Introduction to metapack

Daeyoung Lim

2021-10-20

Table of Contents

Meta-analytic data

Meta-analytic data is largely characterized in three ways: univariate or multivariate, meta-analysis or network meta-analysis, and aggregate data or individual participant data. All other modeling choices fall into prior specification. For aggregate-data models, the standard data format is shown in the following table:

Outcome Std.Dev XCovariate WCovariate Trial (k) Treat (t) Npt
\(\bar{y}_{\cdot 13}\) \(S_{13}\) \(\boldsymbol{x}_{13}^\top\) \(\boldsymbol{w}_{13}^\top\) 1 3 1000
\(\bar{y}_{\cdot 10}\) \(S_{10}\) \(\boldsymbol{x}_{10}^\top\) \(\boldsymbol{w}_{20}^\top\) 1 0 1000
\(\bar{y}_{\cdot 20}\) \(S_{20}\) \(\boldsymbol{x}_{20}^\top\) \(\boldsymbol{w}_{20}^\top\) 2 0 1000
\(\vdots\) \(\vdots\) \(\vdots\) \(\vdots\) \(\vdots\) \(\vdots\) \(\vdots\)

Outcome is the response(s), Std.Dev is the standard deviation(s) of the response(s), XCovariate and WCovariate are the design matrices, and Npt is the trial sample sizes. The pair of trial and treatment indicators is unique to a specific row. Individual participant data obviously doesn’t have Std.Dev and Npt.

bmeta_analyze

bmeta_analyze is the main function in metapack that supersedes and subsumes all previous worker functions. bmeta_analyze internally parses the formula and identifies the model, which ultimately passes the arguments to a suitable worker function. The vanilla formula class lacks parts and should strictly have one left-hand side (LHS) and one right-hand side (RHS). Formula (Zeileis and Croissant 2010) extends this formula interface to allow multiple parts and multiple responses, lending itself well into (network) meta-analysis.

Aside from the model-specific arguments, control has two items universally applicable: scale_x and verbose. When TRUE, the former standardizes the fixed-effects design matrix, and the latter allows printing the progress bar of the Markov chain Monte Carlo algorithm. Both default to FALSE if not provided. Note that if scale_x is TRUE, the subsequent methods like print, summary, or coef will revert the scaling to their original units. If this behavior is not desired, scale the variables explicitly in the formula.

Using Formula

Each part in LHS or RHS is an R expression where variables (or functions of variables) are chained with a plus sign (+), e.g., x1 + x2. The tilde (~) separates all LHS’s from all RHS’s, each side further broken down into parts by vertical bars (|). The meaning of each part is syntactically determined by its location within a formula, like the English language. Therefore, the order must be observed as presribed for bmeta_analyze to correctly identify and configure the model.

Capitalizing on the unique data structure of meta-analysis data, the general form of the formula is as follows:

y1 + y2 | sd1 + sd2 ~ x1 + x2 + x3 + ns(n) | z1 + z2 + z3 | treat + trial (+ groups)

Function arguments

Aside from formula and data, there are four remaining arguments that are required to be lists: prior, mcmc, control, and init.

Use case 1 - (multivariate) meta-analysis with bmeta_analyze

Univariate meta-analysis can be specified with one response endpoint and two treatments, except that the covariance modeling must be “NoRecovery”.

Multivariate meta-analysis is characterized by multiple response endpoints and two treatments. Additionally, multivariate meta-analysis requires a model for the covariance matrix to be specified as model in the prior argument—which defaults to model=NoRecovery if left unspecified. The available options are listed below. The objects parenthesized at the end of every bullet point are the hyperparameters associated with each model.

The boilerplate code for multivariate meta-analysis is as follows:

f <- "y1 + y2 | sd1 + sd2 ~ x1 + x2 + x3 + ns(n) | z1 + z2 + z3 | treat + trial + groups"
out <- bmeta_analyze(formula(f), data = df,
        mcmc = list(ndiscard = 20000, nskip = 5, nkeep = 10000),
        prior = list(model = "NoRecovery"))

Except model="NoRecovery", all else uses R_stepsize in the control argument. The fourth option—model=EquiCorrelation—allows sampling \(\boldsymbol{\rho}\). However, researchers oftentimes have a rough—or concrete—idea about the correlation values, and therefore fixing \(\boldsymbol{\rho}\) makes sense. That edge case can be accomplished through setting sample_Rho=FALSE in control and Rho=<some_matrix> in init. If sample_Rho=FALSE but no initial value for Rho is given, it will default to 0.5 (\(\boldsymbol{\rho}=0.5\boldsymbol{I}+0.5\boldsymbol{1}\boldsymbol{1}^\top\)). In full form,

Rho_init <- diag(3) # assume 3 by 3
Rho_init[upper.tri(Rho_init)] <- 
    Rho_init[lower.tri(Rho_init)] <- 0.2
out <- bmeta_analyze(formula(f), data = df,
        prior = list(model = "EquiCorr"), # abbreviation allowed
        control = list(sample_Rho = FALSE),
        init = list(Rho = Rho_init))

Use case 2 - univariate network meta-analysis with bmeta_analyze

Univariate network meta-analysis is characterized by a single response endpoint and more than two treatments. Similar to model in the multivariate meta-analysis case, the degrees of freedom (df) for the random effects’ multivariate \(t\)-distribution are the parameter of interest in univariate network meta-analysis. Objects parenthesized at the end are the hyperparameters associated with the model.

The boilerplate code for univariate network meta-analysis is as follows:

f <- "y | sd ~ x1 + x2 + x3 + ns(n) | z1 + z2 + z3 | treat + trial"
out <- bmeta_analyze(formula(f), data = df,
        mcmc = list(discard = 20000, nskip = 5, nkeep = 10000),
        prior = list(df = 3)) # heavy-tailed random effects

If control=list(sample_df=TRUE), the df value in prior is ignored. Identically to the multivariate meta-analysis case, sample_Rho can be set to FALSE to suppress the sampling of Rho, and Rho will be initialized using the corresponding value in init, which defaults to 0.5 (\(\boldsymbol{\rho}=0.5\boldsymbol{I}+0.5\boldsymbol{1}\boldsymbol{1}^\top\)). The correlation matrix Rho for univariate network meta-analysis belongs to the random effects, not the responses.

Rho_init <- diag(nT) # nT = the number of treatments
Rho_init[upper.tri(Rho_init)] <- 
    Rho_init[lower.tri(Rho_init)] <- 0.2
out <- bmeta_analyze(formula(f), data = df,
        control = list(sample_df = TRUE, sample_Rho = FALSE),
        init = list(Rho = Rho_init))

Sometimes, there is no covariate information available for modeling the variances. The second RHS can be a vector of ones, which reduces the model to have \(\mathrm{Var}(\overline{y}_{tk})=\sigma_{tk}^2/n_{tk} + \exp(2\phi)\) for every \((t,k)\). Users can simply do without the second RHS in this case—the second RHS technically exists but is the treatment and trial configuration. An example formula is as follows:

f <- "y | sd ~ x1 + x2 + x3 + ns(n) | treat + trial"

Appendix

Multivariate meta-analysis model

When there are multiple endpoints, the correlations thereof are oftentimes unreported. In a meta-regression setting, the correlations are something we want to know. In the case of subject-level meta-analyses (where individual participant/patient data are available), the correlations may only be one function call away. However, for study-level meta-analyses, the correlations must be estimated, which is not an easy task. bmeta_analyze provides five different models to estimate and recover the missing correlations.

Since the aforementioned setting regards the correlations as missing, the reported data are \((\overline{\boldsymbol{y}}_{\cdot tk}, \boldsymbol{s}_{tk}) \in \mathbf{R}^J \times \mathbf{R}^J\) for the \(t\)th treatment arm and \(k\)th trial. Every trial out of \(K\) trials includes \(T\) treatment arms. The sample size of the \(t\)th treatment arm and \(k\)th trial is denoted by \(n_{tk}\). If we write \(\boldsymbol{x}_{tkj}\in \mathbf{R}^{p_j}\) to be the treatment-within-trial level regressor corresponding to the \(j\)th response, reflecting the fixed effects of the \(t\)th treatment arm, and \(\boldsymbol{w}_{tkj}\in \mathbf{R}^{q_j}\) to be the same for the random effects, the model becomes

\[\begin{equation}\label{eq:reduced-model} \overline{\boldsymbol{y}}_{\cdot tk} = \boldsymbol{X}_{tk}\boldsymbol{\beta} + \boldsymbol{W}_{tk}\boldsymbol{\gamma}_k + \overline{\boldsymbol{\epsilon}}_{\cdot tk}, \end{equation}\]

and \((n_{tk}-1)S_{tk} \sim \mathcal{W}_{n_{tk}-1}(\Sigma_{tk})\) where \(\boldsymbol{X}_{tk} = \mathrm{blockdiag}(\boldsymbol{x}_{tk1}^\top,\boldsymbol{x}_{tk2}^\top,\ldots,\boldsymbol{x}_{tkJ}^\top)\), \(\boldsymbol{\beta} = (\boldsymbol{\beta}_1^\top, \cdots, \boldsymbol{\beta}_J^\top)^\top\), \(\boldsymbol{W}_{tk}=\mathrm{blockdiag}(\boldsymbol{w}_{tk1}^\top, \ldots,\boldsymbol{w}_{tkJ}^\top)\), \(\boldsymbol{\gamma}_k = (\boldsymbol{\gamma}_{k1}^\top,\boldsymbol{\gamma}_{k2}^\top, \ldots,\boldsymbol{\gamma}_{kJ}^\top)^\top\), \(\overline{\boldsymbol{\epsilon}}_{\cdot tk} \sim \mathcal{N}(\boldsymbol{0}, \Sigma_{tk}/n_{tk})\), \(S_{tk}\) is the full-rank covariance matrix whose diagonal entries are the observed \(\boldsymbol{s}_{tk}\), and \(\mathcal{W}_\nu(\Sigma)\) is the Wishart distribution with \(\nu\) degrees of freedom and a \(J\times J\) scale matrix \(\Sigma\) whose density function is

\[ f(X\mid \nu,\Sigma) = \dfrac{1}{2^{J\nu}|\Sigma|^{\nu/2}\Gamma_J(\nu/2)}|X|^{(\nu-J-1)/2}\exp\left(-\dfrac{1}{2}\mathrm{tr}(\Sigma^{-1}X) \right)I(X\in \mathcal{S}_{++}^J), \]

where \(\Gamma_p\) is the multivariate gamma function defined by

\[ \Gamma_p(z) = \pi^{p(p-1)/4}\prod_{j=1}^p \Gamma[z+(1-j)/2], \]

and \(\mathcal{S}_{++}^J\) is the space of \(J\times J\) symmetric positive definite matrices. The statistical independence of \(\overline{\boldsymbol{\epsilon}}_{\cdot tk}\) and \(S_{tk}\) follows naturally from the Basu’s theorem.

The patients can sometimes be grouped by a factor that will generate disparate random effects. Although an arbitrary number of groups can exist in theory, metapack restricts the number of groups to two for practicality. Denoting the binary group indicates by \(u_{tk}\) yields

\[ \overline{y}_{\cdot tkj} = \boldsymbol{x}_{tkj}^\top\boldsymbol{\beta} + (1-u_{tk})\boldsymbol{w}_{tkj}^\top \boldsymbol{\gamma}_{kj}^0 + u_{tk}\boldsymbol{w}_{tkj}^\top \boldsymbol{\gamma}_{kj}^1 + \overline{\epsilon}_{\cdot tkj}. \]

The random effects are modeled as \(\boldsymbol{\gamma}_{kj}^l \overset{\text{ind}}{\sim}\mathcal{N}(\boldsymbol{\gamma}_j^{l*},\Omega_j^l)\) and \((\Omega_j^l)^{-1} \sim \mathcal{W}_{d_{0j}}(\Omega_{0j})\). Stacking the vectors, \(\boldsymbol{\gamma}_k^l = ((\boldsymbol{\gamma}_{k1}^l)^\top, \ldots, (\boldsymbol{\gamma}_{kJ}^l)^\top)^\top \sim \mathcal{N}(\boldsymbol{\gamma}^{l*},\Omega^l)\) where \(\boldsymbol{\gamma}^{l*} = ((\boldsymbol{\gamma}_{1}^{l*})^\top,\ldots,(\boldsymbol{\gamma}_{J}^{l*})^\top)^\top\), \(\Omega_j = \mathrm{blockdiag}(\Omega_j^0,\Omega_j^l)\), and \(\Omega = \mathrm{blockdiag}(\Omega_1,\ldots,\Omega_J)\) for \(l \in \{0,1\}\). Adopting the non-centered reparametrization (Bernardo et al. 2003), define \(\boldsymbol{\gamma}_{k,o}^l = \boldsymbol{\gamma}_k^l - \boldsymbol{\gamma}^{l*}\). Denoting \(\boldsymbol{W}_{tk}^* = [(1-u_{tk})\boldsymbol{W}_{tk}, u_{tk}\boldsymbol{W}_{tk}]\), \(\boldsymbol{X}_{tk}^* = [\boldsymbol{X}_{tk},\boldsymbol{W}_{tk}^*]\), and \(\boldsymbol{\theta} = (\boldsymbol{\beta}^\top, {\boldsymbol{\gamma}^{0*}}^\top, {\boldsymbol{\gamma}^{1*}}^\top)^\top\), the model is written as follows:

\[ \overline{\boldsymbol{y}}_{\cdot tk} = \boldsymbol{X}_{tk}^*\boldsymbol{\theta}+\boldsymbol{W}_{tk}^*\boldsymbol{\gamma}_{k,o} + \overline{\boldsymbol{\epsilon}}_{\cdot tk}, \] where \(\boldsymbol{\gamma}_{k,o} = ((\boldsymbol{\gamma}_{k,o}^0)^\top, (\boldsymbol{\gamma}_{k,o}^1)^\top)^\top\). If there is no grouping in the patients, setting \(u_{tk}=0\) for all \((t,k)\) reduces the model back to \(\eqref{eq:reduced-model}\).

The conditional distribution of \((R_{tk} \mid V_{tk}, \Sigma_{tk})\) where \(R_{tk} = V_{tk}^{-\frac{1}{2}}S_{tk}V_{tk}^{-\frac{1}{2}}\) and \(V_{tk} = \mathrm{diag}(S_{tk11},\ldots,S_{tkJJ})\) becomes

\[ f(R_{tk}\mid V_{tk},\Sigma_{tk}) \propto |R_{tk}|^{(n_{tk}-J-2)/2}\exp\left\{-\dfrac{(n_{tk}-1)}{2}\mathrm{tr}\left(V_{tk}^{\frac{1}{2}}\Sigma_{tk}^{-1}V_{tk}^{\frac{1}{2}}R_{tk} \right) \right\}. \]

Univariate network meta-analysis model

Network meta-analysis is an extension of meta-analysis where more than two treatments are compared. Unlike the traditional meta-analyses that restrict the number of treatments to be equal across trials, network meta-analysis allows varying numbers of treatments. This achieves a unique benefit that two treatments that have not been compared head-to-head can be assessed as a pair.

Start by denoting the comprehensive list of treatments in all \(K\) trials by \(\mathcal{T}=\{1,\ldots,T\}\). It is rarely the case that all \(T\) treatments are included in the data but we drop the subscripts \(t_k\) and replace it with \(t\) for notational simplicity. Now, consider the model

\[\begin{equation}\label{eq:nmr-basic} \bar{y}_{\cdot tk} = \boldsymbol{x}_{tk}^\top\boldsymbol{\beta} + \tau_{tk}\gamma_{tk} + \bar{\epsilon}_{\cdot tk}, \quad \bar{\epsilon}_{\cdot tk} \sim \mathcal{N}(0,\sigma_{tk}^2/n_{tk}), \end{equation}\] where \(\bar{y}_{\cdot tk}\) is the univariate aggregate response of the \(k\)th trial for which treatment \(t\) was assigned, \(\boldsymbol{x}_{tk}\) is the aggregate covariate vector for the fixed effects, and \(\gamma_{tk}\) is the random effects term. The observed standard deviation, \(s_{tk}^2\) is modeled by

\[ \dfrac{(n_{tk}-1)s_{tk}^2}{\sigma_{tk}^2} \sim \chi_{n_{tk}-1}^2. \]

\(\tau_{tk}\) in Equation \(\eqref{eq:nmr-basic}\) encapsulates the variance of the random effect for the \(t\)th treatment in the \(k\)th trial, which is modeled as a deterministic function of a related covariate. That is,

\[ \log \tau_{tk} = \boldsymbol{z}_{tk}^\top\boldsymbol{\phi}, \]

where \(\boldsymbol{z}_{tk}\) is the \(q\)-dimensional aggregate covariate vector and \(\boldsymbol{\phi}\) is the corresponding coefficient vector.

For the \(k\)th trial, we define a selection/projection matrix \(E_k = (e_{t_{1k}},e_{t_{2k}},\ldots, e_{t_{T_k k}})\), where \(e_{t_{lk}} = (0,\ldots,1,\ldots,0)^\top\), \(l=1,\ldots,T_k\), with \(t_{lk}\)th element set to 1 and 0 otherwise, and \(T_k\) is the number of treatments included in the \(k\)th trial. Let the scaled random effects \(\boldsymbol{\gamma}_k = (\gamma_{1k},\ldots,\gamma_{Tk})^\top\). Then, \(\boldsymbol{\gamma}_{k,o}=E_k^\top\boldsymbol{\gamma}_k\) is the vector of \(T_k\)-dimensional scaled random effects for the \(k\)th trial. The scaled random effects \(\boldsymbol{\gamma}_k \sim t_T(\boldsymbol{\gamma},\boldsymbol{\rho},\nu)\) where \(t_T(\boldsymbol{\mu},\Sigma,\nu)\) denotes a multivariate \(t\) distribution with \(\nu\) degrees of freedom, a location parameter vector \(\boldsymbol{\mu}\), and a scale matrix \(\Sigma\).

The non-centered reparametrization (Bernardo et al. 2003) gives \(\boldsymbol{\gamma}_{k,o} = E_k^\top(\boldsymbol{\gamma}_k - \boldsymbol{\gamma})\). Then, with \(\bar{\boldsymbol{y}}_k = (\bar{y}_{kt_{k1}},\ldots,\bar{y}_{kt_{kT_k}})^\top\), \(\boldsymbol{X}_k = (\boldsymbol{x}_{kt_{k1}},\ldots, \boldsymbol{x}_{kt_{kT_k}})\), and \(\boldsymbol{Z}_k(\boldsymbol{\phi}) = \mathrm{diag}(\exp(\boldsymbol{z}_{kt_{k1}}^\top \boldsymbol{\phi}),\ldots, \exp(\boldsymbol{z}_{kt_{kT_k}}^\top \boldsymbol{\phi}))\), the model is recast as

\[ \bar{\boldsymbol{y}}_k = \boldsymbol{X}_k^* \boldsymbol{\theta} + \boldsymbol{Z}_k(\boldsymbol{\phi}) \boldsymbol{\gamma}_{k,o} + \bar{\boldsymbol{\epsilon}}_k, \] where \(\boldsymbol{X}_k^* = (\boldsymbol{X}_k, E_k^\top)\), \(\boldsymbol{\theta} = (\boldsymbol{\beta}^\top, \boldsymbol{\gamma}^\top)^\top\), and \(\bar{\boldsymbol{\epsilon}}_k \sim \mathcal{N}_{T_k}(\boldsymbol{0},\Sigma_k)\), \(\Sigma_k = \mathrm{diag}(\sigma_{kt_{k1}}^2/n_{kt_{k1}}, \ldots, \sigma_{kt_{kT_k}}^2/n_{kt_{kT_k}})\). This allows the random effects \(\boldsymbol{\gamma}_{k,o} \sim t_{T_k}(\boldsymbol{0},E_k^\top \boldsymbol{\rho}E_k, \nu)\) to be centered at zero.

Since the multivariate \(t\) random effects are not analytically marginalizable, we represent it as a scale mixture of normals as follows:

\[ (\boldsymbol{\gamma}_{k,o}\mid \lambda_k) \overset{\text{ind}}{\sim} \mathcal{N}_{T_k}\left(\boldsymbol{0}, \lambda_k^{-1}(E_k^\top\boldsymbol{\rho}E_k) \right), \quad \lambda_k \overset{\text{iid}}{\sim}\mathcal{G}a\left(\dfrac{\nu}{2},\dfrac{\nu}{2} \right), \] where \(\mathcal{G}a(a,b)\) indicates the gamma distribution with mean \(a/b\) and variance \(a/b^2\).

References

Bernardo, JM, MJ Bayarri, JO Berger, AP Dawid, D Heckerman, AFM Smith, and M West. 2003. “Non-Centered Parameterisations for Hierarchical Models and Data Augmentation.” In Bayesian Statistics 7: Proceedings of the Seventh Valencia International Meeting. Vol. 307. Oxford University Press, USA.
Zeileis, Achim, and Yves Croissant. 2010. “Extended Model Formulas in R: Multiple Parts and Multiple Responses.” Journal of Statistical Software 34 (1): 1–13. https://doi.org/10.18637/jss.v034.i01.