# Methods

The brms.mmrm R package implements a mixed model of repeated measures (MMRM), a popular and flexible model to analyze continuous longitudinal outcomes (Mallinckrodt et al. (2008), Mallinckrodt and Lipkovich (2017)). brms.mmrm focuses on marginal MMRMs for randomized controlled parallel studies with discrete time points, where each subject shares the same set of time points. Whereas the mmrm package is frequentist, brms.mmrm fits models in Bayesian fashion using brms (Bürkner 2017).

# 1 Model

The MMRM in brms.mmrm is mathematically expressed as follows. Subsequent sections define notation, data, parameters, and priors.

\begin{aligned} &y_i \stackrel{\text{ind}}{\sim} \text{Multivariate-Normal} \left (\text{mean} = X_{i} \beta, \ \text{covariance} = \text{diag}(\sigma) \cdot \Lambda \cdot \text{diag}(\sigma) \right ) \\ &\qquad \beta_p \stackrel{\text{ind}}{\sim} q_p() \\ &\qquad \Lambda \sim r() \\ &\qquad \tau = \log(\sigma) \\ &\qquad \qquad \tau_t \stackrel{\text{ind}}{\sim} s_t() \end{aligned}

# 2 Notation

• $$i$$: positive integer index of a subject or patient in the study.
• $$t$$: positive integer index of a discrete time point in the study.
• $$p$$: positive integer index of a model coefficient parameter $$\beta_p$$.
• $$\theta_j \stackrel{\text{ind}}{\sim} f()$$: assuming $$j$$ is a positive integer index that ranges from 1 to positive integer $$J$$, this notation declares that parameters $$\theta_1, \ldots, \theta_J$$ are independent and each follow the prior distribution with a common probability density function $$f()$$.
• $$\theta_j \stackrel{\text{ind}}{\sim} f_j()$$: same as above, except each $$\theta_j$$ parameter follows its own separate prior distribution with probability density function $$p_j()$$. $$p_i()$$ and $$f_j()$$ may differ if $$i \ne j$$.
• $$\text{diag}(\theta)$$: diagonal matrix with the scalar elements of vector $$\theta$$ on the diagonal and off-diagonal elements equal to 0. The number of rows and number of columns in this matrix are both equal to the number of elements in vector $$\theta$$.

# 3 Data

• $$y_i$$: numeric vector of outcome observations for subject $$i$$. The number of elements in the vector equals the number of discrete time points in the study, and for the purposes of this model specification, they are sorted chronologically with the first element of $$y_i$$ observed first in the study. One or more elements of each $$y_i$$ may be missing due to dropout, discontinuation, etc. The likelihood of the model assumes $$y_i$$ is independent of $$y_j$$ for $$i \ne j$$.
• $$X_i$$: a matrix containing the rows of the model matrix that correspond to subject $$i$$. The rows of $$X_i$$ correspond to the elements of the vector $$y_i$$ (equivalently, the discrete time points of the study), and the columns of $$X_i$$ correspond to the model coefficient parameters $$\beta_p$$. The composition of $$X_i$$ is determined by the covariates in the input dataset and the choice of model formula supplied by the user (via brm_formula()).

# 4 Parameters

• $$\beta$$: vector of model coefficients. $$\beta_p$$ is scalar element $$p$$ of $$\beta$$.
• $$\Lambda$$: matrix of pairwise correlations among each pair of time points within subjects. Outcomes measured from different subjects are assumed to be independent, and outcomes observed at different time points for the same subject are assumed to be correlated according to this matrix. $$\Lambda$$ is positive-definite and symmetric, and the number of rows and columns is equal to the number of discrete time points of the study.
• $$\sigma$$: vector of time-point-specific standard deviations of the residuals on the linear scale. $$\sigma_t$$ is scalar element $$t$$ of $$\sigma$$.
• $$\tau$$: same as $$\sigma$$, but on the natural logarithmic scale. Each scalar element $$\tau_t$$ of $$\tau$$ is defined as the natural logarithm of $$\sigma_t$$.

# 5 Priors

The priors on the parameters depend on the prior argument of brm_model() and related functions. If priors are not specified by the user, then the brms package sets defaults. You can view the default priors using the get_prior() function in brms. See the brms for information on how brms sets default priors.

• $$q_p()$$: univariate prior on the model coefficient $$\beta_p$$.
• $$r()$$: matrix prior on the within-subject residual correlation matrix parameter $$\Lambda$$.
• $$s_t()$$: univariate prior on the natural-log-standard-deviation parameter $$\tau_t$$ of discrete time point $$t$$.

# 6 Sampling

brms.mmrm, through brms, fits the model to the data using the Markov chain Monte Carlo (MCMC) capabilities of Stan (Stan Development Team 2023). Please read https://mc-stan.org/users/documentation/ for more details on the methodology of Stan. The result of MCMC is a collection of draws from the full joint posterior distribution of the parameters given the data. Individual draws of scalar parameters such as $$\beta_3$$ are considered draws from the marginal posterior distribution of e.g. $$\beta_3$$ given the data.

# 7 Inference

Inference in brms.mmrm, uses the estimated marginal posterior distribution of the mean response at each combination of study arm and time point. The emmeans package (Lenth 2016) derives these marginal posteriors while averaging over other covariates as nuisance parameters. During this averaging process, the levels of categorical nuisance parameters are weighted proportionally to their frequencies in the dataset (with wt.nuis = "proportional" in emmeans::ref_grid()).

The brm_marginal_draws() function, described in the usage vignette, derives posterior draws of the marginals using posterior draws of the model coefficients $$\beta_p$$. Then, downstream functions like brm_marginal_probabilities() compute numerical summaries of these marginal draws.

# 8 Subgroup analysis

The model above supports subgroup analysis through the addition of a categorical variable in the data to denote subgroup levels. To analyze the subgroup, new fixed effects parameters $$\beta_p$$ and columns of the model matrices $$X_i$$ are added to the model to describe the additive effect of the subgroup and plausible two-way and three-way interactions with treatment group, discrete time, and baseline (if applicable). Marginal means may include subgroup-specific terms, and model comparison via the widely applicable information criterion (WAIC) and expected log predictive density (ELPD) is implemented via R packages loo and brms (Gabry et al. (2019), Gelman and Hill (2007), Vehtari et al. (2017), Vehtari et al. (2019)).

# References

Bürkner, P.-C. (2017), brms: An R package for Bayesian multilevel models using Stan,” Journal of Statistical Software, 80, 1–28. https://doi.org/10.18637/jss.v080.i01.
Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., and Gelman, A. (2019), “Visualization in bayesian workflow,” Journal of the Royal Statistical Society: Series A (Statistics in Society), 182, 389–402. https://doi.org/10.1111/rssa.12378.
Gelman, A., and Hill, J. (2007), Data analysis using regression and multilevel/hierarchical models, Cambridge, UK: Cambridge University Press.
Lenth, R. V. (2016), “Least-squares means: The r package lsmeans,” Journal of Statistical Software, 69, 1–33. https://doi.org/10.18637/jss.v069.i01.
Mallinckrodt, C. H., Lane, P. W., Schnell, D., and others (2008), “Recommendations for the primary analysis of continuous endpoints in longitudinal clinical trials,” Therapeutic Innovation and Regulatory Science, 42, 303–319. https://doi.org/10.1177/009286150804200402.
Mallinckrodt, C. H., and Lipkovich, I. (2017), Analyzing longitudinal clinical trial data: A practical guide, CRC Press, Taylor; Francis Group.
Stan Development Team (2023), Stan modeling language users guide and reference manual.
Vehtari, A., Gelman, A., and Gabry, J. (2017), “Practical bayesian model evaluation using leave-one-out cross-validation and WAIC,” Statistics and Computing, 27, 1413–1432. https://doi.org/10.1007/s11222-016-9696-4.
Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2019), “Pareto smoothed importance sampling,” arXiv preprint arXiv:1507.02646.