BACKGROUND AND PURPOSE: Ischemic stroke lesion volumes have proven difficult to analyze due to the extremely skewed shape of their underlying distribution. We introduce an extension of generalized linear models, beta regression, as a possible method of modeling extremely skewed distributions as evidenced in ischemic stroke lesion volumes. METHODS: The NINDS rt-PA clinical trials measured ischemic stroke lesion volume as a secondary trial outcome. Three-month lesion volumes from these trials were analyzed using beta regression. A multi-variable regression model associating explanatory variables with ischemic stroke lesion volumes was constructed using accepted model building strategies and compared with the previously published volumetric analysis. RESULTS: Beta regression produced a similar model when compared to the previous analysis published by the study group. All previously identified variables of importance were detected in the model building process. The age by treatment interaction described in previous studies was also found in this analysis, confirming the strong effect age has on stroke outcomes. Further, a treatment effect was elicited in terms of odds ratios, yielding a previously unknown quantification of the effect of rt-PA on lesion volumes. CONCLUSIONS: Beta regression proved adept in modeling ischemic stroke lesions and offered the interpretation of covariates in terms of odds ratios. Beta regression is seen as a legitimate alternative to analyze ischemic stroke volumes.
RCT Entities:
BACKGROUND AND PURPOSE:Ischemic stroke lesion volumes have proven difficult to analyze due to the extremely skewed shape of their underlying distribution. We introduce an extension of generalized linear models, beta regression, as a possible method of modeling extremely skewed distributions as evidenced in ischemic stroke lesion volumes. METHODS: The NINDS rt-PA clinical trials measured ischemic stroke lesion volume as a secondary trial outcome. Three-month lesion volumes from these trials were analyzed using beta regression. A multi-variable regression model associating explanatory variables with ischemic stroke lesion volumes was constructed using accepted model building strategies and compared with the previously published volumetric analysis. RESULTS: Beta regression produced a similar model when compared to the previous analysis published by the study group. All previously identified variables of importance were detected in the model building process. The age by treatment interaction described in previous studies was also found in this analysis, confirming the strong effect age has on stroke outcomes. Further, a treatment effect was elicited in terms of odds ratios, yielding a previously unknown quantification of the effect of rt-PA on lesion volumes. CONCLUSIONS: Beta regression proved adept in modeling ischemic stroke lesions and offered the interpretation of covariates in terms of odds ratios. Beta regression is seen as a legitimate alternative to analyze ischemic stroke volumes.
Authors: D G Altman; K F Schulz; D Moher; M Egger; F Davidoff; D Elbourne; P C Gøtzsche; T Lang Journal: Ann Intern Med Date: 2001-04-17 Impact factor: 25.391
Authors: T Brott; H P Adams; C P Olinger; J R Marler; W G Barsan; J Biller; J Spilker; R Holleran; R Eberle; V Hertzberg Journal: Stroke Date: 1989-07 Impact factor: 7.914
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Authors: Kimo C Stine; Elizabeth C Wahl; Lichu Liu; Robert A Skinner; Jaclyn VanderSchilden; Robert C Bunn; Corey O Montgomery; James Aronson; David L Becton; Richard W Nicholas; Christopher J Swearingen; Larry J Suva; Charles K Lumpkin Journal: J Orthop Res Date: 2016-02-26 Impact factor: 3.494
Authors: Kimo C Stine; Elizabeth C Wahl; Lichu Liu; Robert A Skinner; Jacquelyn Vanderschilden; Robert C Bunn; Corey O Montgomery; Larry J Suva; James Aronson; David L Becton; Richard W Nicholas; Christopher J Swearingen; Charles K Lumpkin Journal: J Orthop Res Date: 2013-11-20 Impact factor: 3.494