# Prob.Covariate.ShortEvent: Estimates P(TL <t0+tau | TL > t0, Z, min(TS, t0), I(TS<=t0)),... In landpred: Landmark Prediction of a Survival Outcome

## Description

This function calculates the probability that the an individual has the event of interest before t0 + tau given the discrete covariate, given short term event information, and given the event has not yet occurred and the individual is still at risk at time t0.

## Usage

 `1` ```Prob.Covariate.ShortEvent(t0, tau, data, weight = NULL, bandwidth = NULL, newdata=NULL) ```

## Arguments

 `t0` the landmark time. `tau` the residual survival time for which probabilities are calculated. Specifically, this function estimates the probability that the an individual has the event of interest before t0 + tau given the event has not yet occurred and the individual is still at risk at time t0. `data` n by 5 matrix. A data matrix where the first column is XL = min(TL, C) where TL is the time of the long term event, C is the censoring time, and the second column is DL =1*(TL

## Value

 `data` the data matrix with an additional column with the estimated individual probabilities; note that the predicted probability is NA if TL t0 `newdata` the newdata matrix with an additional column with the estimated individual probabilities; note that the predicted probability is NA if TL t0; if newdata is not supplied then this returns NULL

Layla Parast

## References

Parast, Layla, Su-Chun Cheng, and Tianxi Cai. Incorporating short-term outcome information to predict long-term survival with discrete markers. Biometrical Journal 53.2 (2011): 294-307.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22``` ```data(data_example_landpred) t0=2 tau = 8 #note: computationally intensive command below #Prob.Covariate.ShortEvent(t0=t0,tau=tau,data=data_example_landpred) #out = Prob.Covariate.ShortEvent(t0=t0,tau=tau,data=data_example_landpred) #out\$data #data.plot = out\$data #plot(data.plot\$XS[data.plot\$Z ==1], data.plot\$Probability[data.plot\$Z ==1], #pch = 20, xlim = c(0,t0)) #points(data.plot\$XS[data.plot\$Z ==0], data.plot\$Probability[data.plot\$Z ==0], #pch = 20, col = 2) newdata = matrix(c(1,1,0.5,1,0, 3,0,1,1,1, 4,1,1.5,1,0, 10,1,5,1,0, 11,0,11,0,1), ncol = 5, byrow=TRUE) #note: computationally intensive command below #out = Prob.Covariate.ShortEvent(t0=t0,tau=tau,data=data_example_landpred,newdata=newdata) #out\$newdata ```

landpred documentation built on May 29, 2017, 9:16 a.m.