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Publikováno v:
Injury. 52:S3-S6
Introduction Typically, a healthcare intervention is evaluated by comparing data before and after its implementation using statistical tests. Comparing group means can miss underlying trends and lead to erroneous conclusions. Segmented linear regress
Publikováno v:
Computational and Mathematical Methods in Medicine, Vol 2019 (2019)
Computational and Mathematical Methods in Medicine
Computational and Mathematical Methods in Medicine
Introduction. In retrospective studies, the effect of a given intervention is usually evaluated by using statistical tests to compare data from before and after the intervention. A problem with this approach is that the presence of underlying trends
Publikováno v:
Computational and Mathematical Methods in Medicine, Vol 2019 (2019)
Computational and Mathematical Methods in Medicine
Computational and Mathematical Methods in Medicine
Funder: SPRINT; doi: http://dx.doi.org/10.13039/100004730
Introduction. In healthcare, change is usually detected by statistical techniques comparing outcomes before and after an intervention. A common problem faced by researchers is distinguish
Introduction. In healthcare, change is usually detected by statistical techniques comparing outcomes before and after an intervention. A common problem faced by researchers is distinguish
Autor:
Thomas Edward Glover, Rajna Golubic, Epaminondas Markos Valsamis, Adnan Hussain, Amir-Reza Jenabzadeh, Henry Husband
Publikováno v:
Journal of surgical education. 75(1)
Objective Methods that model surgical learning curves are frequently descriptive and lack the mathematical rigor required to extract robust, meaningful, and quantitative information. We aimed to formulate a method to model learning that is tailored t