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The Department of Quantitative Health Sciences has expertise in all aspects of clinical research. From study design to statistical analysis to preparing funding applications, we will help you and your department achieve sound scientific results from your research project in a timely manner. Each year, we co-author hundreds of publications and receive millions of dollars in external funding. We have the knowledge and skills to partner with each Cleveland Clinic Institute.
Cleveland Clinic has its own team of biostatisticians, epidemiologists, outcomes researchers, database developers and programmers in the Department of Quantitative Health Sciences. Our pledge is to be better, faster, and/or less expensive than any research group that operates outside Cleveland Clinic. To find out more about how we can serve you, try our Skill Finder.
Here are just a few areas the department specializes in:
The Department is available to all Cleveland Clinic physicians, researchers, and support staff on a pay-as-you-go or dedicated-FTE fee basis. Do you need help training staff for an upcoming research project? We will teach your residents, fellows, medical students and support team about conducting clinical studies, efficient data collecting methods, and other important research skills.
Read more in our department brochure (PDF).
In evaluating the accuracy of diagnostic imaging tests, multi-reader
studies are often performed. These studies characterize the performance
of a sample of readers and allow unbiased comparisons between competing
diagnostic tests. To date, most multi-reader imaging studies have used
a fully-crossed design where each reader interprets each image in the study;
however, these studies require that each reader interpret many images,
and the number of required interpretations often becomes a limiting factor
in the study. Split-plot designs have been proposed recently for multi-reader
studies. These studies can greatly reduce the number of interpretations
required of each reader. Obuchowski et al (Acad Radiol, 2012; 19: 1508-1517)
present and compare three statistical methods for analyzing data from a
split-plot multi-reader imaging study. They illustrate the new methods
with a 36-reader, 200-patient split-plot study of breast cancer detection. Read more here.
Binary prediction models – that is, statistical models intended to measure absolute risk of a dichotomous disease status or outcome variable – are used widely in clinical care and biomedical research settings. These risks are commonly expressed as a predicted probability. A fundamental issue involved with predicted probabilities is whether or not patients with, say, a 20% predicted probability for the event actually express a 20% incidence of the event (and whether or not the same is true across all predicted probabilities). This is known in the statistical literature as model calibration. Jarrod Dalton recently developed a flexible recalibration methodology which will correct predicted probabilities for miscalibrations based on a user-specified logistic regression model structure. Read more here.