# ANCOVA with Combined Treatment Groups

#### C&SP Methodology Team

# Overview

This vignette provides an high-level illustration of ANCOVA with combined treatment groups in `junco`.
The main purpose is to highlight the ability to construct tables with combined groups using `junco` function `a_summarize_ancova_j()`.
The various options such as weighting strategies, interaction models, and alternative estimands will be described.

# Clinical Context

ANCOVA is commonly used for covariate-adjusted analyses of change from baseline in clinical trials. Regulatory tables often require pooling of treatment arms while preserving the original estimand.

# Example Dataset

The dataset used as an example is a small dataset with simulated values for `BASE` and `CHG`.

    library(dplyr)
    library(rtables)
    #> Warning: package 'rtables' was built under R version 4.5.2
    #> Warning: package 'formatters' was built under R version 4.5.2
    library(junco)

    set.seed(101)

    adchg <- tibble(
      USUBJID = sprintf("SUBJ-%03d", 1:300),
      TRT01A = factor(rep(c("Placebo", "Low Dose", "High Dose"), length.out = 300)),
      REGION = factor(sample(c("EU", "US"), 300, replace = TRUE, prob = c(0.6, 0.4))),
      BASE = rnorm(300, 50, 10),
      CHG  = rnorm(300, 0, 8)
    )

# Combined Column Strategy

A combined treatment column can be added in an `rtables` layout, by utilizing a `combodf` dataframe.
As seen further in this document, adding `split_fun = add_combo_levels(combodf)` in `split_cols_by` call, will result in a combined treatment column in the final table.

    combodf <- tibble::tribble(
      ~valname,   ~label,    ~levelcombo,                     ~exargs,
      "ACTIVE",   "Active", c("Low Dose", "High Dose"), list()
    )

# ANCOVA Model

The ANCOVA model used here is:

    CHG ~ TRT01A + BASE + REGION

The model is fitted once using the original randomized treatment variable, for all columns, including the combined column.

# Least-squares means approach

The recommended approach for dealing with a combined treatment group column in an ANCOVA table, is to derive the estimates for that column as contrasts (least-squares means) from the linear ANCOVA model.
This can be achieved by specifying the argument `method_combo = "contrasts"`.

    lyt <- basic_table() |>
      split_cols_by(
        "TRT01A",
        ref_group = "Placebo",
        split_fun = add_combo_levels(combodf)
      ) %>%
      add_colcounts() |>
      analyze(
        vars = "CHG",
        afun = a_summarize_ancova_j,
        var_labels = "Change from Baseline",
        extra_args = list(
          variables = list(arm = "TRT01A", covariates = c("BASE", "REGION")),
          ref_path = c("TRT01A", "Placebo"),
          method_combo = "contrasts",
          weights_combo = "equal",
          weights_emmeans = "equal",
          conf_level = 0.95
        )
      )

    build_table(lyt, adchg)
    #>                                              High Dose             Low Dose               Placebo               Active
    #>                                               (N=100)               (N=100)               (N=100)               (N=200)
    #> —————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
    #> n                                               100                   100                   100                   200
    #> Mean (SD)                                  -0.76 (7.541)         -0.12 (7.490)         -1.16 (6.510)         -0.44 (7.504)
    #> Median                                         -0.27                 -0.13                 -1.60                 -0.18
    #> Min, max                                    -22.1, 16.8           -15.2, 15.6           -16.2, 17.8           -22.1, 16.8
    #> 25% and 75%-ile                             -6.51, 4.08           -5.82, 4.25           -5.68, 2.48           -6.44, 4.15
    #> Adjusted Mean (SE)                         -0.73 (0.72)          -0.03 (0.72)          -0.97 (0.73)          -0.38 (0.51)
    #> Adjusted Mean (95% CI)                  -0.73 (-2.15, 0.68)   -0.03 (-1.45, 1.40)   -0.97 (-2.41, 0.47)   -0.38 (-1.38, 0.63)
    #> Difference in Adjusted Means (95% CI)   0.24 (-1.78, 2.26)    0.95 (-1.06, 2.95)                          0.59 (-1.15, 2.34)
    #>   p-value                                      0.815                 0.354                                       0.504

## Weighting Strategies

Note the argument `weights_combo` in the above layout definition. When defining contrasts in a linear model, weights can specified to use in averaging predictions. See `?emmeans::emmeans()` for more details.

The following 3 weight scenarios are available for `weights_combo`:

- `equal`: equal contribution of arms
- `proportional`: weighted by observed sample size
- `proportional_marginal`: marginal weights across interaction strata

If no interaction term is included in the model, the choices `proportional` and `proportional_marginal` lead to the same weights (by observed sample size), reducing to 2 options only.
While, if an interaction, for example a term like `TRT01A * REGION` is included, `proportional` and `proportional_marginal` are slightly different, with `proportional` according to the proportions observed in the interaction strata.

# Collapsing Treatment Arms

The option `method_combo = "collapse"` is available as alternative approach, mainly as a quick verification method, and for consistency reason with another internal system.
In this scenario, the ANCOVA model is re-fitted on pooled arms and as such changes the estimand.
While it is available as an option, our recommendation is to use the `contrasts` approach, and only utilize `method_combo = "collapse"` when explicitly specified in the SAP.

# Summary

- The `method_combo = "contrasts"` approach is aligned with `emmeans` theory
- Combined LS means and differences are implemented as linear contrasts
- Full covariance propagation is preserved
- Interaction models require explicit decisions on within- and across-stratum weighting

See also [junco::a_summarize_ancova_j()].

# Statistical Appendix: emmeans-based Estimation

## Notation

Let:

- \(\hat{\mu}_k\) denote the LS mean for treatment arm \(k\)
- \(\mathbf{\hat{\mu}} = (\hat{\mu}_1, \ldots, \hat{\mu}_K)^T\)
- \(\mathbf{V} = \mathrm{Var}(\mathbf{\hat{\mu}})\)

All quantities are obtained from the fitted ANCOVA model.

## A. Combined LS Means

A combined treatment LS mean for a set of arms \(C\) is defined as:

\[ \hat{\mu}_{C} = \sum_{k \in C} w_k \, \hat{\mu}_k , \qquad \sum_{k \in C} w_k = 1 \]

where \(w_k\) are user-specified weights (e.g. equal or proportional).

### Variance

The variance of the combined LS mean is:

\[ \mathrm{Var}(\hat{\mu}_{C}) = \mathbf{w}^T \, \mathbf{V} \, \mathbf{w} \]

This corresponds exactly to a linear contrast implemented via `emmeans::contrast()`.

## B. Contrasts Between Treatment Groups

### Simple Treatment Differences

The estimated difference between two individual treatment arms \(k\) and \(r\) (e.g. Active vs Placebo) is defined as the contrast:

\[ \hat{\delta}_{k,r} = \hat{\mu}_k - \hat{\mu}_r \]

with variance:

\[ \mathrm{Var}(\hat{\delta}_{k,r}) = \mathrm{Var}(\hat{\mu}_k) + \mathrm{Var}(\hat{\mu}_r) - 2 \, \mathrm{Cov}(\hat{\mu}_k, \hat{\mu}_r) \]

This covariance term is automatically accounted for when contrasts are derived from the joint covariance matrix \(\mathbf{V}\).

### Differences Involving Combined Arms

For a combined treatment \(C\) compared to reference arm \(r\):

\[ \hat{\delta}_{C,r} = \hat{\mu}_{C} - \hat{\mu}_r \]

This can be written as a single linear contrast with weight vector:

\[ \mathbf{w}_{C,r} = (w_1, \ldots, w_K, -1) \]

and variance:

\[ \mathrm{Var}(\hat{\delta}_{C,r}) = \mathbf{w}_{C,r}^T \, \mathbf{V} \, \mathbf{w}_{C,r} \]

This ensures internally consistent standard errors, confidence intervals, and p-values for combined treatment comparisons.

## C. Contrasts in Interaction Models

When the ANCOVA model includes interaction terms (e.g. `TRT01A * REGION`), LS means are estimated within each interaction stratum.

In this setting:

- Treatment-specific LS means \(\hat{\mu}_{k,s}\) are defined per stratum \(s\)
- Combined means are first formed within stratum
- Pooling across strata is then applied via user-defined weights

Formally, the combined LS mean marginal over strata is:

\[ \hat{\mu}_{C} = \sum_{s} \pi_s \, \Big( \sum_{k \in C} w_{k|s} \, \hat{\mu}_{k,s} \Big) \]

where:

- \(w_{k|s}\) are within-stratum treatment weights
- \(\pi_s\) are stratum-level weights

The distinction between `weights_combo = "proportional"` and `"proportional_marginal"` determines whether \(w_{k|s}\) or \(\pi_s\) reflect observed sample sizes within or across strata.

This two-stage weighting clarifies how estimands change in the presence of interactions and avoids implicit, undocumented averaging.