This function calculates the probability that the an individual has the event of interest before t0 + tau given the discrete covariate, given the short term event has not yet occurred by t0, and given the long term event has not yet occurred and the individual is still at risk at time t0. This function is called by Prob.Covariate.ShortEvent; this function should not be called on its own.

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`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<C), the third column is log(XS) = log(min(TS, C)) where TS is the time of the short term event, C is the censoring time, the fourth column is DS =1*(TS<C), and the fifth column is the covariate. These are the data used to calculate the estimated probability. |

`covariate.value` |
the discrete covariate value at which to calculate the estimated probability. |

`weight` |
an optional weight to be incorporated in all estimation. |

Estimated probability = P(TL <t0+tau | TL > t0, Z, TS>t0).

Layla Parast

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.

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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