rmst_point_estimate {bunsen} | R Documentation |
Calculate the point estimate of the marginal restricted mean survival time (RMST) when adjusting covariates in clinical trials
Description
Estimate the marginal RMST (point estimate) using the Karrison et al.(2018).
Usage
rmst_point_estimate(fit, dt, tau)
Arguments
fit |
A coxph object with strata(trt) in the model. See example. |
dt |
A data frame used for the fit - coxph model including survival time, OS status, trt, and covariates. |
tau |
Numeric. A value for the restricted time or the pre-specified cutoff time point. |
Details
Restricted mean survival time is a measure of average survival time up to a specified time point. We adopted the methods from Karrison et al.(2018) for estimating the marginal RMST when adjusting covariates.
Value
A list containing the RMST, cumulative survival function, and cumulative hazard function.
- output
Marginal RMST
- surv0
Cumulative survival function for the placebo group
- cumhaz0
Cumulative hazard function for the placebo group
- surv1
Cumulative survival function for the treatment group
- cumhaz1
Cumulative hazard function for the treatment group
References
Karrison T, Kocherginsky M. Restricted mean survival time: Does covariate adjustment improve precision in randomized clinical trials? Clinical Trials. 2018;15(2):178-188. doi:10.1177/1740774518759281
Zucker, D. M. (1998). Restricted Mean Life with Covariates: Modification and Extension of a Useful Survival Analysis Method. Journal of the American Statistical Association, 93(442), 702–709. https://doi.org/10.1080/01621459.1998.10473722
Wei, J., Xu, J., Bornkamp, B., Lin, R., Tian, H., Xi, D., … Roychoudhury, S. (2024). Conditional and Unconditional Treatment Effects in Randomized Clinical Trials: Estimands, Estimation, and Interpretation. Statistics in Biopharmaceutical Research, 16(3), 371–381. https://doi.org/10.1080/19466315.2023.2292774
Chen, P. and Tsiatis, A. (2001), “Causal Inference on the Difference of the Restricted Mean Lifetime Between Two Groups,” Biometrics; 57: 1030–1038. DOI: 10.1111/j.0006-341x.2001.01030.x.
Examples
library(survival)
data("oak")
tau <- 26
time <- oak$OS
status <- oak$os.status
trt <- oak$trt
covariates <- oak[, c("btmb", "pdl1")]
dt <- as.data.frame(cbind(time, status, trt, covariates))
fit <- coxph(Surv(time, status) ~ btmb + pdl1 + strata(trt), data = dt)
delta <- rmst_point_estimate(fit, dt = dt, tau)
delta$output