Survival

This R package implements the conditional survival function described in ‘Nonparametric bivariate estimation for successive survival times’ (C. Serrat and G. Gómez, 2007).

Clinical trial designs with composite endpoints

R package to calculate the required sample size in randomized clinical trials with composite endpoints. This package also includes functions to calculate the probability of observing the composite endpoint and the expected effect on the composite endpoint, among others.

Survival analysis

Method to implement some newly developed methods for the estimation of the conditional survival function.

Datos censurados en un intervalo

Pruebas de hipótesis para datos censurados por la derecha y en un intervalo.

Survival analysis

Generation of survival data with one (binary) time-dependent covariate. Generation of survival data arising from a progressive illness-death model.

Bondad de ajuste de modelos paramétricos

Futuro paquete de R (pronto disponible en CRAN) con funciones para estudiar la bondad de ajuste de modelos parámetricos mediante gráficos y pruebas estadísticas.

Análisis de la supervivencia

Ajuste de modelos de supervivencia para eventos recurrentes con censura por la izquierda

Survival analysis

Provides flexible hazard ratio curves allowing non-linear relationships between continuous predictors and survival. To better understand the effects that each continuous covariate has on the outcome, results are ex pressed in terms of hazard ratio curves, taking a specific covariate value as reference. Confidence bands for these curves are also derived.

multi-state models

Newly developed methods for the estimation of several probabilities in an illness-death model. The package can be used to obtain nonparametric and semiparametric estimates for: transition probabilities, occupation probabilities, cumulative incidence function and the sojourn time distributions. Additionally, it is possible to fit proportional hazards regression models in each transition of the Illness-Death Model. Several auxiliary functions are also provided which can be used for marginal estimation of the survival functions.

Design of phase III trials with long-term survival outcomes based on short-term binary results

Sample size and effect size calculations for survival endpoints based on mixture survival-by-response model

Análisis de la supervivencia

Simulación de datos de supervivencia complejos