iptw_est | R Documentation |
The iptw method or importance weighting method estimates the ADRF by weighting the data with stabilized or non-stabilized weights.
iptw_est(Y, treat, treat_formula, numerator_formula, data, degree, treat_mod, link_function, ...)
Y |
is the the name of the outcome variable contained in |
treat |
is the name of the treatment variable contained in
|
treat_formula |
an object of class "formula" (or one that can be
coerced to that class) that regresses |
numerator_formula |
an object of class "formula" (or one that can be
coerced to that class) that regresses |
data |
is a dataframe containing |
degree |
is 1 for linear and 2 for quadratic outcome model. |
treat_mod |
a description of the error distribution to be used in the
model for treatment. Options include: |
link_function |
specifies the link function between the variables in
numerator or denominator and exposure, respectively.
For |
... |
additional arguments to be passed to the low level treatment regression fitting functions. |
This method uses inverse probability of treatment weighting to adjust for possible biases. For more details see Schafer and Galagate (2015) and Robins, Hernan, and Brumback (2000).
iptw_est
returns an object of class "causaldrf",
a list that contains the following components:
param |
parameter estimates for a iptw fit. |
t_mod |
the result of the treatment model fit. |
num_mod |
the result of the numerator model fit. |
weights |
the estimated weights. |
weight_data |
the weights. |
out_mod |
the outcome model. |
call |
the matched call. |
Schafer, J.L., Galagate, D.L. (2015). Causal inference with a continuous treatment and outcome: alternative estimators for parametric dose-response models. Manuscript in preparation.
van der Wal, Willem M., and Ronald B. Geskus. "IPW: an R package for inverse probability weighting." Journal of Statistical Software 43.13 (2011): 1-23.
Robins, James M and Hernan, Miguel Angel and Brumback, Babette. Marginal structural models and causal inference in epidemiology. Epidemiology 11.5 (2000): 550–560.
Zhu, Yeying and Coffman, Donna L and Ghosh, Debashis. A Boosting Algorithm for Estimating Generalized Propensity Scores with Continuous Treatments. Journal of Causal Inference 3.1 (2015): 25–40.
iptw_est
, ismw_est
,
reg_est
, aipwee_est
, wtrg_est
,
etc. for other estimates.
t_mod
, overlap_fun
to prepare the data
for use in the different estimates.
## Example from Schafer (2015). example_data <- sim_data iptw_list <- iptw_est(Y = Y, treat = T, treat_formula = T ~ B.1 + B.2 + B.3 + B.4 + B.5 + B.6 + B.7 + B.8, numerator_formula = T ~ 1, data = example_data, degree = 1, treat_mod = "Normal") sample_index <- sample(1:1000, 100) plot(example_data$T[sample_index], example_data$Y[sample_index], xlab = "T", ylab = "Y", main = "iptw estimate") abline(iptw_list$param[1], iptw_list$param[2], lty=2, lwd = 2, col = "blue") legend('bottomright', "iptw estimate", lty=2, lwd = 2, col = "blue", bty='Y', cex=1) rm(example_data, iptw_list, sample_index) ## Example from van der Wal, Willem M., and Ronald B. Geskus. (2011) #Simulate data with continuous confounder and outcome, binomial exposure. #Marginal causal effect of exposure on outcome: 10. n <- 1000 simdat <- data.frame(l = rnorm(n, 10, 5)) a.lin <- simdat$l - 10 pa <- exp(a.lin)/(1 + exp(a.lin)) simdat$a <- rbinom(n, 1, prob = pa) simdat$y <- 10*simdat$a + 0.5*simdat$l + rnorm(n, -10, 5) simdat[1:5,] temp_iptw <- iptw_est(Y = y, treat = a, treat_formula = a ~ l, numerator_formula = a ~ 1, data = simdat, degree = 1, treat_mod = "Binomial", link_function = "logit") temp_iptw[[1]] # estimated coefficients
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