Most clinical trials are designed to evaluate the relationships between single factors (eg treatment) and simple outcome measures. In today’s medical research, there is an increasing need for greater understanding and characterisation of effects of numerous factors on multiple outcome measures in heterogeneous patient populations. Clinical Trials are being asked to provide more information on safety, efficacy and long-term outcomes within the entire study population as well as within patient subgroups. Established statistical tools are limited in meeting this need.
We are developing software solutions that will enable the user to make better informed decisions during the design of smarter clinical trials. Created as an aid to optimise the design and analysis of clinical trials in a virtual environment, KERUS™ guides users through the design process via a simple intuitive interface. The software allows users to evaluate large numbers of virtual study design and analysis options using both real and user-defined data without the need for a technical knowledge of modelling.
KERUS™ is designed specifically to enable study simulations involving multiple correlated outcomes. This concept has broad application in clinical research including the design of studies for stratified medicine and subgroup analysis, biomarkers and surrogate endpoints, benefit-risk analysis, diagnostic development, health technology assessment and comparative effectiveness research.
The result is a statistically rigorous assessment of study design that has the power to answer multiple questions simultaneously. With KERUS™, we aim to offer an exceptional software tool that can ensure users have the best clinical study design to maximise the chances of success. It allows the user the confidence to ask more of their clinical trial.
KERUS™ – classically named in the spirit of opportunity – is an innovative tool for designing clinical trials, allowing quick convenient optimisation of a wide range of design parameters. The tool is designed to handle multifaceted questions that defeat traditional study design tools. By employing cutting edge simulation approaches the software allows the user to rapidly arrive at solutions to complex queries while providing the reassurance of rigorous statistical validation.
INTUITIVE USER-FRIENDLY INTERFACE: The user is led step by step through the process of defining critical factors affecting the study.
INTERACTIVE VISUALISATION OF RESULTS: The user is provided a series of tools to explore the simulated results to better understand the interaction of the parameters defined for the study.
RAPID REFINEMENT: The user is able to readily redefine the range of tested study parameters to enhance precision or increase simulation power to maximise confidence.
EXPORT OF SIMULATED DATA: The software allows the user to export the settings and data to give the user freedom to further explore, validate or audit the data.
KERUS™ provides the user with confidence in designing trials fit for the purpose of answering the complex and subtle questions being asked in biomedical science today. The output will provide key decision makers with the confidence required to make informed choices.
The software is optimised for:
HIGH DIMENSIONALITY: Many variables affect the performance of drugs, the progress of disease or the risk of adverse events occurring. Capture all of these in one simulation
INTER-RELATEDNESS: Just as no man is an island, few factors are truly independent. Capture the complex web of interactions between variables and bring these within the simulation for a more accurate picture of the situation.
STRATIFICATION: It is increasingly recognised that an intervention itself is not the only factor contributing to effectiveness, but also the unique combination of characteristics of each individual taking that intervention. Kerus™ is able to allow characterisation of multiple sub-groups and multiple treatment groups.
A phase II study of an experimental drug for Alzheimers Disease failed to meet its primary objective of showing improved treatment response relative to a placebo. Blood samples collected in the study were then used to perform a retrospective PGx investigation which also proved inconclusive. Using our simulation approach, we demonstrated that we could have improved the probability of success of the PGx study by more the 40% with better prospective planning of the design of the study and that this could have been achieved without any increase in its size and cost. This case study was published as a paper entitled “Pharmacogenetics: practices and opportunities for study design and data analysis” in the journal Drug Discovery Today in 2011.
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