Relative treatment effects from a cpaic fit — relative

Tidies the relative effects of the fitted model: every treatment versus a chosen reference, or all pairwise comparisons. Effects are reported on the natural scale of the summary measure (e.g. odds ratios) unless backtransf = FALSE.

Usage

relative_effects(
  object,
  reference = NULL,
  all_contrasts = FALSE,
  backtransf = TRUE,
  level = 0.95,
  newdata = NULL,
  ...
)

Arguments

object: A fitted cpaic object (cpaic_bridge, cpaic_maic, cpaic_stc, or cpaic_mlnmr).
reference: Reference treatment. Defaults to the network reference.
all_contrasts: If TRUE, return all pairwise comparisons instead of versus the reference.
backtransf: If TRUE (default) back-transform log-scale measures (OR/RR/HR/...) by exponentiating.
level: Confidence level for the intervals. Default 0.95.
newdata: For cmlnmr() fits: a one-row data frame giving the target population's effect-modifier values. Required when the model has effect modifiers.
...: Unused.

Value

A data frame with columns treatment, comparator, estimate, se (link scale), lower, upper, and z/p for frequentist fits. For cmlnmr() (Bayesian) fits the intervals are credible intervals and the final column is pr_gt0, the posterior probability that the effect (on the link scale) exceeds zero, instead of z/p.

Details

Relative effects that the component design cannot uniquely identify (their contrast vector lies outside the row space of X = B C) are returned as NA rather than as pseudoinverse or prior-driven artefacts. See estimable_effects().

For cmlnmr() fits the model contains component x effect-modifier interactions, so relative effects are population-specific: theta_t(x) = C_t' (beta + gamma x). You must name the target population through newdata; there is no population-free relative effect.