Calculate the marginal treatment effects for hazard ratio (HR) adjusting covariates in clinical trials

Use the simulation approach from Daniel et al. (2020) to estimate the marginal HR when adjusting covariates. Standard error of marginal HR was estimated via the nonparametric bootstrap. The standard error of HR will converge with the increase of bootstrap. We suggest setting a large M for a more robust estimation. This function uses the parallel computation via remote LSF and local multiprocess. Additional feature also includes the C++ optimization that can speed up the calculation.

get_marginal_effect(
  trt,
  cox_event,
  cox_censor,
  data,
  M,
  SE = TRUE,
  seed = NULL,
  cpp = TRUE,
  n.boot = 1000,
  control = clmqControl(),
  verbose = TRUE
)

Arguments

trt

Character. Variable name of the treatment assignment. Only support two arm trial at the moment.

cox_event

Object. A coxph model using the survival time and survival status.

cox_censor

Object. A coxph model using the survival time and 1-survival status.

data

A data frame used for cox_event and cox_censor.

M

Numeric. The number of simulated counterfactual patients. Suggest to set a large number to get robust results, but this will be very time comsuming.

SE

Bool. True for estimating SE. False for not estimating SE.

seed

Numeric. Random seed for simulation.

cpp

Bool. True for using C++ optimization. False for not using C++ optimization. This requires cpp package installed.

n.boot

Numeric. Number of bootstrap used.

control

Named list. A list containing control parameters, including memory of remote workers, whether to use nested parallel computation or local multiprocess, number of remote workers/jobs, etc. See details of clmqControl.

verbose

Bool. Print status messages. Default: TRUE

Value

A vector containing the marginal beta (logHR), standard error, and 95% CI.

Details

In clinical trials, adjusting covariates like prognostic factors in the main analysis can increase the precision resulting in smaller SE. However, adjusting covariates in nonlinear models changes the target estimands, e.g. from marginal treatment effects to conditional treatment effects. This function has implemented the methods of Daniel et al. (2020) to estimate the marginal treatment effects when adjusting covariates. Increasing the M - the number of bootstrap can significantly increase the computation time. Hence, we introduced C++ optimization and parallel computation to speed up the calculation. It provides nested parallel computation via LSF and remote parallel computation with local multiprocess.

References

Daniel R, Zhang J, Farewell D. Making apples from oranges: Comparing noncollapsible effect estimators and their standard errors after adjustment for different covariate sets. Biom J. 2021;63(3):528-557. doi:10.1002/bimj.201900297

Examples

if (FALSE) {
#Don't run as it requires LSF scheduler

library(survival)
data("oak")

cox_event <- coxph(Surv(OS, os.status) ~ trt + btmb + pdl1, data = oak)
#
cox_censor <- coxph(Surv(OS, 1 - os.status) ~ trt + btmb + pdl1, data = oak)
#
get_marginal_effect(
  trt = "trt", cox_event = cox_event, cox_censor = cox_censor, SE = TRUE,
  M = 1000, n.boot = 10, data = oak, seed = 1, cpp = FALSE, control = clmqControl(n_jobs = 100)
)
#
}