find_weights {nphRCT}

Calculate weights

Description

This function can perform two types of weighted log-rank test, the modestly-weighted log-rank test and the Fleming-Harrington (\rho,\gamma) test, in addition to the standard log-rank test.

Usage

find_weights(
  formula,
  data,
  method,
  t_star = NULL,
  s_star = NULL,
  rho = NULL,
  gamma = NULL,
  include_cens = FALSE,
  timefix = TRUE
)

Arguments

formula

Formula object. The response (on the left of the ~ operator) must be a survival object as returned by the Surv function. The terms (on the right of the ~ operator) must include the treatment arm indicator, and additionally can include strata using the strata function.

data

Data frame containing time-to-event data.

method

Character string specifying type of weighted log-rank test. Either "lr" for a standard log-rank test, "mw" for a modestly-weighted log-rank test, or "fh" for the Fleming-Harrington rho-gamma family.

t_star

Parameter t^* in the modestly weighted ("mw") test, see Details.

s_star

Parameter s^* in the modestly weighted ("mw") test, see Details.

rho

Parameter \rho in the Fleming-Harrington ("fh") test, see Details.

gamma

Parameter \gamma in the Fleming-Harrington ("fh") test, see Details.

include_cens

Boolean indicating whether to include values corresponding to censoring times

timefix

Deal with floating point issues (as in the survival package). Default is TRUE. May need to set FALSE for simulated data.

Details

Select which of the three tests to perform using argument method. The output is calculated as outlined in vignette("weighted_log_rank_tests", package="nphRCT").

Value

Vector of weights in the weighted log-rank test. The weights correspond to the ordered, distinct event times (and censoring times if include_cens=TRUE).

References

Magirr, D. (2021). Non-proportional hazards in immuno-oncology: Is an old perspective needed?. Pharmaceutical Statistics, 20(3), 512-527. doi:10.1002/pst.2091

Magirr, D. and Burman, C.F., 2019. Modestly weighted logrank tests. Statistics in medicine, 38(20), 3782-3790.

Examples

library(nphRCT)
set.seed(1)
sim_data <- sim_events_delay(
  event_model=list(
    duration_c = 36,
    duration_e = c(6,30),
    lambda_c = log(2)/9,
    lambda_e = c(log(2)/9,log(2)/18)
  ),
  recruitment_model=list(
    rec_model="power",
    rec_period = 12,
    rec_power = 1
  ),
  n_c=5,
  n_e=5,
  max_cal_t = 36
)
#example setting t_star
find_weights(formula=Surv(event_time,event_status)~group,
  data=sim_data,
  method="mw",
  t_star = 4
)