Exploristics offers a suite of innovative predictive modelling modules for use in the design and implementation of clinical trials.  The suite is built using a common analytic platform and has been validated using data from many trials.  It will be of benefit to pharmaceutical companies, CROs and others wishing to maximise the output of their trials whilst minimising costs.  To date, we have developed and applied modules for optimising Drug Supply and Patient Recruitment during clinical trials.


        Drug Supply Logistics Module

 

Large amounts of expensive drug are wasted due to inefficient supply planning. A solution is needed that accounts for the stochastic nature of recruitment, randomization and supply chain logistics.

 

Key Features

        Computes the amount of supply needed to cover patient demand in           a study with a given risk that a site may run out of stock

        Allows for different randomization schemes and treatment                       allocations, different distributions of local centres in regional depots,         different delivery times, etc.

Utilises multiple models including stochastic processes, patient

recruitment models, randomization processes, and approximations

Click here to download full paper

        Patient Recruitment Module


Patient recruitment is very costly and a well-recognized bottleneck in designing/monitoring clinical trials. More than 60% of studies fail to recruit in time and many are based on deterministic models for recruitment that do not account for uncertainties, variation or fluctuation in recruitment assumptions.


Key Features


       Computes the mean and confidence bounds for the number of

recruited patients over time and for the total recruitment time at any stage of the trial using interim data

        Evaluates the optimal number of centres needed to complete

recruitment in time with a given confidence

•      Utilises stochastic processes modelling, statistical estimation,

Bayesian re-estimation, asymptotic approximations, predictive               modelling.

Click here to download full paper

       Other Case Studies
» Identifying opportunities for Pharmacogenetics » Cross-platform biomarker analysis
» Evaluating surrogate endpoints » Analysis of QoL and PRO data