… framework in which to view proportional and non-proportional hazards regression. The
essential idea is that of prediction, a feature common to all regression models, but one that some…

… determine whether a fitted Cox regression model adequately describes the data. … proportional
hazards; for influential data; and for nonlinearity in the relationship between the log hazard …

… Their method does not assume linearity or proportional hazards. Hazard regression can be
… hazard regression in Figure 20.11). However, the bias-corrected calibration (blue curve from …

… If the timescale is genuinely discrete, we can replace the proportional hazards part of our
model by a discrete logistic model. See Cox & Oakes (1984, § 7.6) for details. We end this …

… ) in a proportional hazards regression analysis was evaluated … of the simulated proportional
hazards analyses and discuss … After a proportional hazards analysis was then done for each …

Researchers measured the effect on one year mortality of secondary drug prevention for
patients with stroke in routine primary care. They used a cohort study design, which …

… Residuals are defined for the proportional hazards regression model introduced by Cox (1972).
These residuals can be plotted against time to test the proportional hazards assumption. …

… The Cox proportional hazards model has enjoyed much attention in the literature and hence
we present here … We also present a similar overview of the new regression tree technique. …

… systematically proportional hazards estimates and cumulative logistic regression estimates
… follow-up period, censoring, non-proportional hazards, and sample size. A second purpose …

… the hazard function … hazards that is constant and depends on their covariate values.
Thismodel is usually called either the Cox regression model or theproportional-hazards regression …