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.
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Numerous studies have examined clinical outcomes associated with immunosuppression regimens among kidney transplant recipients. Re-transplant recipients have unique risk profile associated with immunological condition and experience with a failed graft. Schold et al. used a propensity-score analysis in order to attenuate potential selection bias for allocation of immunosuppression and evaluated the association of induction therapy for re-transplant recipients using a national registry cohort. Findings illustrated a variable set of complications associated with different induction therapies but similar overall patient survival. The results may inform clinical decision-making and potentially illustrate mechanisms of the effect of induction therapy in this population.
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.