| Type: | Package |
| Title: | Bias Reduction Through Analysis of Competing Events (BRACE) |
| Version: | 0.1.0 |
| Maintainer: | Tuo Lin <tulin@health.ucsd.edu> |
| 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>. |
| License: | GPL (≥ 3) |
| Depends: | survival, survminer |
| Suggests: | knitr, testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.1 |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2023-03-03 18:14:28 UTC; tuo |
| Author: | Tuo Lin [aut, cre], Jingjing Zou [aut], Loren Mell [aut] |
| Repository: | CRAN |
| Date/Publication: | 2023-03-06 13:50:13 UTC |
Bias Reduction through Analysis of Competing Events
Description
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
Usage
brace(
ftime,
fstatus,
covs = NA,
trt,
failcode = 1,
cencode = 0,
PS = 0,
B = 1000
)
Arguments
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 |
Value
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 |
$`Competing Endpoint Curve` |
survival curve for competing events |
References
Williamson, Casey W., et al. "Bias Reduction through Analysis of Competing Events (BRACE) Correction to Address Cancer Treatment Selection Bias in Observational Data." Clinical Cancer Research 28.9 (2022): 1832-1840.
Examples
nsims = 1; nobs = 1500
f = 0.5; g = 0.333; b = 8; w1 = w2 = 0.667
theta1 = 0.5; theta2 = 1; omegaplus = 1; k3 = 0.333
sim1 = gendat(nsims,nobs,f,g,b,w1,w2,omegaplus,theta1,theta2,k3)
ftime = sim1$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)
simulation data generating function
Description
generating the simulation data to apply in brace
Usage
gendat(nsims, nobs, f, g, b, w1, w2, omegaplus, theta1, theta2, k3)
Arguments
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 |
Value
a matrix of nsims*nobs row, which consists of nsims datasets
Examples
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)
simulation data generating function (Adding a measured confounder)
Description
generating the simulation data to apply in brace
Usage
gendat2(nsims, nobs, f1, f2, g, b1, b2, w1, w2, omegaplus, theta1, theta2, k3)
Arguments
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 |
Value
a matrix of nsims*nobs row, which consists of nsims datasets
Examples
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)