Map which contrasts are estimable, and on what evidence, across populations

Estimability under population adjustment is itself a function of the target population: a contrast identified at the covariate origin need not be identified in a population where the relevant component by effect-modifier interactions are not pinned down. This plot evaluates estimable_effects_at() over a grid of target populations and tiles the result, separating contrasts identified by IPD (a within-study interaction, which randomization protects) from those identified only ecologically, from between-study differences in aggregate covariate means (which randomization does not protect; Berlin et al. 2002).

Usage

plot_estimability(object, em, values, at = NULL, reference = NULL, ...)

Arguments

object: A cmlnmr() fit.
em: Name of the effect modifier to vary across the grid.
values: Numeric vector of target values for em.
at: Optional named vector fixing the other effect modifiers. Defaults to 0 for each.
reference: Reference treatment. Defaults to the fit's reference.
...: Unused.

Value

A ggplot object.

Details

There is no counterpart in multinma.

References

Berlin JA, Santanna J, Schmid CH, Szczech LA, Feldman HI (2002). Individual patient- versus group-level data meta-regressions for the investigation of treatment effect modifiers. Statistics in Medicine, 21(3), 371–387.

Examples

if (FALSE) {
plot_estimability(fit, em = "x1", values = seq(-1, 1, by = 0.5))
}