ltmle (version 0.9-5)

summary.ltmle: Summarizing results from Longitudinal Targeted Maximum Likelihood Estimation (ltmle)

Description

These functions are methods for class ltmle or summary.ltmle objects.

Usage

## S3 method for class 'ltmle':
summary(object, estimator=ifelse(object$gcomp, "gcomp", "tmle"), ...)
## S3 method for class 'ltmleEffectMeasures':
summary(object, estimator=ifelse(object$gcomp, "gcomp", "tmle"), ...)
## S3 method for class 'ltmleMSM':
summary(object, estimator=ifelse(object$gcomp, "gcomp", "tmle"), ...)
## S3 method for class 'summary.ltmle':
print(x, ...)
## S3 method for class 'summary.ltmleMSM':
print(x, digits = max(3, getOption("digits") - 3), 
  signif.stars = getOption("show.signif.stars"), ...)
## S3 method for class 'summary.ltmleEffectMeasures':
print(x, ...)

Arguments

object

an object of class "ltmle" or "ltmleMSM" or "ltmleEffectMeasures", usually a result of a call to ltmle or ltmleMSM.

x

an object of class "summary.ltmle" or "summary.ltmleMSM" or "ltmleEffectMeasures", usually a result of a call to summary.ltmle or

estimator

character; one of "tmle", "iptw", "gcomp". The estimator for which to get effect measures. "tmle" is valid iff the original ltmle/ltmleMSM call used gcomp=FALSE. "gcomp" is valid iff the original ltmle/ltmleMSM call used gcomp=TRUE

digits

the number of significant digits to use when printing.

signif.stars

logical. If TRUE, significance stars are printed for each coefficient.

...

further arguments passed to or from other methods.

Value

• summary.ltmle returns an object of class "summary.ltmle", a list with components
• treatment
  a list with components summarizing the estimate of object

  • estimate- the parameter estimate of$E[Y_d]$
  • std.dev- estimated standard deviation of parameter
  • p.value- two-sided p-value
  • CI- vector of length 2 with 95 percent confidence interval
• call
  the matched call to ltmle for object
• estimator
  the estimator input argument
• variance.estimate.ratio
  ratio of the TMLE based variance estimate to the influence curve based variance estimate
• summary.ltmleEffectMeasures returns an object of class "summary.ltmleEffectMeasures", a list with same components as summary.ltmle above, but also includes:
• effect.measures
  a list with components, each with the same components as treatment in summary.ltmle above

  • treatment- corresponds to the first in the listabar(orrule) passed toltmle
  • control- corresponds to the second in the listabar(orrule) passed toltmle
  • ATE- average treatment effect
  • RR- relative risk
  • OR- odds ratio
• summary.ltmleMSM returns an object of class "summary.ltmleMSM", a matrix with rows for each MSM parameter and columns for the point estimate, standard error, 2.5percent confidence interval, 97.5percent confidence interval, and p-value.

Details

summary.ltmle returns the parameter value of the estimator, the estimated variance, a 95 percent confidence interval, and a p-value.

summary.ltmleEffectMeasures returns the additive treatment effect for each of the two objects in the abar list passed to ltmle. Relative risk, and odds ratio are also returned, along with the variance, confidence interval, and p-value for each.

summary.ltmleMSM returns a matrix of MSM parameter estimates.

See Also

ltmle, summary

Examples

rexpit <- function(x) rbinom(n=length(x), size=1, prob=plogis(x))

# Compare the expected outcomes under two counterfactual plans:
# Treatment plan: set A1 to 1 if W > 0, set A2 to 1 if W > 1.5, always set A3 to 1
# Control plan: always set A1, A2, and A3 to 0
W <- rnorm(1000)
A1 <- rexpit(W)
A2 <- rexpit(W + 2*A1)
A3 <- rexpit(2*A1 - A2)
Y <- rexpit(W - A1 + 0.5*A2 + 2*A3)
data <- data.frame(W, A1, A2, A3, Y)
treatment <- cbind(W > 0, W > 1.5, 1) 
control <- matrix(0, nrow=1000, ncol=3)
result <- ltmle(data, Anodes = c("A1", "A2", "A3"), Ynodes = "Y", abar = list(treatment, control))
print(summary(result))

## For examples of summary.ltmle and summary.ltmleMSM, see example(ltmle)