Medidata Solutions, Inc.

Christopher Bound

Michael Elashoff

Joshua Hartman

Hrishikesh Karvir

Jingshu Liu

Fanyi Zhang

Glen De Vries

Michelle Marlborough

Valerio Aimale

Vladimir Tereshkov

Syed Haider

Ron Katriel

Jeevan Pendli

A system for predicting subject enrollment for a study includes a time-to-first-enrollment (TTFE) model and a first-enrollment-to-last-enrollment (FELE) model for each site in the study. The TTFE model includes a Gaussian distribution with a generalized linear mixed effects model solved with maximum likelihood point estimation or with Bayesian regression, and the FELE model includes a negative binomial distribution with a generalized linear mixed effects model solved with maximum likelihood point estimation or with Bayesian regression estimation.

System and method for predicting subject enrollment

A method and system for generating a master clinical database from disparate sources, including public databases, private databases, and data from users, correlating the received data, determining a degree of confidence in and requesting and seeking confirmation of any data matched from the correlated data. The invention may identify key time points and users from whom to seek and receive confirmation of correlated data. The invention may be utilized to create a cross-trial, cross-sponsor, clinical master database that maintains appropriate user, subject and data confidentiality and privacy and which may be utilized to facilitate study design collaboration.

Method and system for generating a master clinical database and uses thereof

A method for linking records (related to an entity) from separate databases may include extracting a first record from a first database as a first vector, extracting a second record from a second database as a second vector, generating first and second sub-vectors for the first and second vectors, where each sub-vector includes quality features from the respective vector, pre-processing the first and second sub-vectors using domain knowledge, calculating a distance assessment classifier based on the first and second sub-vectors, and determining whether the distance represented by the distance assessment classifier is greater than a threshold. If the distance is greater than the threshold, the records may be linked; if not, the method extracts additional records and repeats after generating first and second sub-vectors until the distance is greater than the threshold. A system for linking records is also disclosed.

Method and system for linking heterogeneous data sources

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Perkasie, PA, United States of America

Christopher Bound