Left ventricular ejection fraction of patients with malignant ventricular tachyarrhythmias.

A data frame with the left ventricular ejection fraction of patients with malignant ventricular tachyarrhythmias including recurrence-free month and censoring.

data("hohnloser")

Format

EF

left ventricular ejection in percent

month

recurrence-free month

cens

censoring: 0 cencored, 1 not censored

The data used here is published in Table 1 of Lausen and Schumacher (1992).

Source

The data was first published by Hohnloser et al. (1987), the data used here is published in Table 1 of Lausen and Schumacher (1992).

References

Hohnloser, S.H., Raeder, E.A., Podrid, P.J., Graboys, T.B. and Lown, B. (1987), Predictors of antiarrhythmic drug efficacy in patients with malignant ventricular tachyarrhythmias. American Heart Journal 114, 1–7

Lausen, B. and Schumacher, M. (1992), Maximally Selected Rank Statistics. Biometrics 48, 73–85

Examples

set.seed(29)

library("survival")

# limiting distribution

maxstat.test(Surv(month, cens) ~ EF, data=hohnloser, 
smethod="LogRank", pmethod="Lau92")
#> 
#> Maximally selected LogRank statistics using Lau92
#> 
#> data:  Surv(month, cens) by EF
#> M = 3.5691, p-value = 0.01065
#> sample estimates:
#> estimated cutpoint 
#>                 39 
#> 

# with integer valued scores for comparison

maxstat.test(Surv(month, cens) ~ EF, data=hohnloser, 
smethod="LogRank", pmethod="Lau92", iscores=TRUE)
#> 
#> Maximally selected LogRank statistics using Lau92
#> 
#> data:  Surv(month, cens) by EF
#> M = 3.5639, p-value = 0.01083
#> sample estimates:
#> estimated cutpoint 
#>                 39 
#> 

# improved Bonferroni inequality

maxstat.test(Surv(month, cens) ~ EF, data=hohnloser,
smethod="LogRank", pmethod="Lau94")
#> 
#> Maximally selected LogRank statistics using Lau94
#> 
#> data:  Surv(month, cens) by EF
#> M = 3.5691, p-value = 0.005453
#> sample estimates:
#> estimated cutpoint 
#>                 39 
#> 

maxstat.test(Surv(month, cens) ~ EF, data=hohnloser,
smethod="LogRank", pmethod="Lau94", iscores=TRUE)
#> 
#> Maximally selected LogRank statistics using Lau94
#> 
#> data:  Surv(month, cens) by EF
#> M = 3.5639, p-value = 0.005556
#> sample estimates:
#> estimated cutpoint 
#>                 39 
#> 


# small sample solution by Hothorn & Lausen

maxstat.test(Surv(month, cens) ~ EF, data=hohnloser,
smethod="LogRank", pmethod="HL")
#> 
#> Maximally selected LogRank statistics using HL
#> 
#> data:  Surv(month, cens) by EF
#> M = 3.5639, p-value = 0.00667
#> sample estimates:
#> estimated cutpoint 
#>                 39 
#> 

# normal approximation

maxstat.test(Surv(month, cens) ~ EF, data=hohnloser,
smethod="LogRank", pmethod="exactGauss")
#> 
#> Maximally selected LogRank statistics using exactGauss
#> 
#> data:  Surv(month, cens) by EF
#> M = 3.5691, p-value = 0.004435
#> sample estimates:
#> estimated cutpoint 
#>                 39 
#> 

maxstat.test(Surv(month, cens) ~ EF, data=hohnloser,
smethod="LogRank", pmethod="exactGauss", iscores=TRUE)
#> 
#> Maximally selected LogRank statistics using exactGauss
#> 
#> data:  Surv(month, cens) by EF
#> M = 3.5639, p-value = 0.004338
#> sample estimates:
#> estimated cutpoint 
#>                 39 
#> 

# conditional Monte-Carlo
maxstat.test(Surv(month, cens) ~ EF, data=hohnloser,
smethod="LogRank", pmethod="condMC", B = 9999)
#> 
#> Maximally selected LogRank statistics using condMC
#> 
#> data:  Surv(month, cens) by EF
#> M = 3.5691, p-value = 0.0045
#> sample estimates:
#> estimated cutpoint 
#>                 39 
#>