Prediction of conditional mean
Mathematical Derivations
Since residuals can be correlated, potentially existing observed outcomes of the same individual can be informative for predicting the unobserved valued of the same individual.
Assume that the data is sorted such that \(Y_{ij} = y_{ij}, j = k+1, k+2, \dots, p\) are observed and \(Y_{ij}, j = 1, 2, \dots, k\) are not. The special case of all outcomes being unobserved (new individual) is covered with \(k=p\).
Let further \[ \Sigma_i(X_i, \theta) = \begin{pmatrix} \Sigma_i^{new,new}(X_i,\theta) & \Sigma_i^{new,old}(X_i,\theta)\\ \Sigma_i^{old,new}(X_i,\theta) & \Sigma_i^{old,old}(X_i,\theta)\end{pmatrix} \]
be a block decomposition where \(\Sigma_i^{new,new}(X_i,\theta) = \Big(\big(\Sigma_i(X_i,\theta)\big)_{j,l}\Big)_{j = 1\dots k,\, l = 1\ldots k}\) and similarly for the other blocks.
Predictions can then be made based on the conditional distribution \[ Y_{i, 1\ldots k}\,\,X_i,Y_{i,k+1\ldots p}=y_{i, k+1\ldots p}\sim\mathcal{N}(\mu_i, A_i) \]
with
\[ \mu_i(\beta,\theta) = (X_i \ \beta)_{1\ldots k} + \Sigma_i^{new,old}(X_i,\theta) \, \Big(\big(\Sigma_i^{old,old}(X_i,\theta)\big)^{1} \big(y_i^{k+1\ldots p}  (X_i \ \beta)_{k+1\ldots p}\big)\Big) \] and
\[ A_i(\beta, \theta) = \Sigma_i^{new,new}(X_i,\theta)  \Sigma_i^{old,new}(X_i,\theta) \Big(\Sigma_i^{old,old}(X_i,\theta)\Big)^{1} \Sigma_i^{new,old}(X_i,\theta) \ . \] Note that \(A_i\) does not depend on \(\beta\).
Implementation of predict
For implementing predict()
, only \(\widehat{\mu}_i:=\mu_i(\widehat{\beta},\widehat{\theta})\)
is required.
For predict(interval = "confidence")
additionally
standard errors are required. These could be derived using the delta
methods since \(\mu_i\) is a function
of the estimated model parameters \(\beta\) and \(\theta\). This would require the Jacobian
\(\nabla\mu_i(\beta,\theta)_{\big(\widehat{\beta},\widehat{\theta}\big)}\)
in addition to the estimated variance covariance matrix of the parameter
estimate \(\big(\widehat{\beta},\widehat{\theta}\big)\),
\(\widehat{S}\). Standard errors for
\(\widehat{\mu}^{\,(i)}\) are then
given by the square root of the diagonal elements of \[
\Big(\nabla\mu_i(\beta,\theta)_{\big(\widehat{\beta},\widehat{\theta}\big)}\Big)^\top\quad
\widehat{S} \quad
\Big(\nabla\mu_i(\beta,\theta)_{\big(\widehat{\beta},\widehat{\theta}\big)}\Big)
\] For predict(interval = "prediction")
one would
use the square root of the diagonal elements of \(A_i\big(\widehat{\beta},\widehat{\theta}\big)\)
instead. The delta method could again be used to make upper and lower
boundaries reflect parameter estimation uncertainty.
Alternatively, both intervals can be derived using a parametric
bootstrap sample of the unrestricted parameters \(\theta\). This would probably also be
easier for the interval = "prediction"
case.
Please note that for these intervals, we assume that the distribution is approximately normal: we use \(\mu_{i,j}(\hat\beta, \hat\theta) \pm Z_{\alpha} * sqrt(A_{i, j, j}(\hat\beta, \hat\theta))\) to construct it, where \(\mu_{i,j}(\hat\beta, \hat\theta)\) is the \(j\)th element of \(\mu_i(\hat\beta, \hat\theta)\), and \(A_{i, j, j}(\hat\beta, \hat\theta)\) is the \(j,j\) element of \(A_i(\hat\beta, \hat\theta)\).
Parametric Sampling for Prediction Interval
With the conditional variance formula
\[ Var(Y_i) = Var(E(Y_i\theta)) + E(Var(Y_i\theta)) \]
and the conditional expectation \(E(Y_i\theta)\) and the conditional variance \(Var(Y_i\theta)\) being already described as
\[ E(Y_i\theta) = \mu_i(\beta, \theta) \]
and
\[ Var(Y_i\theta) = A_i(\beta, \theta), \]
we can sample on \(\theta\) and obtain \(\beta\), then calculate the variance of conditional mean and the mean of conditional variance.
Simulate response
To create simulation of responses from a fitted model, we have multiple situations: whether this simulation is conditional on both \(\theta\) and \(\beta\) estimates, or it is marginal?
Conditional Simulation
Under conditional simulation setting, the variancecovariance matrix, and the expectation of \(Y_i\) are already given in Mathematical Derivations.
Please note that in implementation of predict
function,
we only use the diagonal elements of \(A_i\), however, here we need to make use of
the full matrix \(A_i\) to obtain
correctly correlated simulated observations.
Marginal Simulation
To simulate marginally, we take the variance of \(\hat\theta\) and \(\hat\beta\) into consideration. For each simulation, we first generate \(\theta\) assuming it approximately follows a multivariate normal distribution. Then, conditional on the \(\theta\) we sampled, we generate \(\beta\) also assuming it approximately follows a multivariate normal distribution.
Now we have \(\theta\) and \(\beta\) estimates, and we just follow the conditional simulation.
Implementation of simulate
To implement simulate
function, we first ensure that the
expectation (\(\mu\)) and
variancecovariance matrix (\(A\)) are
generated in predict
function, for each of the
subjects.
For simulate(method = "conditional")
, we use the
estimated \(\theta\) and \(\beta\) to construct the \(\mu\) and \(A\) directly, and generate response with
\(N(\mu, A)\) distribution.
For simulate(method = "marginal")
, for each repetition
of simulation, we generate \(\theta_{new}\) from the mmrm fit, where the
estimate of \(\theta\) and
variancecovariance matrix of \(\theta\) are provided. Using the generated
\(\theta_{new}\), we then obtain the
\(\beta_{new}\) and its
variancecovariance matrix, with \(\theta_{new}\) and the data used in
fit.
Then we sample \(\beta\) as follows.
We note that on the C++
side we already have the robust
Cholesky decomposition of the inverse of its asymptotic covariance
matrix: \[
cov(\hat\beta) = (X^\top W X)^{1} = (LDL^\top)^{1}
\] Hence we make sure to report the lower triangular matrix \(L\) and the diagonal matrix \(D\) back to the R
side, and
afterwards we can generate \(\beta\)
samples as follows: \[
\beta_{sample} = \beta_{new} + L^{\top}D^{1/2}z_{sample}
\] where \(z_{sample}\) is drawn
from the standard multivariate normal distribution, since \[
cov(L^{\top}D^{1/2}z_{sample})
= L^{\top}D^{1/2} I_p D^{1/2} L^{1}
= L^{\top}D^{1}L^{1}
= (LDL^\top)^{1}
= cov(\hat\beta)
\] We note that calculating \(w =
L^{\top}D^{1/2}z_{sample}\) is efficient via backwards solving
\[
L^\top w = D^{1/2}z_{sample}
\] since \(L^\top\) is upper
right triangular.
Then we simulate the observations once with
simulate(method = "conditional", beta = beta_sample, theta = theta_new)
.
We pool all the repetitions together and thus obtain the marginal
simulation results.
Relationship Between predict
and simulate
Results
We summarize the different options for predict
and
simulate
methods and explain how they relate to each
other.
predict
options

predict(type = "confidence")
gives the variance of the predictions conditional on the \(\theta\) estimate, taking into account the uncertainty of estimating \(\beta\). So here we ignore the uncertainty in estimating \(\theta\). It also does not add measurement error \(\epsilon\). We can use this prediction when we are only interested in predicting the mean of the unobserved \(y_i\), assuming the estimated \(\theta\) as the true variance parameters. 
predict(type = "prediction")
in contrast takes into account the full uncertainty, including the variance of \(\theta\) and the measurement error \(\epsilon\). We can use this prediction when we are interested in reliable confidence intervals for the unobserved \(y_i\) as well as the observed \(y_i\), assuming we would like to predict repeated observations from the same subjects and time points.
simulate
options

simulate(type = "conditional")
simulates observations while keeping the \(\theta\) and \(\beta\) estimates fixed. It adds measurement errors \(\epsilon\) when generating the simulated values. Hence the mean of the simulated values will be within the confidence intervals frompredict(type = "conditional")
. 
simulate(type = "marginal")
simulates observations while taking into account the uncertainty of \(\beta\) and \(\theta\) through sampling from their asymptotic frequentist distributions. On top of that, it also adds measurement errors \(\epsilon\). This hence is using the same distribution aspredict(type = "prediction")
.
Comparison with SAS
In SAS
, from proc mixed
, we are able to
generate predictions using the outp
argument in the
model
statement. For example:
PROC MIXED DATA = fev_data method=reml;
CLASS RACE(ref = 'Asian') AVISIT(ref = 'VIS4') SEX(ref = 'Male') ARMCD(ref = 'PBO') USUBJID;
MODEL FEV1 = ARMCD / ddfm=Satterthewaite solution chisq outp=pred;
REPEATED AVISIT / subject=USUBJID type=un r rcorr;
LSMEANS ARMCD / pdiff=all cl alpha=0.05 slice=AVISIT;
RUN;
However, there are some differences between the SAS
implementation and our mmrm
package, described as
follows:
 While
mmrm
andSAS
both provide predicted means (conditional on other observations) for unobserved records,SAS
also provides predicted means for observed records whilemmrm
does not. The rationale is that in themmrm
package we want to be consistent with the notion of predictions conditional on the observed records  which means that observed records are observed and therefore there is no prediction uncertainty anymore.  The prediction standard error is different between
mmrm
andSAS
. While inSAS
the prediction standard error is conditional on the estimated variance parameters \(\theta\), inmmrm
the marginal prediction standard error is provided. The rationale is that in themmrm
package we want to take into account the full uncertainty about parameter estimates including \(\theta\).  The prediction intervals in
SAS
are based on the t distribution, while currently inmmrm
we use the normal distribution. We will be considering an extension towards using the t distribution in the future and welcome feedback on this detail.