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There are 4 types of treatment in my data. To balance the covariables of different treatment groups, I have used twang::mnps function to perform inverse probability weighting and successfully got the weights. ASMDs shows that the covariables between these four groups are balanced well with weights. But I cannot find a method to perform multivariable competing risks regression in my data after IPW. I only find that cmprsk::crr function can be used to perform competing risks regression for multivariables. But there is no argument for weight in the crr function. So, how to perform weighted multivariable competing risks regression after IPW?

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2 Answers 2

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I think you can use the cmprskcoxmsm package https://cran.r-project.org/web/packages/cmprskcoxmsm/cmprskcoxmsm.pdf

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If you are trying to estimate weighted cumulative incidence functions from competing risks data, you can use the adjustedcif() function from the adjustedCurves package. Small example here:

library(adjustedCurves)
library(survival)

set.seed(42)

# simulate some example data
sim_dat <- sim_confounded_crisk(n=500, max_t=1.2)
sim_dat$group <- as.factor(sim_dat$group)

# using direct adjustment with asymptotic confidence intervals for cause 1
# NOTE: if you already have the weights, simply pass those to the
#       'treatment_model' argument
adjcif <- adjustedcif(data=sim_dat,
                      variable="group",
                      ev_time="time",
                      event="event",
                      cause=1,
                      method="iptw_pseudo",
                      treatment_model=group ~ x2 + x4 + x5 + x6,
                      conf_int=TRUE,
                      bootstrap=FALSE)
plot(adjcif)
```
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