Tutorial in biostatistics: competing risks and multi‐state models

By H. Putter, M. Fiocco, Ronald B. Geskus. Statistics in Medicine, 2007

2,387 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c4f557cd5d9543f54ef1c9f157adf9db64b85e13

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

Citations in the corpus

The mstate package for estimation and prediction in non- and semi-parametric multi-state and competing risks models (2010, background): “Another relaxation is to include the time of entry in a previous or the current state as a time-dependent covariate in the model, leading to the category of what are called state arrival extended Markov models in [1].”
Machine Learning for Survival Analysis: A Survey (2017, background): “To avoid the questionable interpretation problem, the cumulative incidence curve [Putter et al. 2007] is one of the main approaches for competing risks which estimates the marginal probability of each event q .”
mstate: An R Package for the Analysis of Competing Risks and Multi-State Models (2011, background): “A more general introduction into competing risks and multi-state models can be found in Putter et al. (2007).”
Practical recommendations for reporting Fine‐Gray model analyses for competing risk data (2017, methodology): “When using a cause‐specific hazard model in the presence of competing risks, it is incorrect to infer that a given variable is associated with an increased or decreased incidence of the event of interest, as formula 1 does not hold in the presence of competing risks.(5,6) This is because one must ac”
Prognostic Models With Competing Risks: Methods and Application to Coronary Risk Prediction (2009, methodology): “A tutorial for competing risks analyses has recently been published.(9) The observable data in competing risks models are represented by the (possibly censored) time to the event T and the cause of failure D, ie, either the event of interest (D 1, eg, “CHD”) or the competing event (D 2, eg, “non-CHD”
Focus on : Contemporary Methods in Biostatistics ( I ) Regression Modeling Strategies (2017, background): “In a different setting, when the aim is to describe a process in which a subject moves through a series of states in continuous time, Multistate Markov modeling becomes the right analytical tool.(17,18) The natural history of many chronic diseases can be represented by a series of successive stages,”
Developing clinical prediction models: a step-by-step guide (2024, neutral): “65 A simpler approach would be to analyse a composite outcome.”
A predictive model for progression of chronic kidney disease to kidney failure. (2011, methodology): “The quantities under the summation denote the instantaneous hazard of kidney failure at event time ti and survival rate from kidney failure or death past event time ti–1.(26) All statistical analyses were performed using SAS version 9.”
Causes of Death in HIV-1–Infected Patients Treated with Antiretroviral Therapy, 1996–2006: Collaborative Analysis of 13 HIV Cohort Studies (2010, positive): “because of competing causes of death [16].”