This function calculates the Brier score given the data (truth) and corresponding estimated probabilities.
Usage
BS.landmark(t0, tau, data, short = TRUE, weight=NULL)
Arguments
t0
the landmark time.
tau
the residual survival time of interest.
data
n by k matrix, where k = 4 or 6. 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<C), the second to last column is the covariate vector (can be NULL) and the last column is the estimated probability P(TL<t0+tau | TL>t0).
short
logical value indicating whether data includes short term event information. Should be TRUE if short term XS and DS are includes as third and fourth columns of data matrix, FALSE if not. Default is TRUE.
weight
an optional weight to be incorporated in all estimation.
Value
Brier.score
Estimated Brier score
Author(s)
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
data(data_example_landpred)
t0=2
tau = 8
Prob.Null(t0=t0,tau=tau,data=data_example_landpred)
out = Prob.Null(t0=t0,tau=tau,data=data_example_landpred)
out$Prob
out$data
BS.landmark(t0=t0,tau=tau, data = out$data)
providing 
.