Considers the ratio of means $$E(N(min(D,t)))/E(min(D,t))$$ and the the mean of the events per time unit $$E(N(min(D,t))/min(D,t))$$ both based on IPCW etimation. RMST estimator equivalent to Kaplan-Meier based estimator.
Usage
WA_recurrent(
formula,
data,
time = NULL,
cens.code = 0,
cause = 1,
death.code = 2,
trans = NULL,
cens.formula = NULL,
augmentR = NULL,
augmentC = NULL,
type = NULL,
marks = NULL,
...
)Arguments
- formula
Event formula first covariate on rhs must be a factor giving the treatment
- data
data frame
- time
for estimation
- cens.code
of censorings
- cause
of events
- death.code
of terminal events
- trans
possible power for mean of events per time-unit
- cens.formula
censoring model, default is to use strata(treatment)
- augmentR
covariates for model of mean ratio
- augmentC
covariates for censoring augmentation
- type
augmentation for call of binreg, when augmentC is given default is "I" and otherwise "II"
- marks
possible marks for composite outcome situation for model for counts with marks
- ...
arguments for binregATE
References
Nonparametric estimation of the Patient Weighted While-Alive Estimand arXiv preprint by A. Ragni, T. Martinussen, T. Scheike Mao, L. (2023). Nonparametric inference of general while-alive estimands for recurrent events. Biometrics, 79(3):1749–1760. Schmidli, H., Roger, J. H., and Akacha, M. (2023). Estimands for recurrent event endpoints in the presence of a terminal event. Statistics in Biopharmaceutical Research, 15(2):238–248.
Examples
library(mets)
data(hfactioncpx12)
dtable(hfactioncpx12,~status)
#>
#> status
#> 0 1 2
#> 617 1391 124
#>
dd <- WA_recurrent(Event(entry,time,status)~treatment+cluster(id),hfactioncpx12,time=2,death.code=2)
summary(dd)
#> While-Alive summaries:
#>
#> RMST, E(min(D,t))
#> Estimate Std.Err 2.5% 97.5% P-value
#> treatment0 1.859 0.02108 1.817 1.900 0
#> treatment1 1.924 0.01502 1.894 1.953 0
#>
#> Estimate Std.Err 2.5% 97.5% P-value
#> [treatment0] - [treat.... -0.06517 0.02588 -0.1159 -0.01444 0.0118
#>
#> Null Hypothesis:
#> [treatment0] - [treatment1] = 0
#> mean events, E(N(min(D,t))):
#> Estimate Std.Err 2.5% 97.5% P-value
#> treatment0 1.572 0.09573 1.384 1.759 1.375e-60
#> treatment1 1.453 0.10315 1.251 1.656 4.376e-45
#>
#> Estimate Std.Err 2.5% 97.5% P-value
#> [treatment0] - [treat.... 0.1185 0.1407 -0.1574 0.3943 0.4
#>
#> Null Hypothesis:
#> [treatment0] - [treatment1] = 0
#> _______________________________________________________
#> Ratio of means E(N(min(D,t)))/E(min(D,t))
#> Estimate Std.Err 2.5% 97.5% P-value
#> p1 0.8457 0.05264 0.7425 0.9488 4.411e-58
#> p2 0.7555 0.05433 0.6490 0.8619 5.963e-44
#>
#> Estimate Std.Err 2.5% 97.5% P-value
#> [p1] - [p2] 0.09022 0.07565 -0.05805 0.2385 0.233
#>
#> Null Hypothesis:
#> [p1] - [p2] = 0
#> _______________________________________________________
#> Mean of Events per time-unit E(N(min(D,t))/min(D,t))
#> Estimate Std.Err 2.5% 97.5% P-value
#> treat0 1.0725 0.1222 0.8331 1.3119 1.645e-18
#> treat1 0.7552 0.0643 0.6291 0.8812 7.508e-32
#>
#> Estimate Std.Err 2.5% 97.5% P-value
#> [treat0] - [treat1] 0.3173 0.1381 0.04675 0.5879 0.02153
#>
#> Null Hypothesis:
#> [treat0] - [treat1] = 0