Package Introduction

Common usage

A minimal call of mmrm(), consisting of only formula and data arguments will produce an object of class mmrm, mmrm_fit, and mmrm_tmb.

Here we fit a mmrm model with us (unstructured) covariance structure specified, as well as the defaults of reml = TRUE and control = mmrm_control().

library(mmrm)
fit <- mmrm(
  formula = FEV1 ~ RACE + SEX + ARMCD * AVISIT + us(AVISIT | USUBJID),
  data = fev_data
)

The code specifies an MMRM with the given covariates and an unstructured covariance matrix for the timepoints (also called visits in the clinical trial context, here given by AVISIT) within the subjects (here USUBJID). While by default this uses restricted maximum likelihood (REML), it is also possible to use ML, see ?mmrm.

Printing the object will show you output which should be familiar to anyone who has used any popular modeling functions such as stats::lm(), stats::glm(), glmmTMB::glmmTMB(), and lme4::nlmer(). From this print out we see the function call, the data used, the covariance structure with number of variance parameters, as well as the likelihood method, and model deviance achieved. Additionally the user is provided a printout of the estimated coefficients and the model convergence information:

fit
#> mmrm fit
#> 
#> Formula:     FEV1 ~ RACE + SEX + ARMCD * AVISIT + us(AVISIT | USUBJID)
#> Data:        fev_data (used 537 observations from 197 subjects with maximum 4 
#> timepoints)
#> Covariance:  unstructured (10 variance parameters)
#> Inference:   REML
#> Deviance:    3386.45
#> 
#> Coefficients: 
#>                   (Intercept) RACEBlack or African American 
#>                   30.77747548                    1.53049977 
#>                     RACEWhite                     SEXFemale 
#>                    5.64356535                    0.32606192 
#>                      ARMCDTRT                    AVISITVIS2 
#>                    3.77423004                    4.83958845 
#>                    AVISITVIS3                    AVISITVIS4 
#>                   10.34211288                   15.05389826 
#>           ARMCDTRT:AVISITVIS2           ARMCDTRT:AVISITVIS3 
#>                   -0.04192625                   -0.69368537 
#>           ARMCDTRT:AVISITVIS4 
#>                    0.62422703 
#> 
#> Model Inference Optimization:
#> Converged with code 0 and message: convergence: rel_reduction_of_f <= factr*epsmch

The summary() method then provides the coefficients table with Satterthwaite degrees of freedom as well as the covariance matrix estimate:

summary(fit)
#> mmrm fit
#> 
#> Formula:     FEV1 ~ RACE + SEX + ARMCD * AVISIT + us(AVISIT | USUBJID)
#> Data:        fev_data (used 537 observations from 197 subjects with maximum 4 
#> timepoints)
#> Covariance:  unstructured (10 variance parameters)
#> Method:      Satterthwaite
#> Vcov Method: Asymptotic
#> Inference:   REML
#> 
#> Model selection criteria:
#>      AIC      BIC   logLik deviance 
#>   3406.4   3439.3  -1693.2   3386.4 
#> 
#> Coefficients: 
#>                                Estimate Std. Error        df t value Pr(>|t|)
#> (Intercept)                    30.77748    0.88656 218.80000  34.715  < 2e-16
#> RACEBlack or African American   1.53050    0.62448 168.67000   2.451 0.015272
#> RACEWhite                       5.64357    0.66561 157.14000   8.479 1.56e-14
#> SEXFemale                       0.32606    0.53195 166.13000   0.613 0.540744
#> ARMCDTRT                        3.77423    1.07415 145.55000   3.514 0.000589
#> AVISITVIS2                      4.83959    0.80172 143.88000   6.037 1.27e-08
#> AVISITVIS3                     10.34211    0.82269 155.56000  12.571  < 2e-16
#> AVISITVIS4                     15.05390    1.31281 138.47000  11.467  < 2e-16
#> ARMCDTRT:AVISITVIS2            -0.04193    1.12932 138.56000  -0.037 0.970439
#> ARMCDTRT:AVISITVIS3            -0.69369    1.18765 158.17000  -0.584 0.559996
#> ARMCDTRT:AVISITVIS4             0.62423    1.85085 129.72000   0.337 0.736463
#>                                  
#> (Intercept)                   ***
#> RACEBlack or African American *  
#> RACEWhite                     ***
#> SEXFemale                        
#> ARMCDTRT                      ***
#> AVISITVIS2                    ***
#> AVISITVIS3                    ***
#> AVISITVIS4                    ***
#> ARMCDTRT:AVISITVIS2              
#> ARMCDTRT:AVISITVIS3              
#> ARMCDTRT:AVISITVIS4              
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Covariance estimate:
#>         VIS1    VIS2    VIS3    VIS4
#> VIS1 40.5537 14.3960  4.9747 13.3867
#> VIS2 14.3960 26.5715  2.7855  7.4745
#> VIS3  4.9747  2.7855 14.8979  0.9082
#> VIS4 13.3867  7.4745  0.9082 95.5568

Common customizations

From the high-level mmrm() interface, common changes to the default function call can be specified.

Control Function

For fine control, mmrm_control() is provided. This function allows the user to choose the adjustment method for the degrees of freedom and the coefficients covariance matrix, specify optimization routines, number of cores to be used on Unix systems for trying several optimizers in parallel, provide a vector of starting parameter values, decide the action to be taken when the defined design matrix is singular, not drop unobserved visit levels. For example:

mmrm_control(
  method = "Kenward-Roger",
  optimizer = c("L-BFGS-B", "BFGS"),
  n_cores = 2,
  start = c(0, 1, 1, 0, 1, 0),
  accept_singular = FALSE,
  drop_visit_levels = FALSE
)

Note that this control list can either be passed via the control argument to mmrm, or selected controls can be directly specified in the mmrm call. We will see this below.

REML or ML

Users can specify if REML should be used (default) or if ML should be used in optimization.

fit_ml <- mmrm(
  formula = FEV1 ~ RACE + ARMCD * AVISIT + us(AVISIT | USUBJID),
  data = fev_data,
  reml = FALSE
)
fit_ml
#> mmrm fit
#> 
#> Formula:     FEV1 ~ RACE + ARMCD * AVISIT + us(AVISIT | USUBJID)
#> Data:        fev_data (used 537 observations from 197 subjects with maximum 4 
#> timepoints)
#> Covariance:  unstructured (10 variance parameters)
#> Inference:   ML
#> Deviance:    3397.934
#> 
#> Coefficients: 
#>                   (Intercept) RACEBlack or African American 
#>                    30.9663423                     1.5086851 
#>                     RACEWhite                      ARMCDTRT 
#>                     5.6133151                     3.7761037 
#>                    AVISITVIS2                    AVISITVIS3 
#>                     4.8270155                    10.3353319 
#>                    AVISITVIS4           ARMCDTRT:AVISITVIS2 
#>                    15.0487715                    -0.0156154 
#>           ARMCDTRT:AVISITVIS3           ARMCDTRT:AVISITVIS4 
#>                    -0.6663598                     0.6317222 
#> 
#> Model Inference Optimization:
#> Converged with code 0 and message: convergence: rel_reduction_of_f <= factr*epsmch

Optimizer

Users can specify which optimizer should be used, changing from the default of four optimizers, which starts with L-BFGS-B and proceeds through the other choices if optimization fails to converge. Other choices are BFGS, CG, nlminb and other user-defined custom optimizers.

L-BFGS-B, BFGS and CG are all implemented with stats::optim() and the Hessian is not used, while nlminb is using stats::nlminb() which in turn uses both the gradient and the Hessian (by default but can be switch off) for the optimization.

fit_opt <- mmrm(
  formula = FEV1 ~ RACE + ARMCD * AVISIT + us(AVISIT | USUBJID),
  data = fev_data,
  optimizer = "BFGS"
)
fit_opt
#> mmrm fit
#> 
#> Formula:     FEV1 ~ RACE + ARMCD * AVISIT + us(AVISIT | USUBJID)
#> Data:        fev_data (used 537 observations from 197 subjects with maximum 4 
#> timepoints)
#> Covariance:  unstructured (10 variance parameters)
#> Inference:   REML
#> Deviance:    3387.373
#> 
#> Coefficients: 
#>                   (Intercept) RACEBlack or African American 
#>                    30.9676902                     1.5046744 
#>                     RACEWhite                      ARMCDTRT 
#>                     5.6131048                     3.7755423 
#>                    AVISITVIS2                    AVISITVIS3 
#>                     4.8285855                    10.3331770 
#>                    AVISITVIS4           ARMCDTRT:AVISITVIS2 
#>                    15.0525706                    -0.0173504 
#>           ARMCDTRT:AVISITVIS3           ARMCDTRT:AVISITVIS4 
#>                    -0.6675190                     0.6309586 
#> 
#> Model Inference Optimization:
#> Converged with code 0 and message:

Covariance Structure

Covariance structures supported by the mmrm are being continuously developed. For a complete list and description please visit the covariance vignette. Below we see the function call for homogeneous compound symmetry (cs).

fit_cs <- mmrm(
  formula = FEV1 ~ RACE + ARMCD * AVISIT + cs(AVISIT | USUBJID),
  data = fev_data,
  reml = FALSE
)
fit_cs
#> mmrm fit
#> 
#> Formula:     FEV1 ~ RACE + ARMCD * AVISIT + cs(AVISIT | USUBJID)
#> Data:        fev_data (used 537 observations from 197 subjects with maximum 4 
#> timepoints)
#> Covariance:  compound symmetry (2 variance parameters)
#> Inference:   ML
#> Deviance:    3536.989
#> 
#> Coefficients: 
#>                   (Intercept) RACEBlack or African American 
#>                    31.4207077                     0.5357237 
#>                     RACEWhite                      ARMCDTRT 
#>                     5.4546329                     3.4305212 
#>                    AVISITVIS2                    AVISITVIS3 
#>                     4.8326353                    10.2395076 
#>                    AVISITVIS4           ARMCDTRT:AVISITVIS2 
#>                    15.0672680                     0.2801641 
#>           ARMCDTRT:AVISITVIS3           ARMCDTRT:AVISITVIS4 
#>                    -0.5894964                     0.7939750 
#> 
#> Model Inference Optimization:
#> Converged with code 0 and message: convergence: rel_reduction_of_f <= factr*epsmch

The time points have to be unique for each subject. That is, there cannot be time points with multiple observations for any subject. The rationale is that these observations would need to be correlated, but it is not possible within the currently implemented covariance structure framework to do that correctly. Moreover, for non-spatial covariance structures, the time variable must be coded as a factor.

Weighting

Users can perform weighted MMRM by specifying a numeric vector weights with positive values.

fit_wt <- mmrm(
  formula = FEV1 ~ RACE + ARMCD * AVISIT + us(AVISIT | USUBJID),
  data = fev_data,
  weights = fev_data$WEIGHT
)
fit_wt
#> mmrm fit
#> 
#> Formula:     FEV1 ~ RACE + ARMCD * AVISIT + us(AVISIT | USUBJID)
#> Data:        fev_data (used 537 observations from 197 subjects with maximum 4 
#> timepoints)
#> Weights:     fev_data$WEIGHT
#> Covariance:  unstructured (10 variance parameters)
#> Inference:   REML
#> Deviance:    3476.526
#> 
#> Coefficients: 
#>                   (Intercept) RACEBlack or African American 
#>                   31.20065229                    1.18452837 
#>                     RACEWhite                      ARMCDTRT 
#>                    5.36525917                    3.39695951 
#>                    AVISITVIS2                    AVISITVIS3 
#>                    4.85890820                   10.03942420 
#>                    AVISITVIS4           ARMCDTRT:AVISITVIS2 
#>                   14.79354054                    0.03418184 
#>           ARMCDTRT:AVISITVIS3           ARMCDTRT:AVISITVIS4 
#>                    0.01308088                    0.86701567 
#> 
#> Model Inference Optimization:
#> Converged with code 0 and message: convergence: rel_reduction_of_f <= factr*epsmch

Grouped Covariance Structure

Grouped covariance structures are supported by themmrm package. Covariance matrices for each group are identically structured (unstructured, compound symmetry, etc) but the estimates are allowed to vary across groups. We use the form cs(time | group / subject) to specify the group variable.

Here is an example of how we use ARMCD as group variable.

fit_cs <- mmrm(
  formula = FEV1 ~ RACE + ARMCD * AVISIT + cs(AVISIT | ARMCD / USUBJID),
  data = fev_data,
  reml = FALSE
)
VarCorr(fit_cs)
#> $PBO
#>           VIS1      VIS2      VIS3      VIS4
#> VIS1 37.823638  3.601296  3.601296  3.601296
#> VIS2  3.601296 37.823638  3.601296  3.601296
#> VIS3  3.601296  3.601296 37.823638  3.601296
#> VIS4  3.601296  3.601296  3.601296 37.823638
#> 
#> $TRT
#>          VIS1     VIS2     VIS3     VIS4
#> VIS1 49.58110 10.98112 10.98112 10.98112
#> VIS2 10.98112 49.58110 10.98112 10.98112
#> VIS3 10.98112 10.98112 49.58110 10.98112
#> VIS4 10.98112 10.98112 10.98112 49.58110

We can see that the estimated covariance matrices are different in different ARMCD groups.

Adjustment Method

In additional to the residual and Between-Within degrees of freedom, both Satterthwaite and Kenward-Roger adjustment methods are available. The default is Satterthwaite adjustment of the degrees of freedom. To use e.g. the Kenward-Roger adjustment of the degrees of freedom as well as the coefficients covariance matrix, use the method argument:

A list of all allowed method is

1. “Kenward-Roger”
2. “Satterthwaite”
3. “Residual”
4. “Between-Within”

fit_kr <- mmrm(
  formula = FEV1 ~ RACE + ARMCD * AVISIT + us(AVISIT | USUBJID),
  data = fev_data,
  method = "Kenward-Roger"
)

Note that this requires reml = TRUE, i.e. Kenward-Roger adjustment is not possible when using maximum likelihood inference. While this adjustment choice is not visible in the print() result of the fitted model (because the initial model fit is not affected by the choice of the adjustment method), looking at the summary we see the method and the correspondingly adjusted standard errors and degrees of freedom:

summary(fit_kr)
#> mmrm fit
#> 
#> Formula:     FEV1 ~ RACE + ARMCD * AVISIT + us(AVISIT | USUBJID)
#> Data:        fev_data (used 537 observations from 197 subjects with maximum 4 
#> timepoints)
#> Covariance:  unstructured (10 variance parameters)
#> Method:      Kenward-Roger
#> Vcov Method: Kenward-Roger
#> Inference:   REML
#> 
#> Model selection criteria:
#>      AIC      BIC   logLik deviance 
#>   3407.4   3440.2  -1693.7   3387.4 
#> 
#> Coefficients: 
#>                                Estimate Std. Error        df t value Pr(>|t|)
#> (Intercept)                    30.96770    0.83335 187.91000  37.160  < 2e-16
#> RACEBlack or African American   1.50465    0.62901 169.95000   2.392  0.01784
#> RACEWhite                       5.61310    0.67139 158.87000   8.360 2.98e-14
#> ARMCDTRT                        3.77556    1.07910 146.27000   3.499  0.00062
#> AVISITVIS2                      4.82859    0.80408 143.66000   6.005 1.49e-08
#> AVISITVIS3                     10.33317    0.82303 155.66000  12.555  < 2e-16
#> AVISITVIS4                     15.05256    1.30180 138.39000  11.563  < 2e-16
#> ARMCDTRT:AVISITVIS2            -0.01737    1.13154 138.39000  -0.015  0.98777
#> ARMCDTRT:AVISITVIS3            -0.66753    1.18714 158.21000  -0.562  0.57470
#> ARMCDTRT:AVISITVIS4             0.63094    1.83319 129.64000   0.344  0.73127
#>                                  
#> (Intercept)                   ***
#> RACEBlack or African American *  
#> RACEWhite                     ***
#> ARMCDTRT                      ***
#> AVISITVIS2                    ***
#> AVISITVIS3                    ***
#> AVISITVIS4                    ***
#> ARMCDTRT:AVISITVIS2              
#> ARMCDTRT:AVISITVIS3              
#> ARMCDTRT:AVISITVIS4              
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Covariance estimate:
#>         VIS1    VIS2    VIS3    VIS4
#> VIS1 40.7335 14.2740  5.1411 13.5288
#> VIS2 14.2740 26.2243  2.6391  7.3219
#> VIS3  5.1411  2.6391 14.9497  1.0341
#> VIS4 13.5288  7.3219  1.0341 95.6006

For one-dimensional contrasts as in the coefficients table above, the degrees of freedom are the same for Kenward-Roger and Satterthwaite. However, Kenward-Roger uses adjusted standard errors, hence the p-values are different.

Note that if you would like to match SAS results for an unstructured covariance model, you can use the linear Kenward-Roger approximation:

fit_kr_lin <- mmrm(
  formula = FEV1 ~ RACE + ARMCD * AVISIT + us(AVISIT | USUBJID),
  data = fev_data,
  method = "Kenward-Roger",
  vcov = "Kenward-Roger-Linear"
)

This is due to the different parametrization of the unstructured covariance matrix, see the Kenward-Roger vignette for details.

Variance-covariance for Coefficients

There are multiple variance-covariance estimator available for the coefficients, including:

1. “Asymptotic”
2. “Empirical” (Cluster Robust Sandwich)
3. “Empirical-Jackknife”
4. “Empirical-Bias-Reduced”
5. “Kenward-Roger”
6. “Kenward-Roger-Linear”

Please note that, not all combinations of variance-covariance for coefficients and method of degrees of freedom are possible, e.g. “Kenward-Roger” and “Kenward-Roger-Linear” are available only when the degrees of freedom method is “Kenward-Roger”.

Details can be found in Coefficients Covariance Matrix Adjustment vignette and Weighted Least Square Empirical Covariance.

An example of using other variance-covariance is:

fit_emp <- mmrm(
  formula = FEV1 ~ RACE + ARMCD * AVISIT + us(AVISIT | USUBJID),
  data = fev_data,
  method = "Satterthwaite",
  vcov = "Empirical"
)

Keeping Unobserved Visits

Sometimes not all possible time points are observed in a given data set. When using a structured covariance matrix, e.g. with auto-regressive structure, then it can be relevant to keep the correct distance between the observed time points.

Consider the following example where we have deliberately removed the VIS3 observations from our initial example data set fev_data to obtain sparse_data. We first fit the model where we do not drop the visit level explicitly, using the drop_visit_levels = FALSE choice. Second we fit the model by default without this option.

sparse_data <- fev_data[fev_data$AVISIT != "VIS3", ]
sparse_result <- mmrm(
  FEV1 ~ RACE + ar1(AVISIT | USUBJID),
  data = sparse_data,
  drop_visit_levels = FALSE
)

dropped_result <- mmrm(
  FEV1 ~ RACE + ar1(AVISIT | USUBJID),
  data = sparse_data
)
#> In AVISIT there are dropped visits: VIS3

We see that we get a message about the dropped visit level by default. Now we can compare the estimated correlation matrices:

cov2cor(VarCorr(sparse_result))
#>            VIS1      VIS2      VIS3       VIS4
#> VIS1 1.00000000 0.4051834 0.1641736 0.06652042
#> VIS2 0.40518341 1.0000000 0.4051834 0.16417360
#> VIS3 0.16417360 0.4051834 1.0000000 0.40518341
#> VIS4 0.06652042 0.1641736 0.4051834 1.00000000
cov2cor(VarCorr(dropped_result))
#>            VIS1      VIS2       VIS4
#> VIS1 1.00000000 0.1468464 0.02156386
#> VIS2 0.14684640 1.0000000 0.14684640
#> VIS4 0.02156386 0.1468464 1.00000000

We see that when using the default, second result, we just drop the VIS3 from the covariance matrix. As a consequence, we model the correlation between VIS2 and VIS4 the same as the correlation between VIS1 and VIS2. Hence we get a smaller correlation estimate here compared to the first result, which includes VIS3 explicitly.

Extraction of model features

Similar to model objects created in other packages, components of mmrm and mmrm_tmb objects can be accessed with standard methods. Additionally, component() is provided to allow deeper and more precise access for those interested in digging through model output. Complete documentation of standard model output methods supported for mmrm_tmb objects can be found at the package website.

Summary

The summary method for mmrm objects provides easy access to frequently needed model components.

fit <- mmrm(
  formula = FEV1 ~ RACE + ARMCD * AVISIT + us(AVISIT | USUBJID),
  data = fev_data
)
fit_summary <- summary(fit)

From this summary object, you can easily retrieve the coefficients table.

fit_summary$coefficients
#>                                  Estimate Std. Error       df     t value
#> (Intercept)                   30.96769899  0.8293349 187.9132 37.34040185
#> RACEBlack or African American  1.50464863  0.6206596 169.9454  2.42427360
#> RACEWhite                      5.61309565  0.6630909 158.8700  8.46504747
#> ARMCDTRT                       3.77555734  1.0762774 146.2690  3.50797778
#> AVISITVIS2                     4.82858803  0.8017144 143.6593  6.02282805
#> AVISITVIS3                    10.33317002  0.8224414 155.6572 12.56401918
#> AVISITVIS4                    15.05255715  1.3128602 138.3916 11.46546844
#> ARMCDTRT:AVISITVIS2           -0.01737409  1.1291645 138.3926 -0.01538668
#> ARMCDTRT:AVISITVIS3           -0.66753189  1.1865359 158.2106 -0.56258887
#> ARMCDTRT:AVISITVIS4            0.63094392  1.8507884 129.6377  0.34090549
#>                                   Pr(>|t|)
#> (Intercept)                   7.122411e-89
#> RACEBlack or African American 1.638725e-02
#> RACEWhite                     1.605553e-14
#> ARMCDTRT                      6.001485e-04
#> AVISITVIS2                    1.366921e-08
#> AVISITVIS3                    1.927523e-25
#> AVISITVIS4                    8.242709e-22
#> ARMCDTRT:AVISITVIS2           9.877459e-01
#> ARMCDTRT:AVISITVIS3           5.745112e-01
#> ARMCDTRT:AVISITVIS4           7.337266e-01

Other model parameters and metadata available in the summary object is as follows:

str(fit_summary)
#> List of 15
#>  $ cov_type        : chr "us"
#>  $ reml            : logi TRUE
#>  $ n_groups        : int 1
#>  $ n_theta         : int 10
#>  $ n_subjects      : int 197
#>  $ n_timepoints    : int 4
#>  $ n_obs           : int 537
#>  $ beta_vcov       : num [1:10, 1:10] 0.688 -0.207 -0.163 -0.569 -0.422 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : chr [1:10] "(Intercept)" "RACEBlack or African American" "RACEWhite" "ARMCDTRT" ...
#>   .. ..$ : chr [1:10] "(Intercept)" "RACEBlack or African American" "RACEWhite" "ARMCDTRT" ...
#>  $ varcor          : num [1:4, 1:4] 40.73 14.27 5.14 13.53 14.27 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : chr [1:4] "VIS1" "VIS2" "VIS3" "VIS4"
#>   .. ..$ : chr [1:4] "VIS1" "VIS2" "VIS3" "VIS4"
#>  $ method          : chr "Satterthwaite"
#>  $ vcov            : chr "Asymptotic"
#>  $ coefficients    : num [1:10, 1:5] 30.97 1.5 5.61 3.78 4.83 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : chr [1:10] "(Intercept)" "RACEBlack or African American" "RACEWhite" "ARMCDTRT" ...
#>   .. ..$ : chr [1:5] "Estimate" "Std. Error" "df" "t value" ...
#>  $ n_singular_coefs: int 0
#>  $ aic_list        :List of 4
#>   ..$ AIC     : num 3407
#>   ..$ BIC     : num 3440
#>   ..$ logLik  : num -1694
#>   ..$ deviance: num 3387
#>  $ call            : language mmrm(formula = FEV1 ~ RACE + ARMCD * AVISIT + us(AVISIT | USUBJID), data = fev_data)
#>  - attr(*, "class")= chr "summary.mmrm"

Residuals

The residuals method for mmrm objects can be used to provide three different types of residuals:

1. Response or raw residuals - the difference between the observed and fitted or predicted value. MMRMs can allow for heteroscedasticity, so these residuals should be interpreted with caution.

fit <- mmrm(
  formula = FEV1 ~ RACE + ARMCD * AVISIT + us(AVISIT | USUBJID),
  data = fev_data
)
residuals_resp <- residuals(fit, type = "response")

2. Pearson residuals - the raw residuals scaled by the estimated standard deviation of the response. This residual type is better suited to identifying outlying observations and the appropriateness of the covariance structure, compared to the raw residuals.

residuals_pearson <- residuals(fit, type = "pearson")

3. Normalized or scaled residuals - the raw residuals are ‘de-correlated’ based on the Cholesky decomposition of the variance-covariance matrix. These residuals should approximately follow the standard normal distribution, and therefore can be used to check for normality (@galecki2013linear).

residuals_norm <- residuals(fit, type = "normalized")

broom extensions

mmrm also contains S3 methods methods for tidy, glance and augment which were introduced by broom. Note that these methods will work also without loading the broom package. Please see ?mmrm_tidiers for the detailed documentation.

For example, we can apply the tidy method to return a summary table of coefficient estimates:

fit |>
  tidy()
#> # A tibble: 10 × 6
#>    term                          estimate std.error    df statistic  p.value
#>    <chr>                            <dbl>     <dbl> <dbl>     <dbl>    <dbl>
#>  1 (Intercept)                    31.0        0.829  188.   37.3    7.12e-89
#>  2 RACEBlack or African American   1.50       0.621  170.    2.42   1.64e- 2
#>  3 RACEWhite                       5.61       0.663  159.    8.47   1.61e-14
#>  4 ARMCDTRT                        3.78       1.08   146.    3.51   6.00e- 4
#>  5 AVISITVIS2                      4.83       0.802  144.    6.02   1.37e- 8
#>  6 AVISITVIS3                     10.3        0.822  156.   12.6    1.93e-25
#>  7 AVISITVIS4                     15.1        1.31   138.   11.5    8.24e-22
#>  8 ARMCDTRT:AVISITVIS2            -0.0174     1.13   138.   -0.0154 9.88e- 1
#>  9 ARMCDTRT:AVISITVIS3            -0.668      1.19   158.   -0.563  5.75e- 1
#> 10 ARMCDTRT:AVISITVIS4             0.631      1.85   130.    0.341  7.34e- 1

We can also specify some details to request confidence intervals of specific confidence level:

fit |>
  tidy(conf.int = TRUE, conf.level = 0.9)
#> # A tibble: 10 × 8
#>    term           estimate std.error    df statistic  p.value conf.low conf.high
#>    <chr>             <dbl>     <dbl> <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
#>  1 (Intercept)     31.0        0.829  188.   37.3    7.12e-89   29.6       32.3 
#>  2 ARMCDTRT         3.78       1.08   146.    3.51   6.00e- 4    2.01       5.55
#>  3 ARMCDTRT:AVIS…  -0.0174     1.13   138.   -0.0154 9.88e- 1   -1.87       1.84
#>  4 ARMCDTRT:AVIS…  -0.668      1.19   158.   -0.563  5.75e- 1   -2.62       1.28
#>  5 ARMCDTRT:AVIS…   0.631      1.85   130.    0.341  7.34e- 1   -2.41       3.68
#>  6 AVISITVIS2       4.83       0.802  144.    6.02   1.37e- 8    3.51       6.15
#>  7 AVISITVIS3      10.3        0.822  156.   12.6    1.93e-25    8.98      11.7 
#>  8 AVISITVIS4      15.1        1.31   138.   11.5    8.24e-22   12.9       17.2 
#>  9 RACEBlack or …   1.50       0.621  170.    2.42   1.64e- 2    0.484      2.53
#> 10 RACEWhite        5.61       0.663  159.    8.47   1.61e-14    4.52       6.70

Or we can apply the glance method to return a summary table of goodness of fit statistics:

fit |>
  glance()
#> # A tibble: 1 × 4
#>     AIC   BIC logLik deviance
#>   <dbl> <dbl>  <dbl>    <dbl>
#> 1 3407. 3440. -1694.    3387.

Finally, we can use the augment method to return a merged tibble of the data, fitted values and residuals:

fit |>
  augment()
#> # A tibble: 537 × 8
#>    .rownames  FEV1 RACE                     ARMCD AVISIT USUBJID .fitted  .resid
#>        <dbl> <dbl> <fct>                    <fct> <fct>  <fct>     <dbl>   <dbl>
#>  1         2  40.0 Black or African Americ… TRT   VIS2   PT1        40.0  -1.09 
#>  2         4  20.5 Black or African Americ… TRT   VIS4   PT1        20.5 -31.4  
#>  3         6  31.5 Asian                    PBO   VIS2   PT2        31.5  -4.34 
#>  4         7  36.9 Asian                    PBO   VIS3   PT2        36.9  -4.42 
#>  5         8  48.8 Asian                    PBO   VIS4   PT2        48.8   2.79 
#>  6        10  36.0 Black or African Americ… PBO   VIS2   PT3        36.0  -1.31 
#>  7        12  37.2 Black or African Americ… PBO   VIS4   PT3        37.2 -10.4  
#>  8        13  33.9 Asian                    TRT   VIS1   PT4        33.9  -0.851
#>  9        14  33.7 Asian                    TRT   VIS2   PT4        33.7  -5.81 
#> 10        16  54.5 Asian                    TRT   VIS4   PT4        54.5   4.02 
#> # ℹ 527 more rows

Also here we can specify details for the prediction intervals and type of residuals via the arguments:

fit |>
  augment(interval = "confidence", type.residuals = "normalized")
#> # A tibble: 537 × 11
#>    .rownames  FEV1 RACE       ARMCD AVISIT USUBJID .fitted .lower .upper .se.fit
#>        <dbl> <dbl> <fct>      <fct> <fct>  <fct>     <dbl>  <dbl>  <dbl>   <dbl>
#>  1         2  40.0 Black or … TRT   VIS2   PT1        40.0   40.0   40.0       0
#>  2         4  20.5 Black or … TRT   VIS4   PT1        20.5   20.5   20.5       0
#>  3         6  31.5 Asian      PBO   VIS2   PT2        31.5   31.5   31.5       0
#>  4         7  36.9 Asian      PBO   VIS3   PT2        36.9   36.9   36.9       0
#>  5         8  48.8 Asian      PBO   VIS4   PT2        48.8   48.8   48.8       0
#>  6        10  36.0 Black or … PBO   VIS2   PT3        36.0   36.0   36.0       0
#>  7        12  37.2 Black or … PBO   VIS4   PT3        37.2   37.2   37.2       0
#>  8        13  33.9 Asian      TRT   VIS1   PT4        33.9   33.9   33.9       0
#>  9        14  33.7 Asian      TRT   VIS2   PT4        33.7   33.7   33.7       0
#> 10        16  54.5 Asian      TRT   VIS4   PT4        54.5   54.5   54.5       0
#> # ℹ 527 more rows
#> # ℹ 1 more variable: .resid <dbl>

Other components

Specific model quantities not supported by methods can be retrieved with the component() function. The default will output all supported components.

For example, a user may want information about convergence:

component(fit, name = c("convergence", "evaluations", "conv_message"))
#> $convergence
#> [1] 0
#> 
#> $evaluations
#> function gradient 
#>       17       17 
#> 
#> $conv_message
#> [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH"

or the original low-level call:

component(fit, name = "call")
#> mmrm(formula = FEV1 ~ RACE + ARMCD * AVISIT + us(AVISIT | USUBJID), 
#>     data = fev_data)

the user could also ask for all provided components by not specifying the name argument.

component(fit)

Lower level functions

Low-level mmrm

The lower level function which is called by mmrm() is fit_mmrm(). This function is exported and can be used directly. It is similar to mmrm() but lacks some post-processing and support for Satterthwaite and Kenward-Roger calculations. It may be useful for other packages that want to fit the model without the adjustment calculations.

fit_mmrm(
  formula = FEV1 ~ RACE + ARMCD * AVISIT + us(AVISIT | USUBJID),
  data = fev_data,
  weights = rep(1, nrow(fev_data)),
  reml = TRUE,
  control = mmrm_control()
)
#> mmrm fit
#> 
#> Formula:     FEV1 ~ RACE + ARMCD * AVISIT + us(AVISIT | USUBJID)
#> Data:        fev_data (used 537 observations from 197 subjects with maximum 4 
#> timepoints)
#> Weights:     rep(1, nrow(fev_data))
#> Covariance:  unstructured (10 variance parameters)
#> Inference:   REML
#> Deviance:    3387.373
#> 
#> Coefficients: 
#>                   (Intercept) RACEBlack or African American 
#>                   30.96769899                    1.50464863 
#>                     RACEWhite                      ARMCDTRT 
#>                    5.61309565                    3.77555734 
#>                    AVISITVIS2                    AVISITVIS3 
#>                    4.82858803                   10.33317002 
#>                    AVISITVIS4           ARMCDTRT:AVISITVIS2 
#>                   15.05255715                   -0.01737409 
#>           ARMCDTRT:AVISITVIS3           ARMCDTRT:AVISITVIS4 
#>                   -0.66753189                    0.63094392 
#> 
#> Model Inference Optimization:
#> Converged with code 0 and message: convergence: rel_reduction_of_f <= factr*epsmch

Hypothesis testing

This package supports estimation of one- and multi-dimensional contrasts (t-test and F-test calculation) with the df_1d() and df_md() functions. Both functions utilize the chosen adjustment method from the initial mmrm call for the calculation of degrees of freedom and (for Kenward-Roger methods) the variance estimates for the test-statistics.

One-dimensional contrasts

Compute the test of a one-dimensional (vector) contrast for a mmrm object with Satterthwaite degrees of freedom.

fit <- mmrm(
  formula = FEV1 ~ RACE + SEX + ARMCD * AVISIT + us(AVISIT | USUBJID),
  data = fev_data
)

contrast <- numeric(length(component(fit, "beta_est")))
contrast[3] <- 1

df_1d(fit, contrast)
#> $est
#> [1] 5.643565
#> 
#> $se
#> [1] 0.6656093
#> 
#> $df
#> [1] 157.1382
#> 
#> $t_stat
#> [1] 8.478795
#> 
#> $p_val
#> [1] 1.564869e-14

This works similarly when choosing a Kenward-Roger adjustment:

fit_kr <- mmrm(
  formula = FEV1 ~ RACE + SEX + ARMCD * AVISIT + us(AVISIT | USUBJID),
  data = fev_data,
  method = "Kenward-Roger"
)

df_1d(fit_kr, contrast)
#> $est
#> [1] 5.643565
#> 
#> $se
#> [1] 0.6740941
#> 
#> $df
#> [1] 157.1382
#> 
#> $t_stat
#> [1] 8.372073
#> 
#> $p_val
#> [1] 2.931654e-14

We see that because this is a one-dimensional contrast, the degrees of freedoms are identical for Satterthwaite and Kenward-Roger. However, the standard errors are different and therefore the p-values are different.

Additional options for the degrees of freedom method are Residual and Between-Within.

Multi-dimensional contrasts

Compute the test of a multi-dimensional (matrix) contrast for the above defined mmrm object with Satterthwaite degrees of freedom:

contrast <- matrix(data = 0, nrow = 2, ncol = length(component(fit, "beta_est")))
contrast[1, 2] <- contrast[2, 3] <- 1

df_md(fit, contrast)
#> $num_df
#> [1] 2
#> 
#> $denom_df
#> [1] 165.5553
#> 
#> $f_stat
#> [1] 36.91143
#> 
#> $p_val
#> [1] 5.544575e-14

And for the Kenward-Roger adjustment:

df_md(fit_kr, contrast)
#> $num_df
#> [1] 2
#> 
#> $denom_df
#> [1] 165.5728
#> 
#> $f_stat
#> [1] 35.99422
#> 
#> $p_val
#> [1] 1.04762e-13

We see that for the multi-dimensional contrast we get slightly different denominator degrees of freedom for the two adjustment methods.

Also the simpler Residual and Between-Within method choices can be used of course together with multidimensional contrasts.

Support for emmeans

This package includes methods that allow mmrm objects to be used with the emmeans package. emmeans computes estimated marginal means (also called least-square means) for the coefficients of the MMRM. For example, in order to see the least-square means by visit and by treatment arm:

library(emmeans)
#> mmrm() registered as emmeans extension
lsmeans_by_visit <- emmeans(fit, ~ ARMCD | AVISIT)
lsmeans_by_visit
#> AVISIT = VIS1:
#>  ARMCD emmean    SE  df lower.CL upper.CL
#>  PBO     33.3 0.755 148     31.8     34.8
#>  TRT     37.1 0.763 143     35.6     38.6
#> 
#> AVISIT = VIS2:
#>  ARMCD emmean    SE  df lower.CL upper.CL
#>  PBO     38.2 0.612 147     37.0     39.4
#>  TRT     41.9 0.602 143     40.7     43.1
#> 
#> AVISIT = VIS3:
#>  ARMCD emmean    SE  df lower.CL upper.CL
#>  PBO     43.7 0.462 130     42.8     44.6
#>  TRT     46.8 0.509 130     45.7     47.8
#> 
#> AVISIT = VIS4:
#>  ARMCD emmean    SE  df lower.CL upper.CL
#>  PBO     48.4 1.189 134     46.0     50.7
#>  TRT     52.8 1.188 133     50.4     55.1
#> 
#> Results are averaged over the levels of: RACE, SEX 
#> Confidence level used: 0.95

Note that the degrees of freedom choice is inherited here from the initial mmrm fit. Furthermore, we can also obtain the differences between the treatment arms for each visit by applying pairs() on the object returned by emmeans() earlier:

pairs(lsmeans_by_visit, reverse = TRUE)
#> AVISIT = VIS1:
#>  contrast  estimate    SE  df t.ratio p.value
#>  TRT - PBO     3.77 1.074 146   3.514  0.0006
#> 
#> AVISIT = VIS2:
#>  contrast  estimate    SE  df t.ratio p.value
#>  TRT - PBO     3.73 0.859 145   4.346  <.0001
#> 
#> AVISIT = VIS3:
#>  contrast  estimate    SE  df t.ratio p.value
#>  TRT - PBO     3.08 0.690 131   4.467  <.0001
#> 
#> AVISIT = VIS4:
#>  contrast  estimate    SE  df t.ratio p.value
#>  TRT - PBO     4.40 1.681 133   2.617  0.0099
#> 
#> Results are averaged over the levels of: RACE, SEX

(This is similar like the pdiff option in SAS PROC MIXED.) Note that we use here the reverse argument to obtain treatment minus placebo results, instead of placebo minus treatment results.

Tidymodels

Tidymodels

mmrm is compatible to work in a tidymodels workflow. The following is an example of how such a workflow would be constructed.

library(tidymodels)

Direct fit

First we define the direct method to fit an mmrm model using the parsnip package functions linear_reg() and set_engine().

• linear_reg() defines a model that can predict numeric values from predictors using a linear function
• set_engine() is used to specify which package or system will be used to fit the model, along with any arguments specific to that software. We can set the method to adjust degrees of freedom directly in the call.

model <- linear_reg() |>
  set_engine("mmrm", method = "Satterthwaite") |>
  fit(FEV1 ~ RACE + ARMCD * AVISIT + us(AVISIT | USUBJID), fev_data)
model
#> parsnip model object
#> 
#> mmrm fit
#> 
#> Formula:     FEV1 ~ RACE + ARMCD * AVISIT + us(AVISIT | USUBJID)
#> Data:        data (used 537 observations from 197 subjects with maximum 4 
#> timepoints)
#> Weights:     weights
#> Covariance:  unstructured (10 variance parameters)
#> Inference:   REML
#> Deviance:    3387.373
#> 
#> Coefficients: 
#>                   (Intercept) RACEBlack or African American 
#>                   30.96769899                    1.50464863 
#>                     RACEWhite                      ARMCDTRT 
#>                    5.61309565                    3.77555734 
#>                    AVISITVIS2                    AVISITVIS3 
#>                    4.82858803                   10.33317002 
#>                    AVISITVIS4           ARMCDTRT:AVISITVIS2 
#>                   15.05255715                   -0.01737409 
#>           ARMCDTRT:AVISITVIS3           ARMCDTRT:AVISITVIS4 
#>                   -0.66753189                    0.63094392 
#> 
#> Model Inference Optimization:
#> Converged with code 0 and message: convergence: rel_reduction_of_f <= factr*epsmch

We can also pass in the full mmrm_control object into the set_engine() call:

model_with_control <- linear_reg() |>
  set_engine("mmrm", control = mmrm_control(method = "Satterthwaite")) |>
  fit(FEV1 ~ RACE + ARMCD * AVISIT + us(AVISIT | USUBJID), fev_data)

Predictions

Lastly, we can also obtain predictions with the predict() method:

predict(model, new_data = fev_data)
#> # A tibble: 800 × 1
#>    .pred
#>    <dbl>
#>  1  32.5
#>  2  40.0
#>  3  45.7
#>  4  20.5
#>  5  28.0
#>  6  31.5
#>  7  36.9
#>  8  48.8
#>  9  30.7
#> 10  36.0
#> # ℹ 790 more rows

Note that we need to explicitly pass new_data because the method definition does not allow to default it to the data set we used for the model fitting automatically.

By using the type = "numeric" default of predict() as above we cannot further customize the calculations. We obtain predicted values without confidence intervals or standard errors.

On the other hand, when using type = "raw" we can customize the calculations via the opts list:

predict(
  model,
  new_data = fev_data,
  type = "raw",
  opts = list(se.fit = TRUE, interval = "prediction", nsim = 10L)
)
#>          fit        se      lwr      upr
#> 1   32.47877  5.822427 21.06702 43.89052
#> 2   39.97105  0.000000 39.97105 39.97105
#> 3   45.70508  4.211979 37.44975 53.96040
#> 4   20.48379  0.000000 20.48379 20.48379
#> 5   28.01243  5.504787 17.22324 38.80161
#> 6   31.45522  0.000000 31.45522 31.45522
#> 7   36.87889  0.000000 36.87889 36.87889
#> 8   48.80809  0.000000 48.80809 48.80809
#> 9   30.73774  5.560856 19.83866 41.63682
#> 10  35.98699  0.000000 35.98699 35.98699
#> 11  42.64153  3.965408 34.86947 50.41359
#> 12  37.16444  0.000000 37.16444 37.16444
#> 13  33.89229  0.000000 33.89229 33.89229
#> 14  33.74637  0.000000 33.74637 33.74637
#> 15  44.04155  3.903773 36.39029 51.69280
#> 16  54.45055  0.000000 54.45055 54.45055
#> 17  32.31386  0.000000 32.31386 32.31386
#> 18  37.31982  4.476758 28.54553 46.09410
#> 19  46.79361  0.000000 46.79361 46.79361
#> 20  41.71154  0.000000 41.71154 41.71154
#> 21  31.17198  6.125008 19.16718 43.17677
#> 22  36.63341  4.929496 26.97177 46.29504
#> 23  39.02423  0.000000 39.02423 39.02423
#> 24  47.26333 10.139203 27.39086 67.13581
#> 25  31.93050  0.000000 31.93050 31.93050
#> 26  32.90947  0.000000 32.90947 32.90947
#> 27  41.27523  3.886142 33.65853 48.89193
#> 28  48.28031  0.000000 48.28031 48.28031
#> 29  32.23021  0.000000 32.23021 32.23021
#> 30  35.91080  0.000000 35.91080 35.91080
#> 31  45.54898  0.000000 45.54898 45.54898
#> 32  53.02877  0.000000 53.02877 53.02877
#> 33  47.16898  0.000000 47.16898 47.16898
#> 34  46.64287  0.000000 46.64287 46.64287
#> 35  50.84665  3.919537 43.16450 58.52880
#> 36  58.09713  0.000000 58.09713 58.09713
#> 37  33.21881  6.091066 21.28054 45.15708
#> 38  37.68412  4.921998 28.03718 47.33106
#> 39  44.97613  0.000000 44.97613 44.97613
#> 40  47.67506 10.120052 27.84012 67.50999
#> 41  44.32755  0.000000 44.32755 44.32755
#> 42  38.97813  0.000000 38.97813 38.97813
#> 43  43.72862  0.000000 43.72862 43.72862
#> 44  46.43393  0.000000 46.43393 46.43393
#> 45  40.34576  0.000000 40.34576 40.34576
#> 46  42.76568  0.000000 42.76568 42.76568
#> 47  40.11155  0.000000 40.11155 40.11155
#> 48  49.71974  9.923822 30.26941 69.17008
#> 49  41.46341  5.940691 29.81987 53.10695
#> 50  45.73510  4.846716 36.23571 55.23449
#> 51  53.31791  0.000000 53.31791 53.31791
#> 52  56.07641  0.000000 56.07641 56.07641
#> 53  32.16382  6.079309 20.24860 44.07905
#> 54  37.14256  4.918031 27.50339 46.78172
#> 55  41.90837  0.000000 41.90837 41.90837
#> 56  47.46284 10.098476 27.67019 67.25549
#> 57  27.78883  6.057902 15.91556 39.66210
#> 58  34.13887  4.878323 24.57753 43.70021
#> 59  34.65663  0.000000 34.65663 34.65663
#> 60  39.07791  0.000000 39.07791 39.07791
#> 61  31.18775  5.539476 20.33058 42.04492
#> 62  35.89612  0.000000 35.89612 35.89612
#> 63  41.31608  3.932748 33.60803 49.02412
#> 64  47.67264  0.000000 47.67264 47.67264
#> 65  22.65440  0.000000 22.65440 22.65440
#> 66  36.35488  4.647211 27.24651 45.46324
#> 67  45.20175  4.102618 37.16076 53.24273
#> 68  40.85376  0.000000 40.85376 40.85376
#> 69  32.60048  0.000000 32.60048 32.60048
#> 70  33.64329  0.000000 33.64329 33.64329
#> 71  44.00451  3.932627 36.29670 51.71231
#> 72  40.92278  0.000000 40.92278 40.92278
#> 73  32.14831  0.000000 32.14831 32.14831
#> 74  46.43604  0.000000 46.43604 46.43604
#> 75  41.34973  0.000000 41.34973 41.34973
#> 76  66.30382  0.000000 66.30382 66.30382
#> 77  42.79902  5.602172 31.81897 53.77908
#> 78  47.95358  0.000000 47.95358 47.95358
#> 79  53.97364  0.000000 53.97364 53.97364
#> 80  56.89204  9.971995 37.34729 76.43679
#> 81  46.35384  5.656315 35.26767 57.44002
#> 82  56.64544  0.000000 56.64544 56.64544
#> 83  49.70872  0.000000 49.70872 49.70872
#> 84  60.40497  0.000000 60.40497 60.40497
#> 85  45.98525  0.000000 45.98525 45.98525
#> 86  51.90911  0.000000 51.90911 51.90911
#> 87  41.50787  0.000000 41.50787 41.50787
#> 88  53.42727  0.000000 53.42727 53.42727
#> 89  23.86859  0.000000 23.86859 23.86859
#> 90  35.98563  0.000000 35.98563 35.98563
#> 91  43.60626  0.000000 43.60626 43.60626
#> 92  44.77520  9.823932 25.52064 64.02975
#> 93  29.59773  0.000000 29.59773 29.59773
#> 94  35.50688  0.000000 35.50688 35.50688
#> 95  55.42944  0.000000 55.42944 55.42944
#> 96  52.10530  0.000000 52.10530 52.10530
#> 97  31.69644  0.000000 31.69644 31.69644
#> 98  32.16159  0.000000 32.16159 32.16159
#> 99  51.04735  0.000000 51.04735 51.04735
#> 100 55.85987  0.000000 55.85987 55.85987
#> 101 49.11706  0.000000 49.11706 49.11706
#> 102 49.25544  0.000000 49.25544 49.25544
#> 103 51.72211  0.000000 51.72211 51.72211
#> 104 69.99128  0.000000 69.99128 69.99128
#> 105 22.07169  0.000000 22.07169 22.07169
#> 106 36.35845  4.689243 27.16770 45.54920
#> 107 46.08393  0.000000 46.08393 46.08393
#> 108 52.42288  0.000000 52.42288 52.42288
#> 109 37.69466  0.000000 37.69466 37.69466
#> 110 44.59400  0.000000 44.59400 44.59400
#> 111 52.08897  0.000000 52.08897 52.08897
#> 112 58.22961  0.000000 58.22961 58.22961
#> 113 37.22824  0.000000 37.22824 37.22824
#> 114 34.39863  0.000000 34.39863 34.39863
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#> 488 46.60552 10.369796 26.28109 66.92995
#> 489 47.21985  0.000000 47.21985 47.21985
#> 490 40.34525  0.000000 40.34525 40.34525
#> 491 48.29793  0.000000 48.29793 48.29793
#> 492 54.57153  9.902718 35.16256 73.98050
#> 493 36.13680  5.564932 25.22974 47.04387
#> 494 44.39634  0.000000 44.39634 44.39634
#> 495 41.71421  0.000000 41.71421 41.71421
#> 496 47.37535  0.000000 47.37535 47.37535
#> 497 42.03797  0.000000 42.03797 42.03797
#> 498 37.56100  0.000000 37.56100 37.56100
#> 499 45.11793  0.000000 45.11793 45.11793
#> 500 52.86788  9.829297 33.60281 72.13295
#> 501 34.62530  0.000000 34.62530 34.62530
#> 502 45.28206  0.000000 45.28206 45.28206
#> 503 44.51505  3.915206 36.84138 52.18871
#> 504 63.57761  0.000000 63.57761 63.57761
#> 505 35.80878  0.000000 35.80878 35.80878
#> 506 40.93038  4.477400 32.15484 49.70593
#> 507 45.85156  3.902116 38.20355 53.49957
#> 508 52.67314  0.000000 52.67314 52.67314
#> 509 35.88734  0.000000 35.88734 35.88734
#> 510 38.73222  0.000000 38.73222 38.73222
#> 511 46.70361  0.000000 46.70361 46.70361
#> 512 53.65398  0.000000 53.65398 53.65398
#> 513 36.71543  0.000000 36.71543 36.71543
#> 514 43.89170  4.527946 35.01709 52.76631
#> 515 49.56246  3.931437 41.85698 57.26793
#> 516 54.83060  9.860846 35.50370 74.15750
#> 517 37.85241  5.628394 26.82096 48.88386
#> 518 41.54317  0.000000 41.54317 41.54317
#> 519 51.67909  0.000000 51.67909 51.67909
#> 520 51.76691  9.997927 32.17133 71.36249
#> 521 27.40130  0.000000 27.40130 27.40130
#> 522 30.33517  0.000000 30.33517 30.33517
#> 523 37.73092  0.000000 37.73092 37.73092
#> 524 29.11668  0.000000 29.11668 29.11668
#> 525 30.03596  5.518054 19.22077 40.85115
#> 526 32.08830  0.000000 32.08830 32.08830
#> 527 41.66067  0.000000 41.66067 41.66067
#> 528 53.90815  0.000000 53.90815 53.90815
#> 529 34.02622  5.548124 23.15209 44.90034
#> 530 35.06937  0.000000 35.06937 35.06937
#> 531 47.17615  0.000000 47.17615 47.17615
#> 532 56.49347  0.000000 56.49347 56.49347
#> 533 34.02880  5.495593 23.25763 44.79996
#> 534 38.88006  0.000000 38.88006 38.88006
#> 535 47.54070  0.000000 47.54070 47.54070
#> 536 43.53705  0.000000 43.53705 43.53705
#> 537 31.82054  0.000000 31.82054 31.82054
#> 538 39.62816  0.000000 39.62816 39.62816
#> 539 44.95543  0.000000 44.95543 44.95543
#> 540 21.11543  0.000000 21.11543 21.11543
#> 541 34.74671  0.000000 34.74671 34.74671
#> 542 43.27308  4.511734 34.43024 52.11591
#> 543 49.29538  3.923397 41.60567 56.98510
#> 544 56.69249  0.000000 56.69249 56.69249
#> 545 22.73126  0.000000 22.73126 22.73126
#> 546 32.50075  0.000000 32.50075 32.50075
#> 547 42.37206  0.000000 42.37206 42.37206
#> 548 42.89847  0.000000 42.89847 42.89847
#> 549 55.62582  0.000000 55.62582 55.62582
#> 550 45.38998  0.000000 45.38998 45.38998
#> 551 52.66743  0.000000 52.66743 52.66743
#> 552 56.87348 10.392894 36.50379 77.24318
#> 553 30.66032  6.211368 18.48626 42.83438
#> 554 37.44228  4.924304 27.79082 47.09374
#> 555 34.18931  0.000000 34.18931 34.18931
#> 556 45.59740  0.000000 45.59740 45.59740
#> 557 28.89198  0.000000 28.89198 28.89198
#> 558 38.46147  0.000000 38.46147 38.46147
#> 559 42.42099  3.890524 34.79570 50.04627
#> 560 49.90357  0.000000 49.90357 49.90357
#> 561 39.74586  5.540149 28.88737 50.60435
#> 562 44.14167  0.000000 44.14167 44.14167
#> 563 49.91712  3.944239 42.18656 57.64769
#> 564 55.24278  0.000000 55.24278 55.24278
#> 565 36.24790  6.201742 24.09271 48.40310
#> 566 41.05912  4.948930 31.35939 50.75884
#> 567 45.91354  3.996462 38.08062 53.74646
#> 568 51.93141 10.178892 31.98114 71.88167
#> 569 27.38001  0.000000 27.38001 27.38001
#> 570 33.63251  0.000000 33.63251 33.63251
#> 571 44.70168  4.044794 36.77403 52.62933
#> 572 39.34410  0.000000 39.34410 39.34410
#> 573 26.98575  0.000000 26.98575 26.98575
#> 574 24.04175  0.000000 24.04175 24.04175
#> 575 42.16648  0.000000 42.16648 42.16648
#> 576 44.75380  0.000000 44.75380 44.75380
#> 577 31.55469  0.000000 31.55469 31.55469
#> 578 44.42696  0.000000 44.42696 44.42696
#> 579 44.10343  0.000000 44.10343 44.10343
#> 580 48.06505  9.850282 28.75886 67.37125
#> 581 34.87547  5.505592 24.08471 45.66623
#> 582 37.87445  0.000000 37.87445 37.87445
#> 583 48.31828  0.000000 48.31828 48.31828
#> 584 50.21520  0.000000 50.21520 50.21520
#> 585 41.94615  0.000000 41.94615 41.94615
#> 586 39.62690  0.000000 39.62690 39.62690
#> 587 46.69763  0.000000 46.69763 46.69763
#> 588 49.44653  9.939626 29.96522 68.92784
#> 589 38.01775  5.578402 27.08428 48.95121
#> 590 43.75255  0.000000 43.75255 43.75255
#> 591 47.38873  0.000000 47.38873 47.38873
#> 592 52.70780  9.931874 33.24168 72.17391
#> 593 32.43412  0.000000 32.43412 32.43412
#> 594 43.07163  0.000000 43.07163 43.07163
#> 595 42.99551  0.000000 42.99551 42.99551
#> 596 53.82759  0.000000 53.82759 53.82759
#> 597 39.45747  5.996935 27.70369 51.21124
#> 598 42.93167  4.873176 33.38042 52.48292
#> 599 50.64802  0.000000 50.64802 50.64802
#> 600 63.44051  0.000000 63.44051 63.44051
#> 601 34.48949  0.000000 34.48949 34.48949
#> 602 40.08056  0.000000 40.08056 40.08056
#> 603 41.86656  3.893079 34.23626 49.49685
#> 604 47.46553  0.000000 47.46553 47.46553
#> 605 32.03992  5.591766 21.08026 42.99958
#> 606 37.11697  0.000000 37.11697 37.11697
#> 607 44.12071  4.006402 36.26830 51.97311
#> 608 36.25120  0.000000 36.25120 36.25120
#> 609 29.20171  0.000000 29.20171 29.20171
#> 610 31.53773  0.000000 31.53773 31.53773
#> 611 42.35683  0.000000 42.35683 42.35683
#> 612 64.78352  0.000000 64.78352 64.78352
#> 613 32.72757  0.000000 32.72757 32.72757
#> 614 37.50022  0.000000 37.50022 37.50022
#> 615 42.76167  3.895883 35.12588 50.39746
#> 616 57.03861  0.000000 57.03861 57.03861
#> 617 36.32475  0.000000 36.32475 36.32475
#> 618 40.15241  4.478209 31.37528 48.92954
#> 619 41.46725  0.000000 41.46725 41.46725
#> 620 59.01411  0.000000 59.01411 59.01411
#> 621 30.14970  0.000000 30.14970 30.14970
#> 622 34.91740  0.000000 34.91740 34.91740
#> 623 52.13900  0.000000 52.13900 52.13900
#> 624 58.73839  0.000000 58.73839 58.73839
#> 625 35.83185  0.000000 35.83185 35.83185
#> 626 41.04423  4.480351 32.26290 49.82556
#> 627 45.82688  3.902893 38.17735 53.47641
#> 628 56.41409  0.000000 56.41409 56.41409
#> 629 37.80184  5.482578 27.05618 48.54749
#> 630 43.55593  0.000000 43.55593 43.55593
#> 631 44.26320  0.000000 44.26320 44.26320
#> 632 59.25579  0.000000 59.25579 59.25579
#> 633 28.47314  0.000000 28.47314 28.47314
#> 634 47.47581  0.000000 47.47581 47.47581
#> 635 44.01685  3.962468 36.25056 51.78314
#> 636 49.57489 10.027322 29.92170 69.22808
#> 637 39.38085  5.573609 28.45678 50.30492
#> 638 46.47483  0.000000 46.47483 46.47483
#> 639 51.22677  0.000000 51.22677 51.22677
#> 640 45.82777  0.000000 45.82777 45.82777
#> 641 33.43408  5.661230 22.33827 44.52989
#> 642 39.06783  0.000000 39.06783 39.06783
#> 643 42.98333  3.960309 35.22127 50.74540
#> 644 48.01822  9.980887 28.45604 67.58040
#> 645 29.99542  0.000000 29.99542 29.99542
#> 646 35.69583  4.487944 26.89962 44.49204
#> 647 41.11547  3.898811 33.47394 48.75700
#> 648 54.17796  0.000000 54.17796 54.17796
#> 649 39.32289  5.566546 28.41266 50.23312
#> 650 44.55743  0.000000 44.55743 44.55743
#> 651 47.26282  3.970986 39.47983 55.04581
#> 652 62.59579  0.000000 62.59579 62.59579
#> 653 31.80300  5.472832 21.07645 42.52956
#> 654 35.48396  0.000000 35.48396 35.48396
#> 655 44.07768  0.000000 44.07768 44.07768
#> 656 46.57837  0.000000 46.57837 46.57837
#> 657 47.67979  0.000000 47.67979 47.67979
#> 658 47.73388  4.545164 38.82553 56.64224
#> 659 50.94631  3.953061 43.19845 58.69417
#> 660 58.47218  9.941894 38.98642 77.95793
#> 661 22.15439  0.000000 22.15439 22.15439
#> 662 35.14301  4.650458 26.02828 44.25774
#> 663 42.82000  4.070531 34.84191 50.79810
#> 664 46.24563 10.028955 26.58924 65.90203
#> 665 34.27765  0.000000 34.27765 34.27765
#> 666 36.90059  0.000000 36.90059 36.90059
#> 667 43.05627  3.890191 35.43163 50.68090
#> 668 40.54285  0.000000 40.54285 40.54285
#> 669 29.09494  0.000000 29.09494 29.09494
#> 670 37.21768  0.000000 37.21768 37.21768
#> 671 43.08491  0.000000 43.08491 43.08491
#> 672 46.50100  9.739577 27.41178 65.59022
#> 673 27.12174  0.000000 27.12174 27.12174
#> 674 34.11916  0.000000 34.11916 34.11916
#> 675 45.56320  4.018395 37.68730 53.43911
#> 676 48.00823  9.943762 28.51881 67.49764
#> 677 35.93048  5.541162 25.07000 46.79096
#> 678 40.80230  0.000000 40.80230 40.80230
#> 679 45.89269  0.000000 45.89269 45.89269
#> 680 43.69153  0.000000 43.69153 43.69153
#> 681 28.56569  5.661555 17.46925 39.66214
#> 682 29.22869  0.000000 29.22869 29.22869
#> 683 40.67646  3.973858 32.88784 48.46508
#> 684 55.68362  0.000000 55.68362 55.68362
#> 685 31.90698  0.000000 31.90698 31.90698
#> 686 37.31061  0.000000 37.31061 37.31061
#> 687 40.75546  0.000000 40.75546 40.75546
#> 688 49.50911  9.757100 30.38554 68.63267
#> 689 42.19474  0.000000 42.19474 42.19474
#> 690 44.87228  0.000000 44.87228 44.87228
#> 691 47.55198  0.000000 47.55198 47.55198
#> 692 56.68097  9.751287 37.56880 75.79314
#> 693 50.62894  0.000000 50.62894 50.62894
#> 694 45.47551  0.000000 45.47551 45.47551
#> 695 48.62168  0.000000 48.62168 48.62168
#> 696 56.58212 10.029555 36.92455 76.23969
#> 697 29.66493  0.000000 29.66493 29.66493
#> 698 34.57406  0.000000 34.57406 34.57406
#> 699 42.45295  3.918861 34.77212 50.13378
#> 700 38.11676  0.000000 38.11676 38.11676
#> 701 33.77204  0.000000 33.77204 33.77204
#> 702 34.26148  0.000000 34.26148 34.26148
#> 703 45.29511  3.923948 37.60431 52.98590
#> 704 58.81037  0.000000 58.81037 58.81037
#> 705 31.46668  6.105731 19.49967 43.43370
#> 706 36.78469  4.924556 27.13274 46.43664
#> 707 39.88119  0.000000 39.88119 39.88119
#> 708 47.32261 10.120950 27.48592 67.15931
#> 709 31.62708  0.000000 31.62708 31.62708
#> 710 37.03239  4.475086 28.26138 45.80340
#> 711 42.69162  3.888706 35.06989 50.31334
#> 712 48.22049  0.000000 48.22049 48.22049
#> 713 42.58829  0.000000 42.58829 42.58829
#> 714 45.80410  4.475682 37.03192 54.57628
#> 715 49.33262  0.000000 49.33262 49.33262
#> 716 53.74331  0.000000 53.74331 53.74331
#> 717 29.71857  0.000000 29.71857 29.71857
#> 718 30.45651  0.000000 30.45651 30.45651
#> 719 38.29800  0.000000 38.29800 38.29800
#> 720 45.15328  9.792962 25.95943 64.34713
#> 721 36.81040  0.000000 36.81040 36.81040
#> 722 37.61606  4.509721 28.77717 46.45495
#> 723 42.35045  0.000000 42.35045 42.35045
#> 724 39.39860  0.000000 39.39860 39.39860
#> 725 36.09876  5.961678 24.41408 47.78343
#> 726 40.94066  4.865213 31.40501 50.47630
#> 727 49.73629  0.000000 49.73629 49.73629
#> 728 41.58082  0.000000 41.58082 41.58082
#> 729 43.58901  0.000000 43.58901 43.58901
#> 730 40.16762  0.000000 40.16762 40.16762
#> 731 46.70338  3.947929 38.96559 54.44118
#> 732 53.94830  9.871721 34.60009 73.29652
#> 733 39.60913  5.628551 28.57737 50.64088
#> 734 41.08206  0.000000 41.08206 41.08206
#> 735 49.65683  3.980301 41.85559 57.45808
#> 736 69.37409  0.000000 69.37409 69.37409
#> 737 34.12096  5.504849 23.33166 44.91027
#> 738 41.27625  0.000000 41.27625 41.27625
#> 739 44.76138  0.000000 44.76138 44.76138
#> 740 39.69815  0.000000 39.69815 39.69815
#> 741 38.44296  0.000000 38.44296 38.44296
#> 742 48.20586  0.000000 48.20586 48.20586
#> 743 47.54082  3.963976 39.77157 55.31007
#> 744 35.50735  0.000000 35.50735 35.50735
#> 745 32.08153  0.000000 32.08153 32.08153
#> 746 37.16398  4.510170 28.32421 46.00375
#> 747 42.75619  3.906030 35.10051 50.41187
#> 748 47.39510  9.851797 28.08593 66.70427
#> 749 44.69256  0.000000 44.69256 44.69256
#> 750 41.45664  4.640437 32.36156 50.55173
#> 751 42.18689  0.000000 42.18689 42.18689
#> 752 51.68534 10.000564 32.08459 71.28608
#> 753 37.01741  0.000000 37.01741 37.01741
#> 754 38.26920  0.000000 38.26920 38.26920
#> 755 49.28806  0.000000 49.28806 49.28806
#> 756 50.67485  9.799485 31.46822 69.88149
#> 757 40.45953  0.000000 40.45953 40.45953
#> 758 45.10337  0.000000 45.10337 45.10337
#> 759 45.58250  0.000000 45.58250 45.58250
#> 760 62.96989  0.000000 62.96989 62.96989
#> 761 30.78252  0.000000 30.78252 30.78252
#> 762 41.58139  4.579637 32.60546 50.55731
#> 763 48.87398  4.028942 40.97739 56.77056
#> 764 44.69667  0.000000 44.69667 44.69667
#> 765 32.72491  0.000000 32.72491 32.72491
#> 766 45.78702  0.000000 45.78702 45.78702
#> 767 48.74886  0.000000 48.74886 48.74886
#> 768 84.08449  0.000000 84.08449 84.08449
#> 769 28.60809  5.573029 17.68515 39.53103
#> 770 30.19495  0.000000 30.19495 30.19495
#> 771 36.78573  0.000000 36.78573 36.78573
#> 772 61.03588  0.000000 61.03588 61.03588
#> 773 20.36749  0.000000 20.36749 20.36749
#> 774 35.22480  0.000000 35.22480 35.22480
#> 775 37.42847  0.000000 37.42847 37.42847
#> 776 30.20501  0.000000 30.20501 30.20501
#> 777 41.72819  5.637841 30.67823 52.77816
#> 778 49.12862  0.000000 49.12862 49.12862
#> 779 47.31234  0.000000 47.31234 47.31234
#> 780 57.08286  9.993472 37.49601 76.66970
#> 781 19.28388  0.000000 19.28388 19.28388
#> 782 30.00682  0.000000 30.00682 30.00682
#> 783 39.69711  3.999962 31.85733 47.53690
#> 784 49.21768  0.000000 49.21768 49.21768
#> 785 31.42637  6.014824 19.63753 43.21521
#> 786 36.73485  4.860542 27.20836 46.26133
#> 787 42.72556  3.966563 34.95124 50.49988
#> 788 40.13353  0.000000 40.13353 40.13353
#> 789 42.34534  0.000000 42.34534 42.34534
#> 790 52.32575  0.000000 52.32575 52.32575
#> 791 46.92223  4.017040 39.04898 54.79549
#> 792 69.26254  0.000000 69.26254 69.26254
#> 793 40.35635  6.201982 28.20069 52.51201
#> 794 45.16757  4.949405 35.46691 54.86822
#> 795 50.02199  3.991961 42.19789 57.84609
#> 796 56.03985 10.178516 36.09033 75.98938
#> 797 35.70341  0.000000 35.70341 35.70341
#> 798 41.64454  0.000000 41.64454 41.64454
#> 799 43.29513  3.902234 35.64689 50.94337
#> 800 54.25081  0.000000 54.25081 54.25081

The result is now a matrix, because that is what the predict() method returns for mmrm objects. Note that this cannot be changed to return a tibble at the moment.

Similarly, we can also use the augment() method to add predicted values to a new data set:

augment(model, new_data = fev_data) |>
  select(USUBJID, AVISIT, .resid, .pred)
#> # A tibble: 800 × 4
#>    USUBJID AVISIT .resid .pred
#>    <fct>   <fct>   <dbl> <dbl>
#>  1 PT1     VIS1       NA  32.5
#>  2 PT1     VIS2        0  40.0
#>  3 PT1     VIS3       NA  45.7
#>  4 PT1     VIS4        0  20.5
#>  5 PT2     VIS1       NA  28.0
#>  6 PT2     VIS2        0  31.5
#>  7 PT2     VIS3        0  36.9
#>  8 PT2     VIS4        0  48.8
#>  9 PT3     VIS1       NA  30.7
#> 10 PT3     VIS2        0  36.0
#> # ℹ 790 more rows

Note that here we cannot customize the predict options as this is currently not supported by the augment() method in parsnip.

Using mmrm in workflows

We can leverage the workflows package in order to fit the same model.

• First we define the specification for linear regression with the mmrm engine.
• Second we define the workflow, by defining the outcome and predictors that will be used in the formula. We then add the model using the formula.
• Lastly, we fit the model

mmrm_spec <- linear_reg() |>
  set_engine("mmrm", method = "Satterthwaite")

mmrm_wflow <- workflow() |>
  add_variables(outcomes = FEV1, predictors = c(RACE, ARMCD, AVISIT, USUBJID)) |>
  add_model(mmrm_spec, formula = FEV1 ~ RACE + ARMCD * AVISIT + us(AVISIT | USUBJID))

mmrm_wflow |>
  fit(data = fev_data)
#> ══ Workflow [trained] ══════════════════════════════════════════════════════════
#> Preprocessor: Variables
#> Model: linear_reg()
#> 
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> Outcomes: FEV1
#> Predictors: c(RACE, ARMCD, AVISIT, USUBJID)
#> 
#> ── Model ───────────────────────────────────────────────────────────────────────
#> mmrm fit
#> 
#> Formula:     FEV1 ~ RACE + ARMCD * AVISIT + us(AVISIT | USUBJID)
#> Data:        data (used 537 observations from 197 subjects with maximum 4 
#> timepoints)
#> Weights:     weights
#> Covariance:  unstructured (10 variance parameters)
#> Inference:   REML
#> Deviance:    3387.373
#> 
#> Coefficients: 
#>                   (Intercept) RACEBlack or African American 
#>                   30.96769899                    1.50464863 
#>                     RACEWhite                      ARMCDTRT 
#>                    5.61309565                    3.77555734 
#>                    AVISITVIS2                    AVISITVIS3 
#>                    4.82858803                   10.33317002 
#>                    AVISITVIS4           ARMCDTRT:AVISITVIS2 
#>                   15.05255715                   -0.01737409 
#>           ARMCDTRT:AVISITVIS3           ARMCDTRT:AVISITVIS4 
#>                   -0.66753189                    0.63094392 
#> 
#> Model Inference Optimization:
#> Converged with code 0 and message: convergence: rel_reduction_of_f <= factr*epsmch

We can separate out the data preparation step from the modeling step using the recipes package. Here we are converting the ARMCD variable into a dummy variable and creating an interaction term with the new dummy variable and each visit.

mmrm_recipe <- recipe(FEV1 ~ ., data = fev_data) |>
  step_dummy(ARMCD) |>
  step_interact(terms = ~ starts_with("ARMCD"):AVISIT)

Using prep() and juice() we can see what the transformed data that will be used in the model fit looks like.

mmrm_recipe |>
  prep() |>
  juice()
#> # A tibble: 800 × 13
#>    USUBJID AVISIT RACE       SEX   FEV1_BL WEIGHT VISITN VISITN2  FEV1 ARMCD_TRT
#>    <fct>   <fct>  <fct>      <fct>   <dbl>  <dbl>  <int>   <dbl> <dbl>     <dbl>
#>  1 PT1     VIS1   Black or … Fema…    25.3  0.677      1  -0.626  NA           1
#>  2 PT1     VIS2   Black or … Fema…    25.3  0.801      2   0.184  40.0         1
#>  3 PT1     VIS3   Black or … Fema…    25.3  0.709      3  -0.836  NA           1
#>  4 PT1     VIS4   Black or … Fema…    25.3  0.809      4   1.60   20.5         1
#>  5 PT2     VIS1   Asian      Male     45.0  0.465      1   0.330  NA           0
#>  6 PT2     VIS2   Asian      Male     45.0  0.233      2  -0.820  31.5         0
#>  7 PT2     VIS3   Asian      Male     45.0  0.360      3   0.487  36.9         0
#>  8 PT2     VIS4   Asian      Male     45.0  0.507      4   0.738  48.8         0
#>  9 PT3     VIS1   Black or … Fema…    43.5  0.682      1   0.576  NA           0
#> 10 PT3     VIS2   Black or … Fema…    43.5  0.892      2  -0.305  36.0         0
#> # ℹ 790 more rows
#> # ℹ 3 more variables: ARMCD_TRT_x_AVISITVIS2 <dbl>,
#> #   ARMCD_TRT_x_AVISITVIS3 <dbl>, ARMCD_TRT_x_AVISITVIS4 <dbl>

We can pass the covariance structure as well in the set_engine() definition. This allows for more flexibility on presetting different covariance structures in the pipeline while keeping the data preparation step independent.

mmrm_spec_with_cov <- linear_reg() |>
  set_engine(
    "mmrm",
    method = "Satterthwaite",
    covariance = as.cov_struct(~ us(AVISIT | USUBJID))
  )

We combine these steps into a workflow:

(mmrm_wflow_nocov <- workflow() |>
  add_model(mmrm_spec_with_cov, formula = FEV1 ~ SEX) |>
  add_recipe(mmrm_recipe))
#> ══ Workflow ════════════════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: linear_reg()
#> 
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 2 Recipe Steps
#> 
#> • step_dummy()
#> • step_interact()
#> 
#> ── Model ───────────────────────────────────────────────────────────────────────
#> Linear Regression Model Specification (regression)
#> 
#> Engine-Specific Arguments:
#>   method = Satterthwaite
#>   covariance = as.cov_struct(~us(AVISIT | USUBJID))
#> 
#> Computational engine: mmrm

Last step is to fit the data with the workflow object

(fit_tidy <- fit(mmrm_wflow_nocov, data = fev_data))
#> ══ Workflow [trained] ══════════════════════════════════════════════════════════
#> Preprocessor: Recipe
#> Model: linear_reg()
#> 
#> ── Preprocessor ────────────────────────────────────────────────────────────────
#> 2 Recipe Steps
#> 
#> • step_dummy()
#> • step_interact()
#> 
#> ── Model ───────────────────────────────────────────────────────────────────────
#> mmrm fit
#> 
#> Formula:     FEV1 ~ SEX
#> Data:        data (used 537 observations from 197 subjects with maximum 4 
#> timepoints)
#> Weights:     weights
#> Covariance:  unstructured (10 variance parameters)
#> Inference:   REML
#> Deviance:    3699.803
#> 
#> Coefficients: 
#> (Intercept)   SEXFemale 
#> 42.80540973  0.04513432 
#> 
#> Model Inference Optimization:
#> Converged with code 0 and message: convergence: rel_reduction_of_f <= factr*epsmch

To retrieve the fit object from within the workflow object run the following

fit_tidy |>
  hardhat::extract_fit_engine()
#> mmrm fit
#> 
#> Formula:     FEV1 ~ SEX
#> Data:        data (used 537 observations from 197 subjects with maximum 4 
#> timepoints)
#> Weights:     weights
#> Covariance:  unstructured (10 variance parameters)
#> Inference:   REML
#> Deviance:    3699.803
#> 
#> Coefficients: 
#> (Intercept)   SEXFemale 
#> 42.80540973  0.04513432 
#> 
#> Model Inference Optimization:
#> Converged with code 0 and message: convergence: rel_reduction_of_f <= factr*epsmch

Acknowledgments

The mmrm package is based on previous work internal in Roche, namely the tern and tern.mmrm packages which were based on lme4. The work done in the rbmi package has been important since it used glmmTMB for fitting MMRMs.

We would like to thank Ben Bolker from the glmmTMB team for multiple discussions when we tried to get the Satterthwaite degrees of freedom implemented with glmmTMB (see https://github.com/glmmTMB/glmmTMB/blob/satterthwaite_df/glmmTMB/vignettes/satterthwaite_unstructured_example2.Rmd). Also Ben helped us significantly with an example showing how to use TMB for a random effect vector (https://github.com/bbolker/tmb-case-studies/tree/master/vectorMixed).

References