688 research outputs found
Analyzing Competing Risk Data Using the R timereg Package
In this paper we describe flexible competing risks regression models using the comp.risk() function available in the timereg package for R based on Scheike et al. (2008). Regression models are specified for the transition probabilities, that is the cumulative incidence in the competing risks setting. The model contains the Fine and Gray (1999) model as a special case. This can be used to do goodness-of-fit test for the subdistribution hazardsâ proportionality assumption (Scheike and Zhang 2008). The program can also construct confidence bands for predicted cumulative incidence curves. We apply the methods to data on follicular cell lymphoma from Pintilie (2007), where the competing risks are disease relapse and death without relapse. There is important non-proportionality present in the data, and it is demonstrated how one can analyze these data using the flexible regression models.
The Liability Threshold Model for Censored Twin Data
Family studies provide an important tool for understanding etiology of
diseases, with the key aim of discovering evidence of family aggregation and to
determine if such aggregation can be attributed to genetic components.
Heritability and concordance estimates are routinely calculated in twin studies
of diseases, as a way of quantifying such genetic contribution. The endpoint in
these studies are typically defined as occurrence of a disease versus death
without the disease. However, a large fraction of the subjects may still be
alive at the time of follow-up without having experienced the disease thus
still being at risk. Ignoring this right-censoring can lead to severely biased
estimates. We propose to extend the classical liability threshold model with
inverse probability of censoring weighting of complete observations. This leads
to a flexible way of modeling twin concordance and obtaining consistent
estimates of heritability. We apply the method in simulations and to data from
the population based Danish twin cohort where we describe the dependence in
prostate cancer occurrence in twins
Survival Analysis using S
Abstracts not available for BookReview
Time-varying effects when analysing customer lifetime duration, application to the insurance market
The Cox model (Cox, 1972) is widely used in customer lifetime duration research, but it assumes that the regression coefficients are time invariant. In order to analyse the temporal covariate effects on the duration times, we propose to use an extended version of the Cox model where the parameters are allowed to vary over time. We apply this methodology to real insurance policy cancellation data and we conclude that the kind of contracts held by the customer and the concurrence of an external insurer in the cancellation influence the risk of the customer leaving the company, but the effect differs as time goes by.Cox model, customer lifetime.
Analyzing Competing Risk Data Using the R timereg Package
In this paper we describe flexible competing risks regression models using the comp.risk() function available in the timereg package for R based on Scheike et al. (2008). Regression models are specified for the transition probabilities, that is the cumulative incidence in the competing risks setting. The model contains the Fine and Gray (1999) model as a special case. This can be used to do goodness-of-fit test for the subdistribution hazards’ proportionality assumption (Scheike and Zhang 2008). The program can also construct confidence bands for predicted cumulative incidence curves.We apply the methods to data on follicular cell lymphoma from Pintilie (2007), where the competing risks are disease relapse and death without relapse. There is important non-proportionality present in the data, and it is demonstrated how one can analyze these data using the flexible regression models
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