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MMRM Analysis

Source: R/fit_mmrm.R
fit_mmrm.Rd

[Stable]

Does the MMRM analysis. Multiple other functions can be called on the result to produce tables and graphs.

Usage

fit_mmrm(
  vars = list(response = "AVAL", covariates = c(), id = "USUBJID", arm = "ARM", visit =
    "AVISIT"),
  data,
  conf_level = 0.95,
  cor_struct = "unstructured",
  weights_emmeans = "proportional",
  averages_emmeans = list(),
  parallel = FALSE,
  ...
)

Arguments

vars

(named list of string or character)
specifying the variables in the MMRM. The following elements need to be included as character vectors and match corresponding columns in data:

  • response: the response variable.

  • covariates: the additional covariate terms (might also include interactions).

  • id: the subject ID variable.

  • arm: the treatment group variable (factor).

  • visit: the visit variable (factor).

  • weights: optional weights variable (if NULL or omitted then no weights will be used).

Note that the main effects and interaction of arm and visit are by default included in the model.

data

(data.frame)
with all the variables specified in vars. Records with missing values in any independent variables will be excluded.

conf_level

(proportion)
confidence level of the interval.

cor_struct

(string)
specifying the covariance structure, defaults to "unstructured". See the details.

weights_emmeans

(string)
argument from emmeans::emmeans(), "proportional" by default.

averages_emmeans

(list)
optional named list of visit levels which should be averaged and reported along side the single visits.

parallel

(flag)
controls whether the optimizer search can use available free cores on the machine (not default).

...

additional arguments for mmrm::mmrm(), in particular reml and options listed in mmrm::mmrm_control().

Value

A tern_mmrm object which is a list with MMRM results:

  • fit: The mmrm object which was fitted to the data. Note that via mmrm::component(fit, "optimizer") the finally used optimization algorithm can be obtained, which can be useful for refitting the model later on.

  • cov_estimate: The matrix with the covariance matrix estimate.

  • diagnostics: A list with model diagnostic statistics (REML criterion, AIC, corrected AIC, BIC).

  • lsmeans: This is a list with data frames estimates and contrasts. The attributes averages and weights save the settings used (averages_emmeans and weights_emmeans).

  • vars: The variable list.

  • labels: Corresponding list with variable labels extracted from data.

  • cor_struct: input.

  • parallel: input.

  • ref_level: The reference level for the arm variable, which is always the first level.

  • treatment_levels: The treatment levels for the arm variable.

  • conf_level: The confidence level which was used to construct the lsmeans confidence intervals.

  • additional: List with any additional inputs passed via ...

Details

Multiple different degree of freedom adjustments are available via the method argument for mmrm::mmrm(). In addition, covariance matrix adjustments are available via vcov. Please see mmrm::mmrm_control() for details and additional useful options.

For the covariance structure (cor_struct), the user can choose among the following options.

  • unstructured: Unstructured covariance matrix. This is the most flexible choice and default. If there are T visits, then T * (T+1) / 2 variance parameters are used.

  • toeplitz: Homogeneous Toeplitz covariance matrix, which uses T variance parameters.

  • heterogeneous toeplitz: Heterogeneous Toeplitz covariance matrix, which uses 2 * T - 1 variance parameters.

  • ante-dependence: Homogeneous Ante-Dependence covariance matrix, which uses T variance parameters.

  • heterogeneous ante-dependence: Heterogeneous Ante-Dependence covariance matrix, which uses 2 * T - 1 variance parameters.

  • auto-regressive: Homogeneous Auto-Regressive (order 1) covariance matrix, which uses 2 variance parameters.

  • heterogeneous auto-regressive: Heterogeneous Auto-Regressive (order 1) covariance matrix, which uses T + 1 variance parameters.

  • compound symmetry: Homogeneous Compound Symmetry covariance matrix, which uses 2 variance parameters.

  • heterogeneous compound symmetry: Heterogeneous Compound Symmetry covariance matrix, which uses T + 1 variance parameters.

Examples

library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following object is masked from ‘package:testthat’:
#> 
#>     matches
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
library(rtables)

mmrm_results <- fit_mmrm(
  vars = list(
    response = "FEV1",
    covariates = c("RACE", "SEX"),
    id = "USUBJID",
    arm = "ARMCD",
    visit = "AVISIT"
  ),
  data = mmrm_test_data,
  cor_struct = "unstructured",
  weights_emmeans = "equal",
  averages_emmeans = list(
    "VIS1+2" = c("VIS1", "VIS2")
  )
)