Author: Hamid Bulut
Date: 2026-06-18

# Reporting MAIHDA results: tidy output and publication tables

#### Hamid Bulut

#### 2026-06-18

## From a fitted model to a manuscript

This vignette covers the three reporting helpers that turn a `maihda()` analysis into publication-ready output without you recomputing anything: `glance()` (the one-row headline), `tidy()` (estimates as a tidy tibble), and `maihda_table()` (the two canonical write-up tables). They read the quantities `summary()` already computed, so the tables always agree with `print()`/`plot()`.

    library(MAIHDA)
    data("maihda_health_data")

    a <- maihda(
      BMI ~ Age + Gender + Race + Education + (1 | Gender:Race:Education),
      data = maihda_health_data
    )

## `glance()` – the one-row headline

`glance()` returns the whole MAIHDA headline as a one-row tibble: the VPC/ICC (with its interval when bootstrapped), the PCV, the additive/interaction shares, the discriminatory accuracy (AUC/MOR) for a binary outcome, and the bookkeeping (`n_strata`, `nobs`, `engine`, `family`, `mode`).

    glance(a)
    #> # A tibble: 1 × 16
    #>      vpc vpc.conf.low vpc.conf.high   pcv pcv.conf.low pcv.conf.high
    #>    <dbl>        <dbl>         <dbl> <dbl>        <dbl>         <dbl>
    #> 1 0.0627           NA            NA 0.766           NA            NA
    #> # ℹ 10 more variables: additive.share <dbl>, interaction.share <dbl>,
    #> #   auc <dbl>, auc.adjusted <dbl>, mor <dbl>, n_strata <int>, nobs <int>,
    #> #   engine <chr>, family <chr>, mode <chr>

This is the row no generic mixed-model tool emits, because the PCV needs the null + adjusted pair that only a `maihda()` analysis holds. It is ideal for `rbind()`-ing many analyses into a comparison table, or for pulling a single number into inline text – e.g. the VPC is 6.3% and the additive share (PCV) is 76.6%.

`tidy()` and `glance()` come from the lightweight `generics` package – the same generics `broom`/`broom.mixed` re-export – so they dispatch whether you have `broom`, `generics`, or just `MAIHDA` loaded. There is no hard `broom` dependency.

## `tidy()` – estimates as a tidy tibble

`tidy()` returns MAIHDA estimates in broom’s `estimate`/`std.error`/`conf.low`/ `conf.high` shape, ready for `dplyr`, `ggplot2`, or a table package. Three `component`s are available.

The stratum estimates (the default) – one row per intersection, with the human-readable label:

    strata <- tidy(a, component = "strata")
    head(strata)
    #> # A tibble: 6 × 6
    #>   stratum label                          estimate std.error conf.low conf.high
    #>   <chr>   <chr>                             <dbl>     <dbl>    <dbl>     <dbl>
    #> 1 1       male × Hispanic × Some College   -0.245     1.04   -2.29      1.80
    #> 2 2       male × Black × College Grad       0.772     1.11   -1.41      2.96
    #> 3 3       female × White × College Grad    -1.82      0.352  -2.51     -1.13
    #> 4 4       male × Hispanic × 8th Grade       0.921     1.32   -1.66      3.51
    #> 5 5       female × Mexican × 8th Grade      1.82      0.951  -0.0409    3.69
    #> 6 6       male × White × College Grad      -0.743     0.357  -1.44     -0.0431

The variance components:

    tidy(a, component = "variance")
    #> # A tibble: 3 × 4
    #>   component                 variance    sd proportion
    #>   <chr>                        <dbl> <dbl>      <dbl>
    #> 1 Between-stratum (random)      2.90  1.70     0.0627
    #> 2 Within-stratum (residual)    43.4   6.59     0.937
    #> 3 Total                        46.3   6.80     1

And the fixed effects (`component = "fixed"`). For a two-model analysis, `which = "adjusted"` reads the adjusted model instead of the default null:

    tidy(a, component = "fixed", which = "adjusted")
    #> # A tibble: 11 × 5
    #>    term                    estimate std.error conf.low conf.high
    #>    <chr>                      <dbl>     <dbl>    <dbl>     <dbl>
    #>  1 (Intercept)              30.1           NA       NA        NA
    #>  2 Age                       0.0148        NA       NA        NA
    #>  3 Gendermale               -0.431         NA       NA        NA
    #>  4 RaceHispanic             -1.62          NA       NA        NA
    #>  5 RaceMexican              -1.06          NA       NA        NA
    #>  6 RaceWhite                -1.67          NA       NA        NA
    #>  7 RaceOther                -3.92          NA       NA        NA
    #>  8 Education9 - 11th Grade   0.129         NA       NA        NA
    #>  9 EducationHigh School      1.08          NA       NA        NA
    #> 10 EducationSome College    -0.177         NA       NA        NA
    #> 11 EducationCollege Grad    -0.960         NA       NA        NA

Because the output is a plain tibble, you can build your own figure instead of using the built-in `plot()` – here a caterpillar of the stratum interaction estimates, ordered by magnitude:

    library(ggplot2)

    strata_ord <- strata[order(strata$estimate), ]
    strata_ord$label <- factor(strata_ord$label, levels = strata_ord$label)

    ggplot(strata_ord, aes(x = estimate, y = label)) +
      geom_vline(xintercept = 0, linetype = "dashed", colour = "grey60") +
      geom_pointrange(aes(xmin = conf.low, xmax = conf.high)) +
      labs(x = "Stratum random effect (BLUP)", y = NULL,
           title = "Intersectional strata, ordered by estimated deviation") +
      theme_minimal()

## `maihda_table()` – the two canonical write-up tables

`maihda_table()` assembles the two deliverables a MAIHDA paper reports (cf. Evans et al. 2024): a model-results table contrasting the null (Model 1) and adjusted (Model 2) fits, and a ranked-strata table ordering the intersections by predicted outcome. The `print()` method shows both in the familiar “estimate [low, high]” layout:

    tab <- maihda_table(a)
    tab
    #> MAIHDA Results Table
    #> ====================
    #>
    #> Engine: lme4 | Family: gaussian | Mode: two-model
    #> Observations: 3000 | Strata: 50
    #>
    #> Model results:
    #>                 Statistic Null (Model 1) Adjusted (Model 2)
    #>                 Intercept         28.208             30.050
    #>  Between-stratum variance          2.900              1.172
    #>        Between-stratum SD          1.703              1.083
    #>                   VPC/ICC          0.063              0.026
    #>    PCV (null -> adjusted)                             0.766
    #>
    #> Strata ranked by predicted value (null model):
    #>  Rank                          Stratum   N               Predicted
    #>     1     female × Black × High School  46 33.274 [31.621, 34.927]
    #>     2    female × Black × Some College  76 31.413 [30.060, 32.766]
    #>     3  female × Black × 9 - 11th Grade  29 31.124 [29.177, 33.071]
    #>     4     female × Mexican × 8th Grade  33 30.759 [28.895, 32.623]
    #>     5  male × Mexican × 9 - 11th Grade  34 30.206 [28.361, 32.051]
    #>     6     female × Other × High School   9 30.099 [27.462, 32.736]
    #>     7    female × Black × College Grad  30 30.044 [28.119, 31.969]
    #>     8    male × Mexican × College Grad  11 30.036 [27.503, 32.570]
    #>     9   female × Mexican × High School  20 30.007 [27.824, 32.190]
    #>    10      male × Hispanic × 8th Grade  10 29.929 [27.345, 32.513]
    #>   ...                              ... ...                     ...
    #>    41 female × Hispanic × Some College  26 27.762 [25.745, 29.779]
    #>    42       female × Other × 8th Grade  12 27.741 [25.255, 30.227]
    #>    43  female × Mexican × Some College  25 27.739 [25.697, 29.781]
    #>    44  female × Other × 9 - 11th Grade   7 27.610 [24.855, 30.365]
    #>    45         male × Other × 8th Grade  11 27.304 [24.771, 29.838]
    #>    46    female × White × College Grad 335 27.073 [26.383, 27.763]
    #>    47    female × Other × Some College  37 26.795 [25.004, 28.585]
    #>    48    male × Other × 9 - 11th Grade  14 26.758 [24.359, 29.157]
    #>    49      male × Other × College Grad  44 26.669 [24.988, 28.351]
    #>    50    female × Other × College Grad  46 25.913 [24.259, 27.566]
    #>               Stratum RE
    #>     4.367 [2.714, 6.020]
    #>     2.594 [1.241, 3.947]
    #>     2.174 [0.227, 4.121]
    #>    1.823 [-0.041, 3.687]
    #>    1.370 [-0.475, 3.215]
    #>    1.101 [-1.537, 3.738]
    #>    1.115 [-0.810, 3.040]
    #>    1.279 [-1.254, 3.813]
    #>    1.195 [-0.989, 3.378]
    #>    0.921 [-1.663, 3.505]
    #>                      ...
    #>   -0.957 [-2.974, 1.060]
    #>   -1.231 [-3.717, 1.255]
    #>   -0.935 [-2.977, 1.107]
    #>   -1.240 [-3.995, 1.515]
    #>   -1.695 [-4.229, 0.839]
    #>  -1.820 [-2.510, -1.130]
    #>  -2.066 [-3.857, -0.275]
    #>   -2.125 [-4.524, 0.273]
    #>  -2.145 [-3.826, -0.464]
    #>  -2.865 [-4.518, -1.212]
    #>   (30 strata between ranks 11 and 40 not shown; see $strata)
    #>   Predicted intervals are conditional (random-effect) only; Stratum RE is the stratum BLUP.
    #>
    #> Estimates are point values unless a [low, high] interval is shown (VPC/PCV).

The `$models` element is a numeric, export-ready data frame (statistics in rows, a column per model with `*_lower`/`*_upper` interval columns), so it drops straight into `knitr::kable()` or `write.csv()`:

    knitr::kable(tab$models, digits = 3,
                 caption = "MAIHDA model-results table (null vs. adjusted).")

MAIHDA model-results table (null vs. adjusted).
statistic | null | null_lower | null_upper | adjusted | adjusted_lower | adjusted_upper
--- | --- | --- | --- | --- | --- | ---
Intercept | 28.208 | NA | NA | 30.050 | NA | NA
Between-stratum variance | 2.900 | NA | NA | 1.172 | NA | NA
Between-stratum SD | 1.703 | NA | NA | 1.083 | NA | NA
VPC/ICC | 0.063 | NA | NA | 0.026 | NA | NA
PCV (null -> adjusted) | NA | NA | NA | 0.766 | NA | NA

    write.csv(tab$models, "maihda_results.csv", row.names = FALSE)

The `$strata` element holds every stratum ranked by predicted BMI (the data behind the printed top/bottom rows):

    head(tab$strata)
    #>   rank stratum                           label  n predicted predicted_lower
    #> 1    1      26    female × Black × High School 46  33.27402        31.62075
    #> 2    2      29   female × Black × Some College 76  31.41301        30.05958
    #> 3    3      45 female × Black × 9 - 11th Grade 29  31.12401        29.17711
    #> 4    4       5    female × Mexican × 8th Grade 33  30.75902        28.89508
    #> 5    5      34 male × Mexican × 9 - 11th Grade 34  30.20626        28.36146
    #> 6    6      32    female × Other × High School  9  30.09881        27.46154
    #>   predicted_upper random_effect    re_lower re_upper
    #> 1        34.92729      4.366992  2.71372474 6.020259
    #> 2        32.76643      2.594013  1.24058985 3.947436
    #> 3        33.07091      2.174211  0.22730689 4.121115
    #> 4        32.62295      1.823058 -0.04088002 3.686996
    #> 5        32.05106      1.369987 -0.47481559 3.214789
    #> 6        32.73607      1.100737 -1.53652679 3.738002

If you use `gt` or `flextable` for formatted tables, the same numeric frame feeds them directly (shown only if `gt` is installed):

    gt::gt(tab$models)

## Choosing a model structure with `maihda_ic()`

The VPC and PCV do not tell you whether a different model specification (another covariate set, strata definition, or family) fits better. `maihda_ic()` answers that with information criteria – `AIC`/`BIC` for the likelihood engines, `WAIC`/`LOOIC` for brms – and a `delta` column (gap from the best model):

    maihda_ic(a)
    #> MAIHDA Information Criteria
    #> ===========================
    #>
    #>              model    n            estimator df logLik   AIC   BIC delta
    #>      Model1 (Null) 3000 ML (refit from REML)  4  -9940 19889 19913  15.5
    #>  Model1 (Adjusted) 3000 ML (refit from REML) 13  -9924 19873 19951   0.0
    #>
    #> delta = difference from the best model on AIC (lower is better).
    #> REML lmer fit(s) were refitted with ML so AIC/BIC are comparable across different fixed effects.
    #> Information criteria are only comparable across models fitted to the same analytic sample (and, for AIC/BIC, the same family).

## See also

- Introduction to MAIHDA – the end-to-end workflow.
- Finding interaction patterns – which strata show the clearest residual deviations from additive expectations.
- Interpreting MAIHDA plots and diagnostics – and how to restyle the built-in plots.

## References

- Evans, C. R., Leckie, G., Subramanian, S. V., Bell, A., & Merlo, J. (2024). A tutorial for conducting intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA). SSM - Population Health, 26,