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bunsen:Marginal Estimation with Covariate Adjustment for Survival Endpoint inClinical Trials

Thebunsen package aims to provide an easy-to-useinterface for estimatingmarginal or unconditional Hazard Ratio(HR) and Restricted Mean Survival Time (RMST) when adjustingprognostic covariates in clinical trials.

Key features

We introducebunsen package for marginal HR and RMSTestimation and variance for time-to-event endpoints in clinical trials.We included multiple features in current package:

Marginal HR estimation
- Rcpp (C++) optimization
- ClusterMQ parallel computation
  - Local multiprocess and multicores parallel computation for pointestimate(suggested)
  - LSF parallel computation for point estimate
  - LSF and/or nested local multiprocess parallel computation forvariance(suggested)
RMST estimation

Package architecture

Example

Marginalpoint estimate and variance of HR for COX model

library(bunsen)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)result=get_marginal_effect(trt ='trt',cox_event,cox_censor,M=10000,data=oak,seed =1)# Calculating point estimate in local clustermq using multiprocess...# Submitting 4 worker jobs (ID: cmq7488) ...# Running 4 calculations (8 objs/120.1 Kb common; 1 calls/chunk) ...# Master: [8.6 secs 0.8% CPU]; Worker: [avg 19.1% CPU, max 303.9 Mb]# Calculating SE in clustermq using bootstrap N = 1000...# Submitting 100 worker jobs (ID: cmq9642) ...# Running 1,000 calculations (14 objs/354.8 Kb common; 1 calls/chunk) ...# Master: [16.0 secs 8.4% CPU]; Worker: [avg 48.5% CPU, max 307.7 Mb]result# Call:# Surv(OS, os.status) ~ trt + btmb + pdl1# Marginal treatment effect calculated by N = 10000 simulations# Number of sample: 578#             coef        exp(coef)   se(coef)    2.5%        97.5%# trt         -0.442910   0.642165    0.103905    -0.654306   -0.245416# clustermq setting:#  number of remote workers =  100 , each worker has 1 core(s)# Point estimate: parallel computation (clustermq)# SE (bootstrap): parallel computation# 95%CI estimated by bootstrapsummary(result)# Call:# Surv(OS, os.status) ~ trt + btmb + pdl1# Marginal treatment effect calculated by N = 10000 simulations# Treatment variable: trt ------ Number of sample: 578# Number of events in cox_event: 423# Number of events in cox_censor: 155# Random seed =  1# Original treatment effect:#             coef        exp(coef)   se(coef)    z           Pr(>|z|)# trt         -0.452408   0.636095    0.098428    -4.596346   0.000004# Marginal treatment effect:#             coef        exp(coef)   se(coef)    z           Pr(>|z|)# trt         -0.442910   0.642165    0.103905    -4.262646   0.000020# 95% CI of Marginal treatment effect (bootstrap): -0.654 , -0.245

Marginalpoint estimate and variance of RMST for COX model

library(bunsen)data('oak')tau=26time=oak$OSstatus=oak$os.statustrt=oak$trtcovariates=oak[,c('btmb','pdl1')]result=get_rmst_estimate(time, status, trt, covariates, tau,SE ="delta")result# Call:# Surv(time, status) ~ btmb + pdl1 + strata(trt)# Restricted survival time: 26#             coef        se(coef)    2.5%        97.5%# trt         3.265971    0.716351    1.861923    4.670019# Method for SE calculation: deltaresult=get_rmst_estimate(time, status, trt, covariates, tau,SE ="boot",seed =2025)result# Call:# Surv(time, status) ~ btmb + pdl1 + strata(trt)# Restricted survival time: 26#             coef        se(coef)    2.5%        97.5%# trt         3.265971    0.715191    1.994362    4.699867# Method for SE calculation: bootNumber of bootstrap: 1000 , random seed = 2025

Methodology

bunsen is developed based on three key papers:

HR:RhianDaniel et al. (2020)
RMST:TheodoreKarrison et al. (2018)
Extension of above two methods:JiaweiWei et al. (2024)

Package authors

Xinlei Deng (Maintainer)
Mark Baillie
Dominic Magirr
Craig Wang
Alexander Przybylski

Acknowledgements

Jiawei Wei
Lukas Andreas Widmer

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