Identification and evaluation of novel methods for the analysis of ordinal data in acute stroke trials in comparison to ordinal logistic regression

Identification and evaluation of novel methods for the analysis of ordinal data in acute stroke trials in comparison to ordinal logistic regression

Background Ordinal outcomes are common; however, selecting an appropriate analysis is often problematic. Acute stroke trials routinely measure dependency using an ordinal scale as the primary outcome. Historically trials have dichotomised ordinal scales to compare the proportion dependent across groups, which limits statistical power to detect an effect. The OAST Collaboration (2007) showed ord...

data mining rules and classification methods in insurance: the case of collision insurance

assigning premium to the insurance contract in iran mostly has based on some old rules have been authorized by government, in such a situation predicting premium by analyzing database and it’s characteristics will be definitely such a big mistake. therefore the most beneficial information one can gathered from these data is the amount of loss happens during one contract to predicting insurance ...

the analysis of the role of the speech acts theory in translating and dubbing hollywood films

از محوری ترین اثراتی که یک فیلم سینمایی ایجاد می کند دیالوگ هایی است که هنرپیش گان فیلم میگویند. به زعم یک فیلم ساز, یک شیوه متأثر نمودن مخاطب از اثر منظوره نیروی گفتارهای گوینده, مثل نیروی عاطفی, ترس آور, غم انگیز, هیجان انگیز و غیره, است. این مطالعه به بررسی این مسأله مبادرت کرده است که آیا نیروی فراگفتاری هنرپیش گان به مثابه ی اعمال گفتاری در پنج فیلم هالیوودی در نسخه های دوبله شده باز تولید...

Logistic Regression for Multivariate Longitudinal Ordinal Data

Comparison of Ordinal Response Modeling Methods like Decision Trees, Ordinal Forest and L1 Penalized Continuation Ratio Regression in High Dimensional Data

Background: Response variables in most medical and health-related research have an ordinal nature. Conventional modeling methods assume predictor variables to be independent, and consider a large number of samples (n) compared to the number of covariates (p). Therefore, it is not possible to use conventional models for high dimensional genetic data in which p > n. The present study compared th...