README.md

In cyhsuTN/AdjCOXPH: A Novel Adjustment Method for Cox Proportional Hazards Model in Data with Long-term Survival

CoxTEL: a method to correct misinterpretation of clinical trial results with long-term survival

Chih-Yuan Hsu

June 28, 2024

Installation

Download CoxTEL_1.2.0.tar.gz and locally install it, or execute the following code:

library(devtools)
install_github("cyhsuTN/CoxTEL")

Usage

library(CoxTEL)

Example

A demo survival figure for CoxTEL. The KM curves and HR with 95% CI are estimated from simulated data.

Figure 1: Reported KM curves and HR with 95% CI

First step: manually extract probabilities from KM curves

Users can use the following R code to extract the probabilities from Kaplan-Meier curves by mouse-clicks.

library(imager)
library(IPDfromKM)

imgexp <- imager::load.image("../Demo_Fig.png") #png/jpg (bitmap images)

Please decide the limits of x-axis and y-axis when executing the R command line below. For example, x = c(0,48) and y = c(0, 1) for the demo figure.

df <- IPDfromKM::getpoints(imgexp, 0, 48, 0, 1)

Will pop out messages on the R console. Please follow the instruction to extract survival probabilities at specific time points, for example, at the 6th, 12th, …, 48th months. Once users complete the mouse-clicks, please click “Esc” on the keyboard.

df saves all survival probabilities at the 6th, 12th, …, 48th months. Besides, users also need to extract the cure proportions from plateaus at the tails of survival curves. In this case, the cure proportions are the survival probabilities at the 48th months.

Second step: input the extracted probabilities and the reported Cox HR as well as CI

s1mix.chosen <- c(.34, .23, .19, .14, .13, .12, .11, .11) # extracted prob. in treatment arm
s0mix.chosen <- c(.41, .22, .12, .08, .06, .04, .03, .03) # extracted prob. in control arm
pi1.est <- 1 - 0.11 # uncured proportion in treatment arm
pi0.est <- 1 - 0.03 # uncured proportion in control arm
HR_cox <- 0.92 # reported Cox HR
HR_cox_CI <- c(0.77, 1.10) # reported Cox HR CI

adjustment(HR_cox, HR_cox_CI, s1mix.chosen, s0mix.chosen, pi1.est, pi0.est)

## $Adj.before.after
##        Cox_HR    Cox_HR_CIL    Cox_HR_CIU     CoxTEL_HR CoxTEL_HR_CIL 
##         0.920         0.770         1.100         1.185         0.992 
## CoxTEL_HR_CIU     CoxTEL_DP CoxTEL_DP_CIL CoxTEL_DP_CIU 
##         1.417         0.080         0.038         0.123 
## 
## $Proportion.LTS
##     Arm0 Arm0_CIL Arm0_CIU     Arm1 Arm1_CIL Arm1_CIU 
##    0.030    0.010    0.042    0.110    0.080    0.133 
## 
## $s1mix.chosen
## [1] 0.34 0.23 0.19 0.14 0.13 0.12 0.11 0.11
## 
## $s0mix.chosen
## [1] 0.41 0.22 0.12 0.08 0.06 0.04 0.03 0.03

CoxTEL HR: 1.185 (0.992 to 1.417) for short-term survivors; CoxTEL DP: 0.080 (0.038 to 0.123) for long-term survivors.

Proportion.LTS: the estimates for the proportions of long-term survivors in both arms.

References

C.Y. Hsu, E.P.Y. Lin, Y. Shyr. (2021). Development and Evaluation of a Method to Correct Misinterpretation of Clinical Trial Results With Long-term Survival. JAMA Oncol. doi:10.1001/jamaoncol.2021.0289.

E.P.Y. Lin, C.Y. Hsu, J.F. Chiou et al. (2022). Cox Proportional Hazard Ratios Overestimate Survival Benefit of Immune Checkpoint Inhibitors (ICI): Cox-TEL Adjustment and Meta-analyses of PD-L1 Expression and ICI Survival Benefit. J Thorac Oncol. 17, 1365-1374. doi:10.1016/j.jtho.2022.08.010.

E.P.Y. Lin, C.Y. Hsu, L. Berry, P. Bunn, Y. Shyr (2022). Analysis of Cancer Survival associated with Immune Checkpoint Inhibitors after Statistical Adjustment: A Systematic Review and Meta-analyses. JAMA Network Open. 5(8):e2227211. doi:10.1001/jamanetworkopen.2022.27211.

cyhsuTN/AdjCOXPH documentation built on July 4, 2024, 8:05 p.m.