Author: Hamid Bulut
Date: 2026-06-18

# Finding interaction patterns

#### Hamid Bulut

#### 2026-06-18

## Overview

A common follow-up question is which strata depart most clearly from the additive expectation.

For this exploratory diagnostic, `maihda()` can compute the adjusted-model stratum random effects, intervals, and flags. When you use `interactions = "BH"`, the flags use the Benjamini-Hochberg adjustment. The diagnostic is stored in the analysis object and can be reused by the effect-decomposition and predicted-strata plots.

## Run a standard analysis and choose the multiplicity rule

The default is `adjust = "none"`. This reports each stratum-level interval and flag without a multiple-testing correction.

If the goal is to scan all strata and highlight a smaller set for follow-up, use an adjustment such as Benjamini-Hochberg:

    library(MAIHDA)
    model_bh <- maihda(
      BMI ~ Age + Gender + Race + Education + (1 | Gender:Race:Education),
      data = maihda_health_data,
      interactions = "BH" # Benjamini-Hochberg adjustment
    )

The printed output reports how many strata were flagged and which adjustment rule was used. The full table is stored in `model_bh$interactions`.

    model_bh$interactions
    #> Strata with credibly non-zero intersectional interaction
    #> ========================================================
    #>
    #> 1 of 50 strata flagged (95% interval; BH-adjusted p-values).
    #> Model: adjusted (two-model); interaction on the link (latent) scale.
    #>
    #>  stratum                       label   n interaction     se  lower upper
    #>        8 male × White × Some College 328       1.359 0.3448 0.6836 2.035
    #>    p_value p_adjusted flagged direction
    #>  8.056e-05   0.004028    TRUE     above
    #>
    #> Interaction BLUPs are shrunken (partially pooled) estimates; treat flags as
    #>   exploratory. See ?maihda_interactions.

Each row is one stratum. The main columns are:

- `interaction`: the adjusted-model stratum random effect, on the model scale.
- `lower` and `upper`: the interval for that random effect.
- `direction`: whether the stratum is above or below the additive expectation.
- `flagged`: whether the stratum passes the selected screening rule.

For frequentist fits, the table also includes the conditional standard error, p-value, and adjusted p-value when a correction is requested.

## Highlight flagged strata

The plotting methods can reuse the stored diagnostic. Because `model_bh` was fitted with `interactions = "BH"`, `highlight_interactions = TRUE` uses the Benjamini-Hochberg flags. In the effect-decomposition plot, the labels also follow that same flagged set.

    plot(model_bh, type = "effect_decomp", highlight_interactions = TRUE)

The same flags can be reused in the predicted-strata view.

    plot(model_bh, type = "predicted", highlight_interactions = TRUE)

If the analysis was fitted without a stored interaction diagnostic, pass the adjustment rule directly:

    plot(model, type = "effect_decomp", highlight_interactions = "BH")

## See also

- Introduction to MAIHDA, the main two-model workflow.
- Interpreting MAIHDA plots and diagnostics, how to read the effect-decomposition and predicted-strata plots.
- Bayesian MAIHDA for sparse intersections, sparse cells and uncertainty in variance components.

## References

- Evans, C. R., Williams, D. R., Onnela, J. P., & Subramanian, S. V. (2018). A multilevel approach to modeling health inequalities at the intersection of multiple social identities. Social Science & Medicine, 203, 64-73.

- Merlo, J. (2018). Multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) within an intersectional framework. Social Science & Medicine, 203, 74-80.