TERiskSummaries.CMA

Compare estimated Total Effect when all predictors are at a particular quantile to when all are at a second fixed quantile

TERiskSummaries.CMA(
  fit.TE,
  e.y = NULL,
  e.y.names = NULL,
  qs = seq(0.25, 0.75, by = 0.05),
  q.fixed = 0.5,
  alpha = 0.05,
  sel,
  seed = 122
)

Arguments

fit.TE

total effect model fit regressing outcome on exposures, effect modifiers and confounders on outcome

e.y

effect modifier for the outcome variable

e.y.names

column name of the effect modifier for the outcome variable

qs

vector of quantiles at which to calculate the overall risk summary

q.fixed

a second quantile at which to compare the estimated h function

alpha

1-confidence interval

sel

a vector selecting which iterations of the fit should be retained or inference

seed

the random seed to use to evaluate the code

Value

a data frame containing the (posterior mean) estimate and posterior standard deviation of the total effect risk measures

Details

For guided examples, go to https://zc2326.github.io/causalbkmr/articles/BKMRCMA_QuickStart.html

Examples

if (FALSE) {
library(causalbkmr)
riskSummary10 = TERiskSummaries.CMA(fit.TE = fit.y.TE, e.y=e.y10, e.y.name = "E.Y", sel=sel)

ggplot(riskSummary10,
       aes(quantile,
           est,
           ymin = est - 1.96 * sd,
           ymax = est + 1.96 * sd)) +
  geom_pointrange()
}