). Inclusion criteria, treatments (route of administration, dosing schedule), observations (type and sampling schedule) and study design (e.g. lead-in phase, parallel, cross-over Latin-square designs) are easily defined. If and whenever desired, the user may directly modify the R code and include various R-packages as fit for purpose. Moreover, the resulting simulation code may always be executed outside of Simulo in any standard R environment.
Results: Recent applications of Simulo show how simulations can help in predicting the effect of dose adaptation on biomarkers and clinical endpoints. Other examples include power calculations, comparison of different study designs and data analysis approaches for biological products.
Conclusions: Simulo offers a valuable platform for all sorts of model-based simulation activities, both for experienced and non-technical users. As any empirical or advanced model may be applied in simulation scenarios of all complexities, Simulo can facilitate both internal and regulatory decision making in a clear and visually attractive way. In addition, this flexible platform may enhance the collaborative spirit of any project team and is suitable for educational purposes.