This allows one to create the Aalen-Johansen estimate of P, a matrix with one column per state and one row per time, starting with the individual hazard estimates. Each row of P will sum to 1.

1 2 |

`formula` |
a matrix of lists, each element of which is either NULL or a survival curve object. |

`p0` |
the initial state vector. The names of this vector are used as the names of the states in the output object. |

`method` |
use a product of discrete hazards, or a product of matrix exponentials. See details below. |

`...` |
further arguments for other methods |

When using predicted survival curves from a Cox model, it is possible
to get predicted hazards that are greater than 1. For this reason it is
safer to use exp(-hazard) rather than (1-hazard) as the multiplier for
the probability. This is also the statndard approach for ordinarly
survival curves from a Cox model. If the `method`

argument is
not supplied and the input values are derived from a Cox model, this
is used as the default.

This approach is easy to use but has some caveats. First, the input curves must be consistent. The routine checks as best it can, but is easy fool. If you fit two Cox models, obtain predictions for males and females from one, and for treatment A and B from the other, this routine will create two curves but they are not meaningful. A second is that standard errors are not produced.

The names of the resulting states are taken from the names of the vector of initial state probabilities. If they are missing, then the dimnames of the input matrix are used, and lacking that the labels '1', '2', etc. are used.

a survfitms object

The R syntax for creating a matrix of lists is very fussy.

Terry Therneau

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
etime <- with(mgus2, ifelse(pstat==0, futime, ptime))
event <- with(mgus2, ifelse(pstat==0, 2*death, 1))
event <- factor(event, 0:2, labels=c("censor", "pcm", "death"))
cfit1 <- coxph(Surv(etime, event=="pcm") ~ age + sex, mgus2)
cfit2 <- coxph(Surv(etime, event=="death") ~ age + sex, mgus2)
# predicted competing risk curves for a 72 year old with mspike of 1.2
# (median values)
newdata <- expand.grid(sex=c("F", "M"), age=72, mspike=1.2)
AJmat <- matrix(list(), 3,3)
AJmat[1,2] <- list(survfit(cfit1, newdata, std.err=FALSE))
AJmat[1,3] <- list(survfit(cfit2, newdata, std.err=FALSE))
csurv <- survfit(AJmat, p0 =c(entry=1, PCM=0, death=0))
``` |

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