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Statistical considerations for outcomes in clinical research: A review of common data types and methodology
Matthew P Smeltzer1, Meredith A Ray1
1Division of Epidemiology, Biostatistics, and Environmental Health, School of Public Health, The University of Memphis, Memphis, TN 38152, USA.
Abstract:
With the increasing number and variety of clinical trials and observational data analyses, producers and consumers of clinical research must have a working knowledge of an array of statistical methods. Our goal with this body of work is to highlight common types of data and analyses in clinical research. We provide a brief, yet comprehensive overview of common data types in clinical research and appropriate statistical methods for analyses. These include continuous data, binary data, count data, multinomial data, and time-to-event data. We include references for further studies and real-world examples of the application of these methods. In summary, we review common continuous and discrete data, summary statistics for said data, common hypothesis tests and appropriate statistical tests, and underlying assumption for the statistical tests. This information is summarized in tabular format, for additional accessibility.
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