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Milestone test for two survival curves

Source: R/milestone.R
milestone.Rd

Milestone test for two survival curves

Usage

milestone(data, ms_time, test_type = c("log-log", "naive"))

Arguments

data

Data frame containing at least 3 columns:

  • tte - Time to event.

  • event - Event indicator.

  • treatment - Grouping variable.

ms_time

Milestone analysis time.

test_type

Method to build the test statistics. There are 2 options:

  • "native": a native approach by dividing the KM survival difference by its standard derivations, see equation (1) of Klein, J. P., Logan, B., Harhoff, M., & Andersen, P. K. (2007).

  • "log-log": a log-log transformation of the survival, see equation (3) of Klein, J. P., Logan, B., Harhoff, M., & Andersen, P. K. (2007).

Value

A list frame containing:

  • method - The method, always "milestone".

  • parameter - Milestone time point.

  • estimate - Survival difference between the experimental and control arm.

  • se - Standard error of the control and experimental arm.

  • z - Test statistics.

References

Klein, J. P., Logan, B., Harhoff, M., & Andersen, P. K. (2007). "Analyzing survival curves at a fixed point in time." Statistics in Medicine, 26(24), 4505--4519.

Examples

cut_data <- sim_pw_surv(n = 200) |>
  cut_data_by_event(150)

cut_data |>
  milestone(10, test_type = "log-log")
#> $method
#> [1] "milestone"
#> 
#> $parameter
#> [1] 10
#> 
#> $estimate
#> [1] -0.5293995
#> 
#> $se
#> [1] -0.3026401
#> 
#> $z
#> [1] 1.749271
#> 

cut_data |>
  milestone(10, test_type = "naive")
#> $method
#> [1] "milestone"
#> 
#> $parameter
#> [1] 10
#> 
#> $estimate
#> [1] 0.1760742
#> 
#> $se
#> [1] 0.09869579
#> 
#> $z
#> [1] 1.784009
#>