%0 Journal Article %1 Feuer:1992:Stat-Med:1609178 %A Feuer, E J %A Hankey, B F %A Gaynor, J J %A Wesley, M N %A Baker, S G %A Meyer, J S %D 1992 %J Stat Med %K SurvivalAnalysis statistics %N 4 %P 455-474 %R 10.1002/sim.4780110408 %T Graphical representation of survival curves associated with a binary non-reversible time dependent covariate %U https://www.ncbi.nlm.nih.gov/pubmed/1609178 %V 11 %X The use of time dependent covariates has allowed for incorporation into analysis of survival data intervening events that are binary and non-reversible (for example, heart transplant, initial response to chemotherapy). We can represent this type of intervening event as a three-state stochastic process with a starting state (S), an intervening state (I), and an absorbing state (D), which usually represents death. In this paper we present three procedures for calculating survivorship functions which attempt to display the prognostic significance of the time dependent covariate. The first method compares survival from baseline for the two possible paths through the stochastic process; the second method compares overall survival to survival with state I removed from the process; and, the third method compares survival for those already in state I at a landmark time x to those in state S at time x who will never enter state I. We develop discrete hazard estimates for the survival curves associated with the three methods. Two examples illustrate how these methods can yield different results and in which situations one might employ each of the three methods. Extensions to applications with reversible binary time dependent covariates and models with both baseline and time dependent covariates are suggested.

@article{Feuer:1992:Stat-Med:1609178, abstract = {The use of time dependent covariates has allowed for incorporation into analysis of survival data intervening events that are binary and non-reversible (for example, heart transplant, initial response to chemotherapy). We can represent this type of intervening event as a three-state stochastic process with a starting state (S), an intervening state (I), and an absorbing state (D), which usually represents death. In this paper we present three procedures for calculating survivorship functions which attempt to display the prognostic significance of the time dependent covariate. The first method compares survival from baseline for the two possible paths through the stochastic process; the second method compares overall survival to survival with state I removed from the process; and, the third method compares survival for those already in state I at a landmark time x to those in state S at time x who will never enter state I. We develop discrete hazard estimates for the survival curves associated with the three methods. Two examples illustrate how these methods can yield different results and in which situations one might employ each of the three methods. Extensions to applications with reversible binary time dependent covariates and models with both baseline and time dependent covariates are suggested.}, added-at = {2019-11-18T07:47:00.000+0100}, author = {Feuer, E J and Hankey, B F and Gaynor, J J and Wesley, M N and Baker, S G and Meyer, J S}, biburl = {https://www.bibsonomy.org/bibtex/24f36d156f8496a35d9438702f3057fa1/jkd}, description = {Graphical representation of survival curves associated with a binary non-reversible time dependent covariate. - PubMed - NCBI}, doi = {10.1002/sim.4780110408}, interhash = {ccd3db8dced54611e5a9f3b6873963d9}, intrahash = {4f36d156f8496a35d9438702f3057fa1}, journal = {Stat Med}, keywords = {SurvivalAnalysis statistics}, month = feb, number = 4, pages = {455-474}, pmid = {1609178}, timestamp = {2019-11-18T07:47:00.000+0100}, title = {Graphical representation of survival curves associated with a binary non-reversible time dependent covariate}, url = {https://www.ncbi.nlm.nih.gov/pubmed/1609178}, volume = 11, year = 1992 }