409 research outputs found
On pseudo-values for regression analysis in competing risks models
For regression on state and transition probabilities in multi-state models Andersen et al. (Biometrika 90:15-27, 2003) propose a technique based on jackknife pseudo-values. In this article we analyze the pseudo-values suggested for competing risks models and prove some conjectures regarding their asymptotics (Klein and Andersen, Biometrics 61:223-229, 2005). The key is a second order von Mises expansion of the Aalen-Johansen estimator which yields an appropriate representation of the pseudo-values. The method is illustrated with data from a clinical study on total joint replacement. In the application we consider for comparison the estimates obtained with the Fine and Gray approach (J Am Stat Assoc 94:496-509, 1999) and also time-dependent solutions of pseudo-value regression equation
SmoothHazard:An R package for fitting regression models to interval-censored observations of illness-death models
The irreversible illness-death model describes the pathway from an initial state to an absorbing state either directly or through an intermediate state. This model is frequently used in medical applications where the intermediate state represents illness and the absorbing state represents death. In many studies, disease onset times are not known exactly. This happens for example if the disease status of a patient can only be assessed at follow-up visits. In this situation the disease onset times are interval-censored. This article presents the SmoothHazard package for R. It implements algorithms for simultaneously fitting regression models to the three transition intensities of an illness-death model where the transition times to the intermediate state may be interval-censored and all the event times can be right-censored. The package parses the individual data structure of the subjects in a data set to find the individual contributions to the likelihood. The three baseline transition intensity functions are modelled by Weibull distributions or alternatively by M -splines in a semi-parametric approach. For a given set of covariates, the estimated transition intensities can be combined into predictions of cumulative event probabilities and life expectancies
A competing risks approach for nonparametric estimation of transition probabilities in a non-Markov illness-death model
Competing risks model time to first event and type of first event. An example
from hospital epidemiology is the incidence of hospital-acquired infection,
which has to account for hospital discharge of non-infected patients as a
competing risk. An illness-death model would allow to further study hospital
outcomes of infected patients. Such a model typically relies on a Markov
assumption. However, it is conceivable that the future course of an infected
patient does not only depend on the time since hospital admission and current
infection status but also on the time since infection. We demonstrate how a
modified competing risks model can be used for nonparametric estimation of
transition probabilities when the Markov assumption is violated
Decline in Danish use of oral tranexamic acid with increasing use of the levonorgestrel-releasing intrauterine system:A nationwide drug utilization study
Evaluating the predictive performance of malaria antibodies and <i>FCGR3B</i> gene polymorphisms on <i>Plasmodium falciparum</i> infection outcome:a prospective cohort study
Perceived Stress and Social Networks among Young Adults:Measuring Social Interactions with Mobile data and Self-reports
- …