LogrankA-package: Logrank Test for Aggregated Survival Data

In LogrankA: Logrank Test for Aggregated Survival Data

Description

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

Details

Package:	LogrankA
Type:	Package
Version:	1.0
Date:	2013-07-15
License:	GPL-2

The package contains the function LogrankA.

Author(s)

Jonas Richter-Dumke and Roland Rau

Maintainer: Jonas Richter-Dumke <[e-mail]>

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

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)

Example output

Valid observations: 2843
Dropped observations: 0

Logrank test statistic = 42.03813 on 3 degrees of freedom,
p = 3.93806e-09

$p.chi2
[1] 3.93806e-09

$df
[1] 3

$LR
[1] 42.03813

$lr.parameter
            N Obs. events Exp. events    (O-E)^2/E
[0,20)     39          24    24.02395 2.387355e-05
[20,40)  1727        1063  1145.09080 5.885036e+00
[40,60)  1000         616   563.62149 4.867644e+00
[60,100)   77          58    28.26376 3.128543e+01

Valid observations: 2843
Dropped observations: 0

Logrank test statistic = 42.03813 on 3 degrees of freedom,
p = 3.93806e-09

$p.chi2
[1] 3.93806e-09

$df
[1] 3

$LR
[1] 42.03813

$lr.parameter
            N Obs. events Exp. events    (O-E)^2/E
[0,20)     39          24    24.02395 2.387355e-05
[20,40)  1727        1063  1145.09080 5.885036e+00
[40,60)  1000         616   563.62149 4.867644e+00
[60,100)   77          58    28.26376 3.128543e+01

LogrankA documentation built on May 2, 2019, 11:01 a.m.