Christiana Care Center for Outcomes Research Christiana Care

- The Christiana Care Center for Outcomes Research (CCOR) is a multidisciplinary research group within the Christiana Care Health System, with expertise in clinical medicine, epidemiology, biostatistics and informatics. - CCOR scientists have broad research experience in creating, running and analyzing data from registries, running clinical trials, health status assessment and cost-effectiveness analysis. - CCOR statistical capabilities include standard descriptive and inferential statistics, general and generalized linear models, survival analysis, and multivariate analysis. In addition, CCOR biostatisticians have expertise in more advanced statistical methods such as data mining methods, propensity score methods, multiple imputation of missing data, cluster analysis, structural equation modeling, survival analysis for multiple events, latent growth curve modeling, cost-effectiveness analysis, both simulation with Markov modeling and patient level stochastic analysis, and Bayesian sensitivity analysis for cost-effectiveness models. CCOR Mission - To promote and conduct research aimed at improving patient care and/or informing health care policy - To provide service and education in broad areas of clinical, epidemiologic, translational, and outcomes research

A Delware INBRE Core Facility



Christiana Care- Center for Outcomes Research
131 Continental Dr. Suite 202
Newark, DE 19713


T. Jurkovitz, MD, MPH
Director of Operations

Paul Kolm, PhD
Director of Biostatistics


1) Study design and statistical analysis - Initial conceptualization and design of a research or performance improvement study - Sampling and randomization strategies - Questionnaire construction - Determination of required sample size for a valid study - Appropriate statistical analysis to address the research hypothesis. - Description of methods and presentation of results for reports or publications with supporting tables and/or graphs necessary to communicate the results. - Assistance with writing the statistical analytical plan and/or data management plan for grants submission purposes 2) Data management for prospective studies - Creation of data collection tools - Data entry - Database design for management and analytical purposes - Data management (storage, transformation, quality control, reports, export of files) for prospective studies - Integration of data from heterogeneous sources and systems 3) Merging various databases to build analytical datasets using deterministic or probabilistic methods