| Title: | Bias Reduction Through Analysis of Competing Events (BRACE) |
|---|---|
| Description: | Adjusting the bias due to residual confounding (often called treatment selection bias) in estimating the treatment effect in a proportional hazard model, as described in Williamson et al. (2022) <doi:10.1158/1078-0432.ccr-21-2468>. |
| Authors: | Tuo Lin [aut, cre], Jingjing Zou [aut], Loren Mell [aut] |
| Maintainer: | Tuo Lin <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 0.1.0 |
| Built: | 2024-11-20 06:51:49 UTC |
| Source: | CRAN |
brace is used to estimate the treatment effect with adjusted
confounders on the composite hazard
for primary or competing events, and adjust for bias from residual
confounding in non-randomized data by BRACE method
brace( ftime, fstatus, covs = NA, trt, failcode = 1, cencode = 0, PS = 0, B = 1000 )brace( ftime, fstatus, covs = NA, trt, failcode = 1, cencode = 0, PS = 0, B = 1000 )
ftime |
vector of failure/censoring times |
fstatus |
vector with a unique code for each failure type and a separate code for censored observations (default is primary event = 1, competing event = 2, censored = 0) |
covs |
matrix (nobs x ncovs) of fixed covariates. If no covariates, set covs = NA (default is NA) |
trt |
vector of treatment indicator (1 for treatment group) |
failcode |
code of fstatus that denotes the failure type of interest |
cencode |
code of fstatus that denotes censored observations |
PS |
whether to use propensity score method for adjusting the confounding effect (1 for propensity score method, default is 0) |
B |
bootstrap sample size for calculating the Confidence interval, default is 1000 |
a list of class brace, with components:
$Summary |
summary table of BRACE method |
$`BRACE HR Distribution` |
the estimated regression coefficients in each bootstrap sample |
$`Omega Estimate` |
estimate of relative hazards for primary events vs. combined events |
$Epsilon |
the estimated bias |
$`Combined Endpoint Model` |
the regression model for combined events |
$`Primary Endpoint Model` |
the regression model for primary events |
$`Competing Endpoint Model` |
the regression model for competing events |
$`Omega Curve` |
estimate of omega over time |
$`Combined Endpoint Curve` |
survival curve for combined events |
$`Primary Endpoint Curve` |
survival curve for primary events |
$`Competing Endpoint Curve` |
survival curve for competing events |
Williamson, Casey W., et al. "Bias Reduction through Analysis of Competing Events (BRACE) Correction to Address Cancer Treatment Selection Bias in Observational Data." Clinical Cancer Research 28.9 (2022): 1832-1840.
nsims = 1; nobs = 1500 f = 0.5; g = 0.333; b = 8; w1 = w2 = 0.667 theta1 = 0.5; theta2 = 1; omegaplus = 1; k3 = 0.333 sim1 = gendat(nsims,nobs,f,g,b,w1,w2,omegaplus,theta1,theta2,k3) ftime = sim1$time fstatus = sim1$pfs_ci covs = NA trt = sim1$group braceoutput = brace(ftime, fstatus, covs, trt, PS=0, B=10) nsims = 1; nobs = 1500 f1 = f2 = 0.5; g = 0.333; b1 = 8; b2 = 4; w1 = w2 = 0.667 theta1 = 0.5; theta2 = 1; omegaplus = 1; k3 = 0.333 sim1 = gendat2(nsims,nobs,f1,f2,g,b1,b2,w1,w2,omegaplus,theta1,theta2,k3) ftime = sim1$time fstatus = sim1$pfs_ci covs = sim1$factor2 trt = sim1$group braceoutput = brace(ftime, fstatus, covs, trt, PS=1, B=10)nsims = 1; nobs = 1500 f = 0.5; g = 0.333; b = 8; w1 = w2 = 0.667 theta1 = 0.5; theta2 = 1; omegaplus = 1; k3 = 0.333 sim1 = gendat(nsims,nobs,f,g,b,w1,w2,omegaplus,theta1,theta2,k3) ftime = sim1$time fstatus = sim1$pfs_ci covs = NA trt = sim1$group braceoutput = brace(ftime, fstatus, covs, trt, PS=0, B=10) nsims = 1; nobs = 1500 f1 = f2 = 0.5; g = 0.333; b1 = 8; b2 = 4; w1 = w2 = 0.667 theta1 = 0.5; theta2 = 1; omegaplus = 1; k3 = 0.333 sim1 = gendat2(nsims,nobs,f1,f2,g,b1,b2,w1,w2,omegaplus,theta1,theta2,k3) ftime = sim1$time fstatus = sim1$pfs_ci covs = sim1$factor2 trt = sim1$group braceoutput = brace(ftime, fstatus, covs, trt, PS=1, B=10)
generating the simulation data to apply in brace
gendat(nsims, nobs, f, g, b, w1, w2, omegaplus, theta1, theta2, k3)gendat(nsims, nobs, f, g, b, w1, w2, omegaplus, theta1, theta2, k3)
nsims |
number of simulation datasets |
nobs |
number of observations for one dataset |
f |
parameter for generating unmeasured binary confounder |
g |
parameter for generating group assignment |
b |
confounder effect on group assignment |
w1 |
shape parameter in generating survival time for event 1 from weibull distribution |
w2 |
shape parameter in generating survival time for event 2 from weibull distribution |
omegaplus |
multiplier on the baseline hazard for event 1 |
theta1 |
multiplier on the baseline hazard for event 1 |
theta2 |
multiplier on the baseline hazard for event 2 |
k3 |
multiplier on the baseline hazard for event 2 |
a matrix of nsims*nobs row, which consists of nsims datasets
nsims = 1; nobs = 1500 f = 0.5; g = 0.333; b = 8; w1 = w2 = 0.667 theta1 = 0.5; theta2 = 1; omegaplus = 1; k3 = 0.333 sim1 = gendat(nsims,nobs,f,g,b,w1,w2,omegaplus,theta1,theta2,k3)nsims = 1; nobs = 1500 f = 0.5; g = 0.333; b = 8; w1 = w2 = 0.667 theta1 = 0.5; theta2 = 1; omegaplus = 1; k3 = 0.333 sim1 = gendat(nsims,nobs,f,g,b,w1,w2,omegaplus,theta1,theta2,k3)
generating the simulation data to apply in brace
gendat2(nsims, nobs, f1, f2, g, b1, b2, w1, w2, omegaplus, theta1, theta2, k3)gendat2(nsims, nobs, f1, f2, g, b1, b2, w1, w2, omegaplus, theta1, theta2, k3)
nsims |
number of simulation datasets |
nobs |
number of observations for one dataset |
f1 |
parameter for generating unmeasured binary confounder |
f2 |
parameter for generating measured binary confounder |
g |
parameter for generating group assignment |
b1 |
unmeasured confounder effect on group assignment |
b2 |
measured confounder effect on group assignment |
w1 |
shape parameter in generating survival time for event 1 from weibull distribution |
w2 |
shape parameter in generating survival time for event 2 from weibull distribution |
omegaplus |
multiplier on the baseline hazard for event 1 |
theta1 |
multiplier on the baseline hazard for event 1 |
theta2 |
multiplier on the baseline hazard for event 2 |
k3 |
multiplier on the baseline hazard for event 2 |
a matrix of nsims*nobs row, which consists of nsims datasets
nsims = 1; nobs = 1500 f1 = f2 = 0.5; g = 0.333; b1 = 8; b2 = 4; w1 = w2 = 0.667 theta1 = 0.5; theta2 = 1; omegaplus = 1; k3 = 0.333 sim1 = gendat2(nsims,nobs,f1,f2,g,b1,b2,w1,w2,omegaplus,theta1,theta2,k3)nsims = 1; nobs = 1500 f1 = f2 = 0.5; g = 0.333; b1 = 8; b2 = 4; w1 = w2 = 0.667 theta1 = 0.5; theta2 = 1; omegaplus = 1; k3 = 0.333 sim1 = gendat2(nsims,nobs,f1,f2,g,b1,b2,w1,w2,omegaplus,theta1,theta2,k3)