TestSurvRec (version 1.2.1)

Dif.Surv.Rec: This function computes statistical difference between two survival curves

Description

p-values of these tests are computed.

Usage

Dif.Surv.Rec(XX, type, alfa, beta,gamma,eta)

Arguments

XX

Object type recurrent events data

type

"LRrec","Grec","TWrec","PPrec","PMrec","FHrec","CMrec","Mrec","all"

alfa

The appropriate choice, see $w_{z}$. Defect value is equal zero

beta

The appropriate choice, see $w_{z}$. Defect value is equal zero

gamma

The appropriate choice, see $w_{z}$. Defect value is equal zero

eta

The appropriate choice, see $w_{z}$. Defect value is equal zero

Value

• # Dif.Surv.Rec(TBCplapyr,"all",0,0,0,0). Values returned lll{ Nomb.Est Chi.square p.value LRrec 0.3052411 0.5806152 Grec 1.4448446 0.2293570 TWrec 0.9551746 0.3284056 PPrec 1.1322772 0.2872901 PMrec 1.1430319 0.2850126 PPrrec 1.1834042 0.2766641 HFrec 0.3052411 0.5806152 CMrec 0.3052411 0.5806152 Mrec 1.5298763 0.2161310 }

Details

This function contains tests to compare survival curves with recurrent events. The curves are estimated using Pe�a-Strawderman-Hollander estimator . Pe�a et al. (2001) defined the estimator of the survival function to recurrent events or Kaplan-Meier estimator GPLE. They used two counting processes N and Y. The PSH estimator was defined as, $$\hat{S}(z) =\prod_{t\leq\,z}\left[1-\frac{\Delta\,N\left(s,z\right)}{Y\left(s,z\right)}\right]$$ The authors considered two time scales: one related to calendar time (S) and other related to intercurrences time (T). So, the counting process N(s, z) represents the number of observed events in the calendar period $[0,s]$ with $t\leq\,z$ and Y(s, z) represents the number of observed events in the period $[0,s]$ with $t\geq\,z$. The produc-limit estimator was developed by Pe�a, Strawderman and Hollander,called PSH. This estimator is useful when the interoccurrence times are assumed to represents IID sample from some underlying distribution F. GPLE model The GPLE estimator is defined as: A fundamental assumption of this approach is that individuals have been previously and properly classified in groups according to a stratification variable denote by r. Thus, the estimator of the survival curve by each group is defined as, $$\hat{S}_{r}(z) =\prod_{t\leq\,z}\left[1-\frac{\Delta\,N\left(s,z;r\right)}{Y\left(s,z;r\right)}\right]\!\nabla\:r\:=\!1,2.$$

References

Mart�nez C., Ram�rez, G., V�squez M.(2009).Pruebas no param�tricas para comparar curvas de supervivencia de dos grupos que experimentan eventos recurrentes. Propuestas. Revista Ingenier�a U.C.,Vol 16, 3, 45-55. Mart�nez, C. (2009). Generalizaci�n de algunas pruebas cl�sicas de comparaci�n de curvas de supervivencia al caso de eventos de naturaleza recurrente. Tesis doctoral.Universidad Central de Venezuela (UCV). Caracas-Venezuela. Pe�a E., Strawderman R., Hollander, M. (2001). Nonparametric Estimation with Recurrent Event Data. J.A.S.A. 96, 1299-1315.

See Also

Plot.Event.Rec,Plot.Surv.Rec,Print.Summary

Examples

data(TBCplapyr)
 #Return the p-values of the all tests 
 Dif.Surv.Rec(TBCplapyr,"all",0,0,0,0)
 #Return the p-value of the LRrec test
 Dif.Surv.Rec(TBCplapyr)
 #Return the p-value of the Grec test
 Dif.Surv.Rec(TBCplapyr,"Grec")
 #Return the p-values of the CMrec tests 
 #The CMrec test with this parameters generates LRrec test
 Dif.Surv.Rec(TBCplapyr,"all",0,0,0,0)
 #The CMrec test with this parameters generates Grec test
 Dif.Surv.Rec(TBCplapyr,"all",0,0,1,0)
 #The CMrec test with this parameters generates TWrec test
 Dif.Surv.Rec(TBCplapyr,"all",0,0,0.5,0)
 #The CMrec test with this parameters generates PPrec test
 Dif.Surv.Rec(TBCplapyr,"all",1,0,0,0)
 #The CMrec test with this parameters generates HFrec test
 Dif.Surv.Rec(TBCplapyr,"all",1,1,0,0)