# Logrank Test for Aggregated Survival Data

### Description

`LogrankA`

provides a logrank test across unlimited groups with the
possibility to input aggregated survival data.

### Usage

1 |

### Arguments

`surv` |
An object of type |

`group` |
Argument |

`weight` |
The argument |

### Details

The `group`

and `weight`

arguments must correspond to the entries in
the `surv`

argument. Therefore the `group`

and `weight`

vectors
must be equal in length to the time and status columns in the survival object
of `surv`

If the weight argument is not specified it is assumed that the input data is not aggregated.

More than a single group must be specified.

### Value

`p.chi2` |
P-value of chi-squared test of logrank test statistic. |

`df` |
Degrees of freedom used for chi-squared test. |

`LR` |
Value of logrank test statistic. |

`lr.parameter` |
Number of observations, observed events, expected events, (O-E)^2/E for each group. |

In addition a short text summary of the logrank test is printed to the console.

### Note

For an in-depth explanation of `LogrankA`

please see the package vignette.

### Author(s)

Jonas Richter-Dumke and Roland Rau

Maintainer: Jonas Richter-Dumke <jrd.r.project@gmail.com>

### References

Peto, R. et al. (1977). "Design and analysis of randomized clinical trials requiring prolonged observation of each patient". II. analysis and examples. In: British journal of cancer 35.1, pp. 1-39.

Ziegler, A., S. Lange, and R. Bender (2007). "Ueberlebenszeitanalyse: Der Log-Rang-Test". In: Deutsche Medizinische Wochenschrift 132, pp. 39-41.

### See Also

`Surv`

, `survdiff`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | ```
library(survival)
library(MASS)
## data: survival of australian aids patients (individual and aggregated)
aids2.ind <- Aids2 # import australian aids data
aids2.ind$status <- as.numeric(aids2.ind$status) - 1 # recode status to 0/1
stime.days <- aids2.ind$death - aids2.ind$diag # generate survival time in weeks
aids2.ind$stime <- round(stime.days / 7, 0)
aids2.ind$agegr <- cut(aids2.ind$age, # generate age groups
c(0, 20, 40, 60, 100), right = FALSE)
aids2.ind <- aids2.ind[ , c(5, 8, 9)] # keep only important columns
aids2.aggr <- aggregate(aids2.ind$stime, # transform to aggregated data
by = list(aids2.ind$status, aids2.ind$stime,
aids2.ind$agegr),
FUN = length)
colnames(aids2.aggr) <- c("status", "stime", "agegr", "n")
# generate survival objects for individual and aggregated data
surv.ind <- Surv(aids2.ind$stime, aids2.ind$status)
surv.aggr <- Surv(aids2.aggr$stime, aids2.aggr$status)
## logrank test on individual and aggregated data
# logrank on individual data
LogrankA(surv = surv.ind,
group = aids2.ind$agegr)
# logrank on aggregated data
LogrankA(surv = surv.aggr,
group = aids2.aggr$agegr,
weight = aids2.aggr$n)
``` |