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).
We propose a nonparametric procedure to describe the progression of longitudinal cohorts over time, leading to multi-state probability curves with the states defined jointly by survival and longitudinal outcomes measured with error. To account for the challenges of informative dropout and nonlinear shapes of the longitudinal trajectories, a bias corrected penalized spline regression is applied to estimate the unobserved longitudinal trajectory for each subject. The multi-state probability curves are then estimated based on the survival data and the estimated longitudinal trajectories. Simulation Extrapolation (SIMEX) is further used to reduce the estimation bias caused by the randomness of the estimated trajectories. We present theoretical justification of the estimation procedure along with a simulation study to demonstrate finite sample performance. The procedure is illustrated by data from the African American Study of Kidney Disease and Hypertension, and it can be widely applied in longitudinal studies. Click here for more.
In many neuroscience studies, multidimensional outcomes of different natures
are obtained simultaneously from multiple modalities. A joint modeling
approach is presented to model the multidimensional outcomes together, which
allows us to not only estimate the covariate effects but also evaluate the
strength of association among the multiple responses from different
modalities. A simulation study is conducted to quantify the possible
benefits by the new approach in finite sample situations. An analysis of neurophysiology data is illustrated with the use of the proposed method.
Click here for
more.
Lerner Research Institute
Cleveland Clinic, Mail Code NB21
9500 Euclid Avenue
Cleveland, Ohio 44195
