View source: R/cmest_multistate.R
cmest_multistate | R Documentation |
cmest_multistate
is used to implement the multistate approach by Valeri et al. (2023)
for causal mediation analysis.
cmest_multistate(
data = NULL,
outcome = NULL,
yevent = NULL,
mediator = NULL,
mevent = NULL,
exposure = NULL,
EMint = NULL,
basec = NULL,
basecval = NULL,
ymreg = "coxph",
astar = NULL,
a = NULL,
nboot = 200,
bh_method = "breslow",
s = NULL,
multistate_seed = 123,
n_workers = NULL
)
data |
a data frame (or object coercible by as.data.frame to a data frame) containing the variables in the model. |
outcome |
variable name of the outcome. |
yevent |
variable name of the event for the outcome. |
mediator |
variable name of the mediator. |
mevent |
Event indicator for the mediator in multistate modeling. |
exposure |
variable name of the exposure. |
EMint |
a logical value. |
basec |
a vector of variable names of the confounders. |
basecval |
(required when |
ymreg |
type of multistate survival model to be used. Currently supporting coxph only. |
astar |
the control value of the exposure. |
a |
the treatment value of the exposure. |
nboot |
(used when |
bh_method |
Method for estimating baseline hazards in multistate modeling. Currently supporting "breslow" only. |
s |
The time point(s) beyond which survival probability is interested in multistate modeling. |
multistate_seed |
The seed to be used when generating bootstrap datasets for multistate modeling. |
Assumptions of the multistate method
Consistency of potential outcomes: For each i and each t, the survival in a world where we intervene, i.e., setting the time to treatment to a specific value t (via a fixed or stochastic intervention) is the same as the survival in the real world where we observe a time to treatment equal to t.
There is no unmeasured mediator-outcome confounding: Given exposure
and
basec
, mediator
is independent of outcome
.
Non-informative censoring of event times: The observed censoring time is conditionally independent of all potential event times.
Positivity: Each exposure-covariate combination has a non-zero probability of occurring.
The output is a list that consists of 4 elements:
the model summary of the joint multistate Cox proportional hazards model fitted on the original dataset
the point estimates of RD and SD for each of the user-specified time points of interest on the original dataset
the summary of the bootstrapped RD, SD, and TE estimates for each of the user-specified time point of interest, including the 2.5, 50, and 97.5th percentiles
the estimated RD, SD, TD for each of the user-specified time point of interest for each bootstrap dataset
Valeri L, Proust-Lima C, Fan W, Chen JT, Jacqmin-Gadda H. A multistate approach for the study of interventions on an intermediate time-to-event in health disparities research. Statistical Methods in Medical Research. 2023;32(8):1445-1460.
## Not run:
library(CMAverse)
multistate_out = cmest_multistate(data = sc_data,
s = s_vec,
multistate_seed = 1,
exposure = 'A', mediator = 'M', outcome = 'S',
yevent = "ind_S", mevent = "ind_M",
basec = c("C1", "C2"),
basecval = c("C1" = "1", "C2" = as.character(mean(sc_data$C2))),
astar="0", a="1",
nboot=1, EMint=F,
bh_method = "breslow")
multistate_out
## End(Not run)
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