NCI Homogeneity Analysis Grant FDA Data Mining Grant NCI Permutation Test Grant Research Interests Lincoln conducts an active program of research and development in medical informatics. Our focus is on taking advantage of emerging software technologies to create practical solutions to important problems in healthcare. Worldwide government regulatory agencies have assembled large databases of spontaneously reported adverse drug reactions. The databases are difficult to interpret and analyze because the data is of inconsistent quality and because it is not linked to population statistics on drug exposure.
Present work is focused on validation of the approach.
This is attributable in large measure to the lack of a consistent approach for describing and representing data from trials. Recent advances in clinical trials ontology, medical nomenclatures, and metadata frameworks have created a favorable climate for development of the model. The goal of this proposed project is to create a structured document technology for drug labels that will enable computerized processing of the information content in support of tasks such as scientific review, post-marketing risk assessment, and patient care. Most hypothesis tests used for analyzing clinical trials assume a population model for statistical inference, when in fact a randomization model is more consistent with the way randomized clinical trials are actually conducted. Failure to consider the randomization model when analyzing clinical trials can lead to effective drugs being declared ineffective, and ineffective drugs being declared effective.