TL24: Applications of Multidimensional Time Model for Probability Cumulative Function to Biopharmaceutical Industry
*Michael Fundator, National Academies, DBASSE 

Keywords: finite-dimensional time model, turnover problem, associated random variables.

In time of focus on precision medicine, and new stage in drug developments, this is a perfect reason for applications of new method based on changes of Cumulative Distribution Function in relation to time change in sampling patterns, in which Multidimensional Time Model for Probability Cumulative Function can be reduced to finite-dimensional time model, which can be characterized by Boolean algebra for operations over events and their probabilities and index set for reduction of infinite dimensional time model to finite number of dimensions of time model considering the fractal-dimensional time that is arising from alike supersymmetrical properties of probability, in the directions of multidimensional data analysis, modeling, and simulation. The new method that is based on properties of Brownian motion, philosophically based on Erdos- Renyi Law for the prediction and philosophically intended to reach high level of precision. Achieving the goal of precision medicine new method is very important after the challenges posed by both, the availability of big data and complex data structures, including missing and sparse data, and complex dependence structures, such as various DNA analyses, considering, that there are more than 80 million genetic variations are currently counted in the human genome, is changing the scope of analysis of statistical and medical experts from academia, industry and government. These applications could be further extended in view of decision-theoretical approach to risk assessment in evaluation of large amounts of clinical data and analysis for the support of approval of drugs and devices by Pharmaceutical and biotechnology companies that are kept confidential.