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Marginal treatment effects

Source: R/predict.R
marginal_effects.Rd

Extract marginal treatment effects from a fitted ML-UMR model. For binomial: log odds ratio, risk difference, risk ratio. For normal: mean difference. For poisson: rate ratio.

Usage

marginal_effects(
  object,
  population = c("both", "index", "comparator"),
  effect = "all",
  summary = TRUE,
  probs = c(0.025, 0.5, 0.975)
)

Arguments

object

An mlumr_fit object

population

Which population: "both", "index", or "comparator"

effect

Which effect measure. For binomial: "all", "lor", "rd", or "rr". For normal: "all" or "md" (mean difference). For poisson: "all" or "rr" (rate ratio).

summary

Return summary (TRUE) or full draws (FALSE)

probs

Quantiles for summary

Value

A data frame

See also

predict.mlumr_fit() for absolute predictions; conditional_effects() for covariate-conditional effects at specific profiles; prior_sensitivity() to check how strongly the marginal effect depends on prior_beta.

Examples

if (FALSE) { # \dontrun{
# All effect measures for both populations
marginal_effects(fit)

# Only the log odds ratio in the index population
marginal_effects(fit, population = "index", effect = "lor")

# Full posterior draws rather than summary statistics
marginal_effects(fit, summary = FALSE)
} # }