Provides a geometric average hazard ratio under various non-proportional hazards assumptions for either single or multiple strata studies. The piecewise exponential distribution allows a simple method to specify a distribution and enrollment pattern where the enrollment, failure and dropout rates changes over time.
Usage
pw_info(
enroll_rate = define_enroll_rate(duration = c(2, 2, 10), rate = c(3, 6, 9)),
fail_rate = define_fail_rate(duration = c(3, 100), fail_rate = log(2)/c(9, 18), hr =
c(0.9, 0.6), dropout_rate = 0.001),
total_duration = 30,
ratio = 1
)Arguments
- enroll_rate
An
enroll_ratedata frame with or without stratum created bydefine_enroll_rate().- fail_rate
A
fail_ratedata frame with or without stratum created bydefine_fail_rate().- total_duration
Total follow-up from start of enrollment to data cutoff; this can be a single value or a vector of positive numbers.
- ratio
Ratio of experimental to control randomization.
Value
A tibble with time (from total_duration),
ahr (average hazard ratio), event (expected number of events),
info (information under given scenarios), and info0
(information under related null hypothesis) for each value of
total_duration input
Examples
# Example: default
pw_info()
#> time stratum t hr event info info0
#> 1 30 All 0 0.9 21.24782 5.300180 5.311956
#> 2 30 All 3 0.6 37.24314 9.027063 9.310786
# Example: default with multiple analysis times (varying total_duration)
pw_info(total_duration = c(15, 30))
#> time stratum t hr event info info0
#> 1 15 All 0 0.9 20.13991 5.023729 5.034979
#> 2 15 All 3 0.6 10.13850 2.417457 2.534625
#> 3 30 All 0 0.9 21.24782 5.300180 5.311956
#> 4 30 All 3 0.6 37.24314 9.027063 9.310786
# Stratified population
enroll_rate <- define_enroll_rate(
stratum = c(rep("Low", 2), rep("High", 3)),
duration = c(2, 10, 4, 4, 8),
rate = c(5, 10, 0, 3, 6)
)
fail_rate <- define_fail_rate(
stratum = c(rep("Low", 2), rep("High", 2)),
duration = 1,
fail_rate = c(.1, .2, .3, .4),
dropout_rate = .001,
hr = c(.9, .75, .8, .6)
)
# Give results by change-points in the piecewise model
ahr(enroll_rate = enroll_rate, fail_rate = fail_rate, total_duration = c(15, 30))
#> time ahr event info info0
#> 1 15 0.7332218 113.2782 28.11984 28.31954
#> 2 30 0.7175169 166.1836 41.30913 41.54590
# Same example, give results by strata and time period
pw_info(enroll_rate = enroll_rate, fail_rate = fail_rate, total_duration = c(15, 30))
#> time stratum t hr event info info0
#> 1 15 High 0 0.80 12.076677 2.990626 3.019169
#> 2 15 High 1 0.60 23.118608 5.716292 5.779652
#> 3 15 Low 0 0.90 9.962824 2.484435 2.490706
#> 4 15 Low 1 0.75 68.120046 16.928485 17.030011
#> 5 30 High 0 0.80 14.169853 3.509171 3.542463
#> 6 30 High 1 0.60 45.213092 11.198980 11.303273
#> 7 30 Low 0 0.90 9.962824 2.484435 2.490706
#> 8 30 Low 1 0.75 96.837847 24.116546 24.209462