tsesimp | R Documentation |
Obtains the causal parameter estimate of the accelerated failure-time (AFT) model for switching after disease progression and the hazard ratio estimate of the outcome Cox model to adjust for treatment switching.
tsesimp(
data,
id = "id",
stratum = "",
time = "time",
event = "event",
treat = "treat",
censor_time = "censor_time",
pd = "pd",
pd_time = "pd_time",
swtrt = "swtrt",
swtrt_time = "swtrt_time",
base_cov = "",
base2_cov = "",
aft_dist = "weibull",
strata_main_effect_only = TRUE,
recensor = TRUE,
admin_recensor_only = TRUE,
swtrt_control_only = TRUE,
alpha = 0.05,
ties = "efron",
offset = 1,
boot = TRUE,
n_boot = 1000,
seed = NA
)
data |
The input data frame that contains the following variables:
|
id |
The name of the id variable in the input data. |
stratum |
The name(s) of the stratum variable(s) in the input data. |
time |
The name of the time variable in the input data. |
event |
The name of the event variable in the input data. |
treat |
The name of the treatment variable in the input data. |
censor_time |
The name of the censor_time variable in the input data. |
pd |
The name of the pd variable in the input data. |
pd_time |
The name of the pd_time variable in the input data. |
swtrt |
The name of the swtrt variable in the input data. |
swtrt_time |
The name of the swtrt_time variable in the input data. |
base_cov |
The names of baseline covariates (excluding treat) in the input data for the outcome Cox model. |
base2_cov |
The names of baseline and secondary baseline covariates (excluding swtrt) in the input data for the AFT model for post-progression survival. |
aft_dist |
The assumed distribution for time to event for the AFT model. Options include "exponential", "weibull" (default), "loglogistic", and "lognormal". |
strata_main_effect_only |
Whether to only include the strata main
effects in the AFT model. Defaults to |
recensor |
Whether to apply recensoring to counterfactual
survival times. Defaults to |
admin_recensor_only |
Whether to apply recensoring to administrative
censoring times only. Defaults to |
swtrt_control_only |
Whether treatment switching occurred only in
the control group. The default is |
alpha |
The significance level to calculate confidence intervals. The default value is 0.05. |
ties |
The method for handling ties in the Cox model, either "breslow" or "efron" (default). |
offset |
The offset to calculate the time to event, PD, and
treatment switch. We can set |
boot |
Whether to use bootstrap to obtain the confidence
interval for hazard ratio. Defaults to |
n_boot |
The number of bootstrap samples. |
seed |
The seed to reproduce the bootstrap results. The default is missing, in which case, the seed from the environment will be used. |
We use the following steps to obtain the hazard ratio estimate and confidence interval had there been no treatment switching:
Fit an AFT model to post-progression survival data to estimate
the causal parameter \psi
based on the patients
in the control group who had disease progression.
Derive the counterfactual survival times for control patients had there been no treatment switching.
Fit the Cox proportional hazards model to the observed survival times for the experimental group and the counterfactual survival times for the control group to obtain the hazard ratio estimate.
If bootstrapping is used, the confidence interval and corresponding
p-value for hazard ratio are calculated based on a t-distribution with
n_boot - 1
degrees of freedom.
A list with the following components:
psi
: The estimated causal parameter for the control group.
psi_CI
: The confidence interval for psi
.
psi_CI_type
: The type of confidence interval for psi
,
i.e., "AFT model" or "bootstrap".
logrank_pvalue
: The two-sided p-value of the log-rank test
for the ITT analysis.
cox_pvalue
: The two-sided p-value for treatment effect based on
the Cox model applied to counterfactual unswitched survival times.
If boot
is TRUE
, this value represents the
bootstrap p-value.
hr
: The estimated hazard ratio from the Cox model.
hr_CI
: The confidence interval for hazard ratio.
hr_CI_type
: The type of confidence interval for hazard ratio,
either "Cox model" or "bootstrap".
data_aft
: A list of input data for the AFT model by treatment
group. The variables include id
, stratum
, "pps"
,
"event"
, "swtrt"
, base2_cov
, pd_time
,
swtrt_time
, and time
.
fit_aft
: A list of fitted AFT models by treatment group.
data_outcome
: The input data for the outcome Cox model
of counterfactual unswitched survival times.
The variables include id
, stratum
, "t_star"
,
"d_star"
, "treated"
, base_cov
, and treat
.
fit_outcome
: The fitted outcome Cox model.
settings
: A list with the following components:
aft_dist
: The distribution for time to event for the AFT
model.
strata_main_effect_only
: Whether to only include the strata
main effects in the AFT model.
recensor
: Whether to apply recensoring to counterfactual
survival times.
admin_recensor_only
: Whether to apply recensoring to
administrative censoring times only.
swtrt_control_only
: Whether treatment switching occurred
only in the control group.
alpha
: The significance level to calculate confidence
intervals.
ties
: The method for handling ties in the Cox model.
offset
: The offset to calculate the time to event, PD, and
treatment switch.
boot
: Whether to use bootstrap to obtain the confidence
interval for hazard ratio.
n_boot
: The number of bootstrap samples.
seed
: The seed to reproduce the bootstrap results.
psi_trt
: The estimated causal parameter for the experimental
group if swtrt_control_only
is FALSE
.
psi_trt_CI
: The confidence interval for psi_trt
if
swtrt_control_only
is FALSE
.
hr_boots
: The bootstrap hazard ratio estimates if boot
is
TRUE
.
psi_boots
: The bootstrap psi
estimates if boot
is
TRUE
.
psi_trt_boots
: The bootstrap psi_trt
estimates if
boot
is TRUE
and swtrt_control_only
is
FALSE
.
Kaifeng Lu, kaifenglu@gmail.com
Nicholas R Latimer, KR Abrams, PC Lambert, MK Crowther, AJ Wailoo, JP Morden, RL Akehurst, and MJ Campbell. Adjusting for treatment switching in randomised controlled trials - A simulation study and a simplified two-stage method. Statistical Methods in Medical Research. 2017;26(2):724-751.
library(dplyr)
# the eventual survival time
shilong1 <- shilong %>%
arrange(bras.f, id, tstop) %>%
group_by(bras.f, id) %>%
slice(n()) %>%
select(-c("ps", "ttc", "tran"))
# the last value of time-dependent covariates before pd
shilong2 <- shilong %>%
filter(pd == 0 | tstart <= dpd) %>%
arrange(bras.f, id, tstop) %>%
group_by(bras.f, id) %>%
slice(n()) %>%
select(bras.f, id, ps, ttc, tran)
# combine baseline and time-dependent covariates
shilong3 <- shilong1 %>%
left_join(shilong2, by = c("bras.f", "id"))
# apply the two-stage method
fit1 <- tsesimp(
data = shilong3, id = "id", time = "tstop", event = "event",
treat = "bras.f", censor_time = "dcut", pd = "pd",
pd_time = "dpd", swtrt = "co", swtrt_time = "dco",
base_cov = c("agerand", "sex.f", "tt_Lnum", "rmh_alea.c",
"pathway.f"),
base2_cov = c("agerand", "sex.f", "tt_Lnum", "rmh_alea.c",
"pathway.f", "ps", "ttc", "tran"),
aft_dist = "weibull", alpha = 0.05,
recensor = TRUE, swtrt_control_only = FALSE, offset = 1,
boot = FALSE)
c(fit1$hr, fit1$hr_CI)
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