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Time-to-event data example 1 for non-proportional hazards working group

Source: R/ex1_delayed_effect.R
ex1_delayed_effect.Rd

Survival objects reverse-engineered datasets from published Kaplan-Meier curves. Individual trials are de-identified since the data are only approximations of the actual data. Data are intended to evaluate methods and designs for trials where non-proportional hazards may be anticipated for outcome data.

Usage

data(ex1_delayed_effect)

Format

Data frame with 4 variables:

  • id: Sequential numbering of unique identifiers.

  • month: Time-to-event.

  • event: 1 for event, 0 for censored.

  • trt: 1 for experimental, 0 for control.

References

Lin, Ray S., Ji Lin, Satrajit Roychoudhury, Keaven M. Anderson, Tianle Hu, Bo Huang, Larry F Leon, Jason J.Z. Liao, Rong Liu, Xiaodong Luo, Pralay Mukhopadhyay, Rui Qin, Kay Tatsuoka, Xuejing Wang, Yang Wang, Jian Zhu, Tai-Tsang Chen, Renee Iacona & Cross-Pharma Non-proportional Hazards Working Group. 2020. Alternative analysis methods for time to event endpoints under nonproportional hazards: A comparative analysis. Statistics in Biopharmaceutical Research 12(2): 187–198.

See also

ex2_delayed_effect, ex3_cure_with_ph, ex4_belly, ex5_widening, ex6_crossing

Examples

library(survival)
#> 
#> Attaching package: ‘survival’
#> The following object is masked from ‘package:future’:
#> 
#>     cluster

data(ex1_delayed_effect)
km1 <- with(ex1_delayed_effect, survfit(Surv(month, evntd) ~ trt))
km1
#> Call: survfit(formula = Surv(month, evntd) ~ trt)
#> 
#>         n events median 0.95LCL 0.95UCL
#> trt=0 121     86   5.04    4.18    6.21
#> trt=1 240    132   7.66    6.54    9.48
plot(km1)

with(subset(ex1_delayed_effect, trt == 1), survfit(Surv(month, evntd) ~ trt))
#> Call: survfit(formula = Surv(month, evntd) ~ trt)
#> 
#>        n events median 0.95LCL 0.95UCL
#> [1,] 240    132   7.66    6.54    9.48
with(subset(ex1_delayed_effect, trt == 0), survfit(Surv(month, evntd) ~ trt))
#> Call: survfit(formula = Surv(month, evntd) ~ trt)
#> 
#>        n events median 0.95LCL 0.95UCL
#> [1,] 121     86   5.04    4.18    6.21