In most event history models, the effect of a covariate on the hazard is assumed to have a log-linear functional form. For continuous covariates, this assumption is often violated as the effect is highly nonlinear. Assuming a log-linear functional form when the nonlinear form applies causes specification errors leading to erroneous statistical conclusions. Scholars can, instead of ignoring the ...

5 Linear models and populations 6 5.1 Case weights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 5.2 Categorical predictors and contrasts . . . . . . . . . . . . . . . . . . . . . . . . . 9 5.3 Different codings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 5.4 Sums of squares and projections . . . . . . . . . . . . . . . . . . . . . ...

4 Linear models and populations 5 4.1 Case weights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 4.2 Categorical predictors and contrasts . . . . . . . . . . . . . . . . . . . . . . . . . 8 4.3 Different codings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.4 Sums of squares and projections . . . . . . . . . . . . . . . . . . . . . ...

background: cox proportional hazard (cph) model is the most widely used model for survival analysis. when there are unobserved/unmeasured individuals factor, then the results of the cox proportional hazard model may not be reliable. the purpose of this study was to compare the results of cph and frailty models in breast cancer (bc) patients. methods: a historical cohort study was carried out us...

background : the aim of this study was to predict the survival rate of iranian gastric cancer patients using the cox proportional hazard and artificial neural network models as well as comparing the ability of these approaches in predicting the survival of these patients. methods: in this historical cohort study, the data gathered from 436 registered gastric cancer patients who have had surgery...

Background: In many studies, Cox regression was used to assess the important factors that affect the survival of cancer patients based on demographic and clinical variables. The aim of this study was to determine the factors affecting the survival of patients with Hodgkin's lymphoma using the random survival forest (RSF) method and compare it with the Cox model. Methods: In this retrospective ...