# Informative prior archetypes

Informative prior archetypes allow users to conveniently set informative priors in `brms.mmrm` in a robust way, guarding against common pitfalls such as reference level issues, interpretation problems, and rank deficiency.

## Constructing an archetype

We begin with the FEV dataset from the `mmrm` package, an artificial (simulated) dataset of a clinical trial investigating the effect of an active treatment on FEV1 (forced expired volume in one second), compared to placebo. FEV1 is a measure of how quickly the lungs can be emptied and low levels may indicate chronic obstructive pulmonary disease (COPD).

The dataset is a tibble with 800 rows and 7 variables:

- `USUBJID` (subject ID),
- `AVISIT` (visit number),
- `ARMCD` (treatment, TRT or PBO),
- `RACE` (3-category race),
- `SEX` (sex),
- `FEV1_BL` (FEV1 at baseline, %),
- `FEV1` (FEV1 at study visits),
- `WEIGHT` (weighting variable).

We will derive `FEV1_CHG = FEV1 - FEV1_BL` and analyze `FEV1_CHG` as the outcome variable.

    library(brms.mmrm)
    data(fev_data, package = "mmrm")
    data <- fev_data |>
      brm_data(
        outcome = "FEV1",
        group = "ARMCD",
        time = "AVISIT",
        patient = "USUBJID",
        reference_time = "VIS1",
        reference_group = "PBO",
        covariates = c("WEIGHT", "SEX")
      ) |>
      brm_data_chronologize(order = "VISITN")
    data
    #> # A tibble: 800 × 10
    #>    USUBJID AVISIT ARMCD RACE                SEX   FEV1_BL  FEV1 WEIGHT VISITN VISITN2
    #>    <fct>   <ord>  <fct> <fct>               <fct>   <dbl> <dbl>  <dbl>  <int>   <dbl>
    #>  1 PT2     VIS1   PBO   Asian               Male     45.0  NA    0.465      1  0.330
    #>  2 PT2     VIS2   PBO   Asian               Male     45.0  31.5  0.233      2 -0.820
    #>  3 PT2     VIS3   PBO   Asian               Male     45.0  36.9  0.360      3  0.487
    #>  4 PT2     VIS4   PBO   Asian               Male     45.0  48.8  0.507      4  0.738
    #>  5 PT3     VIS1   PBO   Black or African A… Fema…    43.5  NA    0.682      1  0.576
    #>  6 PT3     VIS2   PBO   Black or African A… Fema…    43.5  36.0  0.892      2 -0.305
    #>  7 PT3     VIS3   PBO   Black or African A… Fema…    43.5  NA    0.128      3  1.51
    #>  8 PT3     VIS4   PBO   Black or African A… Fema…    43.5  37.2  0.222      4  0.390
    #>  9 PT5     VIS1   PBO   Black or African A… Male     43.6  32.3  0.411      1 -0.0162
    #> 10 PT5     VIS2   PBO   Black or African A… Male     43.6  NA    0.422      2  0.944
    #> # ℹ 790 more rows

The functions listed at https://openpharma.github.io/brms.mmrm/reference/index.html#informative-prior-archetypes can create different kinds of informative prior archetypes from a dataset like the one above. For example, suppose we want to place informative priors on the successive differences between adjacent time points. This approach is appropriate and desirable in many situations because the structure naturally captures the prior correlations among adjacent visits of a clinical trial. To do this, we create an instance of the “successive cells” archetype.

    archetype <- brm_archetype_successive_cells(data, baseline = FALSE)

The instance of the archetype is an ordinary tibble, but it adds new columns with prefixes `"x_"` and `"nuisance_"`. These new columns constitute a custom model matrix to describe the desired parameterization.

    archetype
    #> # A tibble: 800 × 20
    #>    x_PBO_VIS1 x_PBO_VIS2 x_PBO_VIS3 x_PBO_VIS4 x_TRT_VIS1 x_TRT_VIS2 x_TRT_VIS3
    #>  *      <dbl>      <dbl>      <dbl>      <dbl>      <dbl>      <dbl>      <dbl>
    #>  1          1          0          0          0          0          0          0
    #>  2          1          1          0          0          0          0          0
    #>  3          1          1          1          0          0          0          0
    #>  4          1          1          1          1          0          0          0
    #>  5          1          0          0          0          0          0          0
    #>  6          1          1          0          0          0          0          0
    #>  7          1          1          1          0          0          0          0
    #>  8          1          1          1          1          0          0          0
    #>  9          1          0          0          0          0          0          0
    #> 10          1          1          0          0          0          0          0
    #> # ℹ 790 more rows
    #> # ℹ 13 more variables: x_TRT_VIS4 <dbl>, nuisance_WEIGHT <dbl>,
    #> #   nuisance_SEX_Male <dbl>, USUBJID <fct>, AVISIT <ord>, ARMCD <fct>, RACE <fct>,
    #> #   SEX <fct>, FEV1_BL <dbl>, FEV1 <dbl>, WEIGHT <dbl>, VISITN <int>, VISITN2 <dbl>

We have effects of interest to express successive differences:

    attr(archetype, "brm_archetype_interest")
    #> [1] "x_PBO_VIS1" "x_PBO_VIS2" "x_PBO_VIS3" "x_PBO_VIS4" "x_TRT_VIS1" "x_TRT_VIS2"
    #> [7] "x_TRT_VIS3" "x_TRT_VIS4"

We also have nuisance variables. Some nuisance variables are continuous covariates, while others are levels of one-hot-encoded concomitant factors or interactions of those concomitant factors with baseline and/or subgroup. All nuisance variables are centered at their means so the reference level of the model is at the “center” of the data and not implicitly conditional on a subset of the data.1 In addition, some nuisance variables are automatically dropped in order to ensure the model matrix is full-rank, and automatic centering in `brms` is disabled2. This is critically important to preserve the interpretation of the columns of interest and make sure the informative priors behave as expected.

    attr(archetype, "brm_archetype_nuisance")
    #> [1] "nuisance_WEIGHT"   "nuisance_SEX_Male"

The factors of interest linearly map to marginal means. To see the mapping, call `summary()` on the archetype. The printed output helps build intuition on how the archetype is parameterized and what those parameters are doing.3

    summary(archetype)
    #> # This is the "successive cells" informative prior archetype in brms.mmrm.
    #> # The following equations show the relationships between the
    #> # marginal means (left-hand side) and fixed effect parameters
    #> # (right-hand side).
    #> #
    #> #   PBO:VIS1 = x_PBO_VIS1
    #> #   PBO:VIS2 = x_PBO_VIS1 + x_PBO_VIS2
    #> #   PBO:VIS3 = x_PBO_VIS1 + x_PBO_VIS2 + x_PBO_VIS3
    #> #   PBO:VIS4 = x_PBO_VIS1 + x_PBO_VIS2 + x_PBO_VIS3 + x_PBO_VIS4
    #> #   TRT:VIS1 = x_TRT_VIS1
    #> #   TRT:VIS2 = x_TRT_VIS1 + x_TRT_VIS2
    #> #   TRT:VIS3 = x_TRT_VIS1 + x_TRT_VIS2 + x_TRT_VIS3
    #> #   TRT:VIS4 = x_TRT_VIS1 + x_TRT_VIS2 + x_TRT_VIS3 + x_TRT_VIS4

Above, `x_PBO_VIS1` serves as the intercept, and `x_TRT_VIS1` is defined relative to `x_TRT_VIS1`. The rest of the parameters keep their original interpretations.

## Informative priors

Let’s assume you want to assign informative priors to the fixed effect parameters of interest declared in the archetype, such as `x_group_1_time_2` and `x_group_2_time_3`. Your priors may come from expert elicitation, historical data, or some other method, and you might consider distributional families recommended by the Stan team. However you construct these priors, `brms.mmrm` helps you assign them to the model without having to guess at the automatically-generated names of model coefficients in R.

In the printed output from `summary(archetype)`, parameters of interest such as `x_group_1_time_2` and `x_group_2_time_3` are always labeled using treatment groups and time points in the data (and subgroup levels, if applicable). This labeling mechanism is the same regardless of which archetype you choose, and it the way `brms.mmrm` helps you assign priors.

`brm_prior_label()` is one way to create a labeling scheme. Each call to `brm_prior_label()` below assigns a univariate prior to a fixed effect parameter. Each univariate prior is a Stan code string. Possible choices are documented in the Stan function reference at https://mc-stan.org/docs/functions-reference/unbounded_continuous_distributions.html.

    label <- NULL |>
      brm_prior_label(code = "student_t(4, -7.57, 4.96)", group = "PBO", time = "VIS1") |>
      brm_prior_label(code = "student_t(4,  3.14, 7.86)", group = "PBO", time = "VIS2") |>
      brm_prior_label(code = "student_t(4,  8.78, 8.18)", group = "PBO", time = "VIS3") |>
      brm_prior_label(code = "student_t(4,  3.36, 8.10)", group = "PBO", time = "VIS4") |>
      brm_prior_label(code = "student_t(4, -2.96, 4.78)", group = "TRT", time = "VIS1") |>
      brm_prior_label(code = "student_t(4,  3.13, 7.64)", group = "TRT", time = "VIS2") |>
      brm_prior_label(code = "student_t(4,  7.65, 8.24)", group = "TRT", time = "VIS3") |>
      brm_prior_label(code = "student_t(4,  4.64, 8.21)", group = "TRT", time = "VIS4")
    label
    #> # A tibble: 8 × 3
    #>   code                      group time
    #>   <chr>                     <chr> <chr>
    #> 1 student_t(4, -7.57, 4.96) PBO   VIS1
    #> 2 student_t(4,  3.14, 7.86) PBO   VIS2
    #> 3 student_t(4,  8.78, 8.18) PBO   VIS3
    #> 4 student_t(4,  3.36, 8.10) PBO   VIS4
    #> 5 student_t(4, -2.96, 4.78) TRT   VIS1
    #> 6 student_t(4,  3.13, 7.64) TRT   VIS2
    #> 7 student_t(4,  7.65, 8.24) TRT   VIS3
    #> 8 student_t(4,  4.64, 8.21) TRT   VIS4

As an alternative to `brm_prior_label()`, you can start with a template and manually fill in the Stan code.

    template <- brm_prior_template(archetype)
    template
    #> # A tibble: 8 × 3
    #>   code  group time
    #>   <chr> <chr> <chr>
    #> 1 <NA>  PBO   VIS1
    #> 2 <NA>  PBO   VIS2
    #> 3 <NA>  PBO   VIS3
    #> 4 <NA>  PBO   VIS4
    #> 5 <NA>  TRT   VIS1
    #> 6 <NA>  TRT   VIS2
    #> 7 <NA>  TRT   VIS3
    #> 8 <NA>  TRT   VIS4

    label <- template |>
      mutate(
        code = c(
          "student_t(4, -7.57, 4.96)",
          "student_t(4,  3.14, 7.86)",
          "student_t(4,  8.78, 8.18)",
          "student_t(4,  3.36, 8.10)",
          "student_t(4, -2.96, 4.78)",
          "student_t(4,  3.13, 7.64)",
          "student_t(4,  7.65, 8.24)",
          "student_t(4,  4.64, 8.21)"
        )
      )
    label
    #> # A tibble: 8 × 3
    #>   code                      group time
    #>   <chr>                     <chr> <chr>
    #> 1 student_t(4, -7.57, 4.96) PBO   VIS1
    #> 2 student_t(4,  3.14, 7.86) PBO   VIS2
    #> 3 student_t(4,  8.78, 8.18) PBO   VIS3
    #> 4 student_t(4,  3.36, 8.10) PBO   VIS4
    #> 5 student_t(4, -2.96, 4.78) TRT   VIS1
    #> 6 student_t(4,  3.13, 7.64) TRT   VIS2
    #> 7 student_t(4,  7.65, 8.24) TRT   VIS3
    #> 8 student_t(4,  4.64, 8.21) TRT   VIS4

After you have a labeling scheme, `brm_prior_archetype()` can create a `brms` prior for the important fixed effects.4

    prior <- brm_prior_archetype(label = label, archetype = archetype)
    prior
    #>                      prior class       coef group resp dpar nlpar   lb   ub source
    #>  student_t(4, -7.57, 4.96)     b x_PBO_VIS1                       <NA> <NA>   user
    #>  student_t(4,  3.14, 7.86)     b x_PBO_VIS2                       <NA> <NA>   user
    #>  student_t(4,  8.78, 8.18)     b x_PBO_VIS3                       <NA> <NA>   user
    #>  student_t(4,  3.36, 8.10)     b x_PBO_VIS4                       <NA> <NA>   user
    #>  student_t(4, -2.96, 4.78)     b x_TRT_VIS1                       <NA> <NA>   user
    #>  student_t(4,  3.13, 7.64)     b x_TRT_VIS2                       <NA> <NA>   user
    #>  student_t(4,  7.65, 8.24)     b x_TRT_VIS3                       <NA> <NA>   user
    #>  student_t(4,  4.64, 8.21)     b x_TRT_VIS4                       <NA> <NA>   user

In less common situations, you may wish to assign priors to nuisance parameters. For example, our model accounts for interactions between baseline and discrete time, and it may be reasonable to assign priors to these slopes based on high-quality historical data. This requires a thorough understanding of the fixed effect structure of the model, but it can be done directly through `brms`. First, check the formula for the included nuisance parameters. `brm_formula()` automatically understands archetypes.

    brm_formula(archetype)
    #> FEV1 ~ 0 + x_PBO_VIS1 + x_PBO_VIS2 + x_PBO_VIS3 + x_PBO_VIS4 + x_TRT_VIS1 + x_TRT_VIS2 + x_TRT_VIS3 + x_TRT_VIS4 + nuisance_WEIGHT + nuisance_SEX_Male + unstr(time = AVISIT, gr = USUBJID)
    #> sigma ~ 0 + AVISIT

The `"nuisance_*"` terms are the nuisance variables, and the ones involving baseline are `nuisance_FEV1_BL.AVISITVIS1`, `nuisance_FEV1_BL.AVISITVIS2`, `nuisance_FEV1_BL.AVISITVIS3`, and `nuisance_FEV1_BL.AVISITVIS4`. Because there is no overall slope for baseline, we can interpret each term as the linear rate of change in the outcome variable per unit increase in baseline for a given discrete time point. Suppose we use this interpretation to construct informative priors `student_t(4, -0.83, 1)`, `student_t(4, -0.78, 1)`, `student_t(4, -0.86, 1)`, and `student_t(4, -0.82, 1)`, respectively. Use `brms::set_prior()` and `c()` to append these priors to our existing `prior` object:

The model still has many parameters where we did not set priors, and `brms` sets automatic defaults. You can see these defaults with `brms::get_prior()`.

https://paulbuerkner.com/brms/reference/set_prior.html documents many of the default priors set by `brms`. In particular, `"(flat)"` denotes an improper uniform prior over all the real numbers.

## Modeling and analysis

The downstream methods in `brms.mmrm` automatically understand how to work with informative prior archetypes. Notably, the formula uses custom interest and nuisance variables instead of the original variables in the data.

    formula <- brm_formula(archetype)
    formula
    #> FEV1 ~ 0 + x_PBO_VIS1 + x_PBO_VIS2 + x_PBO_VIS3 + x_PBO_VIS4 + x_TRT_VIS1 + x_TRT_VIS2 + x_TRT_VIS3 + x_TRT_VIS4 + nuisance_WEIGHT + nuisance_SEX_Male + unstr(time = AVISIT, gr = USUBJID)
    #> sigma ~ 0 + AVISIT

The model can accept the archetype, formula, and prior. Usage is the same as in non-archetype workflows.

    model <- brm_model(
      data = archetype,
      formula = formula,
      prior = prior,
      refresh = 0
    )
    #> Compiling Stan program...
    #> Start sampling
    brms::prior_summary(model)
    #>                      prior    class              coef group resp  dpar nlpar lb ub
    #>                     (flat)        b
    #>                     (flat)        b nuisance_SEX_Male
    #>                     (flat)        b   nuisance_WEIGHT
    #>  student_t(4, -7.57, 4.96)        b        x_PBO_VIS1
    #>  student_t(4,  3.14, 7.86)        b        x_PBO_VIS2
    #>  student_t(4,  8.78, 8.18)        b        x_PBO_VIS3
    #>  student_t(4,  3.36, 8.10)        b        x_PBO_VIS4
    #>  student_t(4, -2.96, 4.78)        b        x_TRT_VIS1
    #>  student_t(4,  3.13, 7.64)        b        x_TRT_VIS2
    #>  student_t(4,  7.65, 8.24)        b        x_TRT_VIS3
    #>  student_t(4,  4.64, 8.21)        b        x_TRT_VIS4
    #>                     (flat)        b                              sigma
    #>                     (flat)        b        AVISITVIS1            sigma
    #>                     (flat)        b        AVISITVIS2            sigma
    #>                     (flat)        b        AVISITVIS3            sigma
    #>                     (flat)        b        AVISITVIS4            sigma
    #>       lkj_corr_cholesky(1) Lcortime
    #>        source
    #>       default
    #>  (vectorized)
    #>  (vectorized)
    #>          user
    #>          user
    #>          user
    #>          user
    #>          user
    #>          user
    #>          user
    #>          user
    #>       default
    #>  (vectorized)
    #>  (vectorized)
    #>  (vectorized)
    #>  (vectorized)
    #>       default

Marginal mean estimation, post-processing, and visualization automatically understand the archetype without any user intervention.

    draws <- brm_marginal_draws(
      data = archetype,
      formula = formula,
      model = model
    )
    summaries_model <- brm_marginal_summaries(draws)
    summaries_data <- brm_marginal_data(archetype)
    brm_plot_compare(model = summaries_model, data = summaries_data)

plot of chunk archetype_compare_data

## All archetypes

`brms.mmrm` supports a variety of informative prior archetypes with different kinds of fixed effects. For example, `brms.mmrm` supports simple cell mean and treatment effect parameterizations.

    summary(brm_archetype_cells(data, intercept = FALSE))
    #> # This is the "cells" informative prior archetype in brms.mmrm.
    #> # The following equations show the relationships between the
    #> # marginal means (left-hand side) and fixed effect parameters
    #> # (right-hand side).
    #> #
    #> #   PBO:VIS1 = x_PBO_VIS1
    #> #   PBO:VIS2 = x_PBO_VIS2
    #> #   PBO:VIS3 = x_PBO_VIS3
    #> #   PBO:VIS4 = x_PBO_VIS4
    #> #   TRT:VIS1 = x_TRT_VIS1
    #> #   TRT:VIS2 = x_TRT_VIS2
    #> #   TRT:VIS3 = x_TRT_VIS3
    #> #   TRT:VIS4 = x_TRT_VIS4

    summary(brm_archetype_effects(data, intercept = FALSE))
    #> # This is the "effects" informative prior archetype in brms.mmrm.
    #> # The following equations show the relationships between the
    #> # marginal means (left-hand side) and fixed effect parameters
    #> # (right-hand side).
    #> #
    #> #   PBO:VIS1 = x_PBO_VIS1
    #> #   PBO:VIS2 = x_PBO_VIS2
    #> #   PBO:VIS3 = x_PBO_VIS3
    #> #   PBO:VIS4 = x_PBO_VIS4
    #> #   TRT:VIS1 = x_PBO_VIS1 + x_TRT_VIS1
    #> #   TRT:VIS2 = x_PBO_VIS2 + x_TRT_VIS2
    #> #   TRT:VIS3 = x_PBO_VIS3 + x_TRT_VIS3
    #> #   TRT:VIS4 = x_PBO_VIS4 + x_TRT_VIS4

There are archetypes to parameterize the average across all time points in the data. Below, `x_group_1_time_2` is the average across time points for group 1 because it is the algebraic result of simplifying `(group_1:time_2 + group_1:time_3 + group_1:time_3) / 3`.

    summary(brm_archetype_average_cells(data, intercept = FALSE))
    #> # This is the "average cells" informative prior archetype in brms.mmrm.
    #> # The following equations show the relationships between the
    #> # marginal means (left-hand side) and fixed effect parameters
    #> # (right-hand side).
    #> #
    #> #   PBO:VIS1 = 4*x_PBO_VIS1 - x_PBO_VIS2 - x_PBO_VIS3 - x_PBO_VIS4
    #> #   PBO:VIS2 = x_PBO_VIS2
    #> #   PBO:VIS3 = x_PBO_VIS3
    #> #   PBO:VIS4 = x_PBO_VIS4
    #> #   TRT:VIS1 = 4*x_TRT_VIS1 - x_TRT_VIS2 - x_TRT_VIS3 - x_TRT_VIS4
    #> #   TRT:VIS2 = x_TRT_VIS2
    #> #   TRT:VIS3 = x_TRT_VIS3
    #> #   TRT:VIS4 = x_TRT_VIS4

There is also a treatment effect version where `x_group_2_time_2` becomes the time-averaged treatment effect of group 2 relative to group 1.

    summary(brm_archetype_average_effects(data, intercept = FALSE))
    #> # This is the "average effects" informative prior archetype in brms.mmrm.
    #> # The following equations show the relationships between the
    #> # marginal means (left-hand side) and fixed effect parameters
    #> # (right-hand side).
    #> #
    #> #   PBO:VIS1 = 4*x_PBO_VIS1 - x_PBO_VIS2 - x_PBO_VIS3 - x_PBO_VIS4
    #> #   PBO:VIS2 = x_PBO_VIS2
    #> #   PBO:VIS3 = x_PBO_VIS3
    #> #   PBO:VIS4 = x_PBO_VIS4
    #> #   TRT:VIS1 = 4*x_PBO_VIS1 - x_PBO_VIS2 - x_PBO_VIS3 - x_PBO_VIS4 + 4*x_TRT_VIS1 - x_TRT_VIS2 - x_TRT_VIS3 - x_TRT_VIS4
    #> #   TRT:VIS2 = x_PBO_VIS2 + x_TRT_VIS2
    #> #   TRT:VIS3 = x_PBO_VIS3 + x_TRT_VIS3
    #> #   TRT:VIS4 = x_PBO_VIS4 + x_TRT_VIS4

The example in this vignette uses the “successive cells” archetype, where fixed effects represent successive differences between adjacent time points.

    summary(brm_archetype_successive_cells(data, intercept = FALSE))
    #> # This is the "successive cells" informative prior archetype in brms.mmrm.
    #> # The following equations show the relationships between the
    #> # marginal means (left-hand side) and fixed effect parameters
    #> # (right-hand side).
    #> #
    #> #   PBO:VIS1 = x_PBO_VIS1
    #> #   PBO:VIS2 = x_PBO_VIS1 + x_PBO_VIS2
    #> #   PBO:VIS3 = x_PBO_VIS1 + x_PBO_VIS2 + x_PBO_VIS3
    #> #   PBO:VIS4 = x_PBO_VIS1 + x_PBO_VIS2 + x_PBO_VIS3 + x_PBO_VIS4
    #> #   TRT:VIS1 = x_TRT_VIS1
    #> #   TRT:VIS2 = x_TRT_VIS1 + x_TRT_VIS2
    #> #   TRT:VIS3 = x_TRT_VIS1 + x_TRT_VIS2 + x_TRT_VIS3
    #> #   TRT:VIS4 = x_TRT_VIS1 + x_TRT_VIS2 + x_TRT_VIS3 + x_TRT_VIS4

There is also a treatment effect version of the successive differences archetype:

    summary(brm_archetype_successive_effects(data, intercept = FALSE))
    #> # This is the "successive effects" informative prior archetype in brms.mmrm.
    #> # The following equations show the relationships between the
    #> # marginal means (left-hand side) and fixed effect parameters
    #> # (right-hand side).
    #> #
    #> #   PBO:VIS1 = x_PBO_VIS1
    #> #   PBO:VIS2 = x_PBO_VIS1 + x_PBO_VIS2
    #> #   PBO:VIS3 = x_PBO_VIS1 + x_PBO_VIS2 + x_PBO_VIS3
    #> #   PBO:VIS4 = x_PBO_VIS1 + x_PBO_VIS2 + x_PBO_VIS3 + x_PBO_VIS4
    #> #   TRT:VIS1 = x_PBO_VIS1 + x_TRT_VIS1
    #> #   TRT:VIS2 = x_PBO_VIS1 + x_PBO_VIS2 + x_TRT_VIS1 + x_TRT_VIS2
    #> #   TRT:VIS3 = x_PBO_VIS1 + x_PBO_VIS2 + x_PBO_VIS3 + x_TRT_VIS1 + x_TRT_VIS2 + x_TRT_VIS3
    #> #   TRT:VIS4 = x_PBO_VIS1 + x_PBO_VIS2 + x_PBO_VIS3 + x_PBO_VIS4 + x_TRT_VIS1 + x_TRT_VIS2 + x_TRT_VIS3 + x_TRT_VIS4

## Variations on archetypes

Archetypes can be customized. As an example, consider the simple cell means archetype.

    summary(brm_archetype_cells(data))
    #> # This is the "cells" informative prior archetype in brms.mmrm.
    #> # The following equations show the relationships between the
    #> # marginal means (left-hand side) and fixed effect parameters
    #> # (right-hand side).
    #> #
    #> #   PBO:VIS1 = x_PBO_VIS1
    #> #   PBO:VIS2 = x_PBO_VIS2
    #> #   PBO:VIS3 = x_PBO_VIS3
    #> #   PBO:VIS4 = x_PBO_VIS4
    #> #   TRT:VIS1 = x_TRT_VIS1
    #> #   TRT:VIS2 = x_TRT_VIS2
    #> #   TRT:VIS3 = x_TRT_VIS3
    #> #   TRT:VIS4 = x_TRT_VIS4

To include an intercept term which all the marginal means share, set `intercept = TRUE`.

    summary(brm_archetype_cells(data, intercept = TRUE))
    #> # This is the "cells" informative prior archetype in brms.mmrm.
    #> # The following equations show the relationships between the
    #> # marginal means (left-hand side) and fixed effect parameters
    #> # (right-hand side).
    #> #
    #> #   PBO:VIS1 = x_PBO_VIS1
    #> #   PBO:VIS2 = x_PBO_VIS1 + x_PBO_VIS2
    #> #   PBO:VIS3 = x_PBO_VIS1 + x_PBO_VIS3
    #> #   PBO:VIS4 = x_PBO_VIS1 + x_PBO_VIS4
    #> #   TRT:VIS1 = x_PBO_VIS1 + x_TRT_VIS1
    #> #   TRT:VIS2 = x_PBO_VIS1 + x_TRT_VIS2
    #> #   TRT:VIS3 = x_PBO_VIS1 + x_TRT_VIS3
    #> #   TRT:VIS4 = x_PBO_VIS1 + x_TRT_VIS4

To set up constrained longitudinal data analysis (cLDA), set `clda = TRUE`. This constraint pools all treatment groups at baseline, and it can help model clinical trials where a baseline measurement is observed before randomization. Some archetypes cannot support cLDA (e.g. `brm_archetype_average_cells()` and `brm_archetype_average_effects()`).

    summary(brm_archetype_cells(data, clda = TRUE))
    #> # This is the "cells" informative prior archetype in brms.mmrm.
    #> # The following equations show the relationships between the
    #> # marginal means (left-hand side) and fixed effect parameters
    #> # (right-hand side).
    #> #
    #> #   PBO:VIS1 = x_PBO_VIS1
    #> #   PBO:VIS2 = x_PBO_VIS2
    #> #   PBO:VIS3 = x_PBO_VIS3
    #> #   PBO:VIS4 = x_PBO_VIS4
    #> #   TRT:VIS1 = x_PBO_VIS1
    #> #   TRT:VIS2 = x_TRT_VIS2
    #> #   TRT:VIS3 = x_TRT_VIS3
    #> #   TRT:VIS4 = x_TRT_VIS4

1. `brm_recenter_nuisance()` can retroactively recenter a nuisance column to a fixed value other than its mean.↩︎

2. `brm_formula()` assigns `center = FALSE` in `brmsformula()` for all informative prior archetypes.↩︎

3. `summary()` also invisibly returns a simple character vector with the equations below.↩︎

4. `brms` priors are documented in https://paulbuerkner.com/brms/reference/set_prior.html.↩︎