multimedia (version 0.2.0)

fdr_summary: Calibration using Synthetic Nulls

Description

This function computes a threshold for indirect or direct effect estimates that controls the false discovery rate according to estimates made using real and synthetic null data, against the null hypotheses that effects are zero. It computes the proportion of synthetic null estimates that are among the top K largest effects (in magnitude) as an estimate of the FDR.

Usage

fdr_summary(contrast, effect = "indirect_overall", q_value = 0.15)

Value

fdr A data.frame specifying, for each candidate effect, whether it should be selected.

Arguments

contrast

A data.frame summarizing the differences between outcomes across hypothetical treatments, typically as output by null_contrast. Each row is one outcome in one hypothetical scenario.

effect

Either "indirect_overall" (the default), "indirect_pathwise", or "direct_effect" specifying the type of effect that we want to select.

q_value

The target for false discovery rate control. The last time the estimated FDR is above this threshold is smallest magnitude of effect size that we will consider.

Examples

# example with null data - notice synthetic data has larger effect.
exper <- demo_joy() |>
    mediation_data("PHQ", "treatment", starts_with("ASV"))
multimedia(exper) |>
    estimate(exper) |>
    null_contrast(exper) |>
    fdr_summary("direct_effect")

multimedia(exper) |>
    estimate(exper) |>
    null_contrast(exper, "M->Y", indirect_overall) |>
    fdr_summary("indirect_overall")

# example with another dataset - synthetic effect is smaller.
exper <- demo_spline(tau = c(2, 1)) |>
    mediation_data(starts_with("outcome"), "treatment", "mediator")
multimedia(exper) |>
    estimate(exper) |>
    null_contrast(exper) |>
    fdr_summary("direct_effect")

multimedia(exper) |>
    estimate(exper) |>
    null_contrast(exper, "M->Y", indirect_overall) |>
    fdr_summary("indirect_overall")