Department of Quantitative Health Sciences

Our Goal: Your Research Success

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.

QHS: An Excellent Resource for You!

Here are just a few areas the department specializes in:

  • Clinical Trial Design
  • Biostatistics
  • Epidemiology
  • Statistical Genetics
  • Outcomes Research
  • Quality-of-life Assessment
  • Database Development

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).


Measurement error problems have attracted a great deal of interest in the past two decades. A variety of models and methods for the problems have been applied in scientific fields, such as medicine, economy, and astronomy. This paper (Wang and Ye) is motivated by a wide range of background correction problems in gene array data analysis, which refer to adjustments to the contaminated data intended to remove measurement error from the measured signal. Estimating the conditional density function from contaminated gene expression data, therefore, plays a key aspect of statistical inference and visualization here. We propose re-weighted deconvolution kernel methods to estimate the conditional density function in a general additive error model, when the error distribution is known as well as when it is unknown. Theoretical and numerical properties of the proposed estimators are comprehensively investigated. The new methodology could serve as an informative graphical and inferential tool for studying the relationship between the contaminated gene intensities and the unobserved true signals.


Comparative studies typically assess whether an exposure affects an outcome. However, just as important is to understand how. Mediation analysis attempts to quantify how much, if any, of the effect of an exposure on outcome goes though pre-specified mediator, or “mechanism”, which sits on the causal pathway between exposure and outcome.  Mediation is suggested when two conditions are true: exposure affects mediator and mediator (adjusting for exposure) affects outcome. A mediation analysis can validate or refute one’s original hypothesis and stimulate further research to modify mediators to improve patient outcomes. In this work Mascha et al discuss design and analysis of studies investigating mediation, including distinguishing mediation from confounding, identifying potential mediators when the exposure is chronic versus acute, and requirements for claiming mediation. Besides the simplest design with a single continuous mediator and outcome, we consider binary mediator and outcome, multiple mediators, multiple outcomes and mixed data types. Methods are illustrated with NSQIP data assessing the effects of pre-operative anemic status on post-operative outcomes through a set of intraoperative mediators. 

News Archive