View source: R/robincar-logrank.R
robincar_logrank | R Documentation |
Perform a robust covariate-adjusted logrank test ("CL") that can be stratified ("CSL") if desired.
robincar_logrank(adj_method, ...)
adj_method |
Adjustment method, one of "CL", "CSL" |
... |
Additional arguments to 'robincar_tte' |
Note: Since RobinCar version 0.4.0, the variance of the test statistic has changed to better accommodate tied event times.
\hat{\sigma}_{\rm CL}^2
and \hat{\sigma}_{\rm CSL}^2
for the
covariate-adjusted stratified log-rank test are given by Ye, Shao, and Yi (2024) after
equation (8) on page 700, with \hat{\sigma}_{\rm SL}^2
replaced
by the following estimator, which is the standard denominator of the logrank test:
\frac1n \sum_{j} \sum_{i} v_j(t_i)
v_j(t_i) = \frac{Y_{0,j}(t)Y_{1,j}(t)d_j(t)\left[Y_j(t) - d_j(t)\right]}{Y_j(t)^2\left[Y_j(t) - 1 \right]}
where t_i
are strata-specific unique failure times, d_j(t)
is the number of events at time t
in strata j
,
Y_j(t)
is the number at risk within strata j
at time t
,
and Y_{a,j}(t)
is the number at risk within strata j
and treatment a
at time t
.
Please see Ye, Shao, and Yi (2024)'s "Covariate-adjusted log-rank test: guaranteed efficiency
gain and universal applicability" in Biometrika for more details about \hat{\sigma}_{\rm CSL}^2
.
A result object with the following attributes:
result |
A list: "statistic" is the adjusted logrank test statistic which can be used to obtain p-values; "U" and "se" are the numerator and denominator of the test statistic, respectively. |
settings |
The covariate adjustment settings used. |
original_df |
The dataset supplied by the user. |
library(magrittr)
library(dplyr)
library(forcats)
set.seed(0)
n=100
data.simu0=data_gen(n=n,
theta=0,
randomization="permuted_block",
p_trt=0.5,
case="case2") %>% mutate(strata1=sample(letters[1:3],n,replace=TRUE),
strata2=sample(LETTERS[4:5],n,replace=TRUE))
out <- robincar_logrank(df=data.simu0,
treat_col="I1",
p_trt=0.5,
ref_arm=0,
response_col="t",
event_col="delta",
covariate_cols=c("model_z1", "model_z2"),
car_scheme="simple",
adj_method=c("CL"))
set.seed(0)
n=100
data.simu0=data_gen(n=n,
theta=0,
randomization="permuted_block",
p_trt=0.5,
case="case1")
data.simu <- data.simu0 %>%
tidyr::pivot_longer(cols=starts_with("car_strata"),
names_prefix="car_strata",
names_to="strt") %>%
filter(value==1) %>% select(-value) %>%
mutate(strt=forcats::as_factor(strt)) %>%
select(t,strt) %>%
left_join(data.simu0, .)
out1 <- robincar_logrank(df=data.simu,
treat_col="I1",
p_trt=0.5,
ref_arm=0,
response_col="t",
event_col="delta",
car_strata_cols="strt",
covariate_cols=NULL,
car_scheme=c("permuted-block"),
adj_method=c("CSL")
)
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