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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.
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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.
There are numerous factors which explain processes of healthcare delivery and outcomes beyond traditional clinical characteristics. Emerging research suggests that factors such as behavioral characteristics, environmental hazards, mental health and socioeconomic status may have a marked impact on patient outcomes independent of clinically defined medical condition. Using data from numerous national registries and surveys, we sought to quantify the effect of the prevalence of underlying risks in patients' communities on outcomes for kidney transplant candidates in the United States. Findings suggest that independent of known clinical characteristics, there is a dose-response relationship of the level of community risk with outcomes for transplant candidates in the United States including mortality and likelihood to receive a transplant. These findings (summarized in the September, 2013 edition of the American Journal of Transplantation here) may have important implications for developing interventions as well as measuring the quality of care of providers who treat a high proportion of patients from high risk communities.