Comparison of Cox's and relative survival models when estimating the effects of prognostic factors on disease-specific mortality: a simulation study under proportional excess hazards.

In many prognostic studies focusing on mortality of persons affected by a particular disease, the cause of death of individual patients is not recorded. In such situations, the conventional survival analytical methods, such as the Cox's proportional hazards regression model, do not allow to discriminate the effects of prognostic factors on disease-specific mortality from their effects on all-causes mortality. In the last decade, the relative survival approach has been proposed to deal with the analyses involving population-based cancer registries, where the problem of missing information on the cause of death is very common.

A relative survival regression model using B-spline functions to model non-proportional hazards.

Relative survival, a method for assessing prognostic factors for disease-specific mortality in unselected populations, is frequently used in population-based studies. However, most relative survival models assume that the effects of covariates on disease-specific mortality conform with the proportional hazards hypothesis, which may not hold in some long-term studies. To accommodate variation over time of a predictor's effect on disease-specific mortality, we developed a new relative survival regression model using B-splines to model the hazard ratio as a flexible function of time, without having to specify a particular functional form.

Assessing time-by-covariate interactions in relative survival models using restrictive cubic spline functions.

Background: The Cox model is widely used in the evaluation of prognostic factors in clinical research. However, in population-based studies, which assess long-term survival of unselected populations, relative-survival models are often considered more appropriate. In both approaches, the validity of proportional hazards hypothesis should be evaluated.

Modelling time-dependent hazard ratios in relative survival: application to colon cancer.

The Cox model is widely used in the evaluation of prognostic factors in clinical research. In population-based studies, however, which assess long-term survival of unselected populations, relative survival models are often considered more appropriate. In both approaches, the validity of proportional hazard hypothesis should be evaluated.

Stroke patterns in cardio-embolic infarction in a population-based study.

The study demonstrates that clinical-radiological causes and outcome of cardio-embolic infarcts in a population-based study correspond to a well-identified stroke pattern. Cardio-embolic infarcts was diagnosed in 882 cases (37.9%) of 2,330 consecutive first-ever stroke patients included in a prospective population-based stroke registry over a 14-year period (1985-1997).