01.Association05.Time to event - sporedata/researchdesigneR Wiki

1. Use cases: in which situations should I use this method?

2. Input: what kind of data does the method require?

3. Algorithm: how does the method work?

Model mechanics

Describing in words

Multi-state models work in different scenarios: Figure 1

Describing in images

Describing with code

Breaking down equations

Suggested companion methods

Learning materials

  1. Books

  2. Articles

4. Output: how do I interpret this method's results?

Typical tables and plots and corresponding text description



Survival trees offer a relatively flexible approach to understanding the effects of covariates, including their interaction, on survival times when the association's functional form is unknown. Survival trees have the advantage of a more straightforward interpretation.

A survival tree is typically constructed based on the heterogeneity in time-to-event distribution. However, ignorance of censoring distribution might lead to inconsistent survival trees in some applications with marker-dependent censoring. For instance when less education or better prognosis might lead to early censoring.

The SurvCART algorithm 6(#6) is flexible in constructing a survival tree based on heterogeneity in time-to-event and censoring distribution. However, it is essential to emphasize that censoring heterogeneity in the construction of survival trees is optional.

Associated concepts

  1. Time until something or some event happens.
  2. It is possible to account for people who are lost to follow up.

Associated concepts

Reporting guidelines for Methods

5. SporeData-specific


Data science functions

 * sdatools::kaplanMeierPlot


[1] Vandenbroucke JP, Von Elm E, Altman DG, Gøtzsche PC, Mulrow CD, Pocock SJ, Poole C, Schlesselman JJ, Egger M, Strobe Initiative. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. PLoS Med. 2007 Oct 16;4(10):e297.
[2] Methods strobe template
[3] survCurve: Plots Survival Curves Element by Element
[4] Multi-state models and competing risks
[5] Creating a survival-swimmer plot in R
[6] SurvCART: Constructing Survival Tree in R