Description Usage Arguments Details Value Note Author(s) See Also Examples

The result of the transformation can be used to do survival analysis via
logistic regression. If the `cloglog`

link is used, this corresponds to
a discrete time analogue to Cox's proportional hazards model.

1 2 |

`dat` |
A data frame with three variables representing the survival
response. The default is that they are named |

`surv` |
A character vector with the names of the three variables representing survival. |

`strats` |
An eventual stratification variable. |

`max.survs` |
Maximal number of survivors per risk set. If set to a (small) number, survivors are sampled from the risk sets. |

toBinary calls `risksets`

in the `eha`

package.

Returns a data frame expanded risk set by risk set. The three
"survival variables" are replaced by a variable named `event`

(which
overwrites an eventual variable by that name in the input). Two more
variables are created, `riskset`

and `orig.row`

.

`event` |
Indicates an event in the corresponding risk set. |

`riskset` |
Factor (with levels 1, 2, ...) indicating risk set. |

`risktime` |
The 'risktime' (age) in the corresponding riskset. |

`orig.row` |
The row number for this item in the original data frame. |

The survival variables must be three. If you only have *exit* and
*event*, create a third containing all zeros.

Göran Broström

1 2 3 4 5 6 7 8 9 10 11 | ```
enter <- rep(0, 4)
exit <- 1:4
event <- rep(1, 4)
z <- rep(c(-1, 1), 2)
dat <- data.frame(enter, exit, event, z)
binDat <- toBinary(dat)
dat
binDat
coxreg(Surv(enter, exit, event) ~ z, method = "ml", data = dat)
## Same as:
summary(glm(event ~ z + riskset, data = binDat, family = binomial(link = cloglog)))
``` |

goranbrostrom/eha documentation built on Sept. 30, 2018, 5:11 p.m.

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