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Fit a mixed model for repeated measures (MMRM)

Source: R/models-mmrm.R
fitMMRM.Rd

Fits the Mallinckrodt MMRM for a longitudinal randomised trial: fixed effects for treatment, categorical time and their interaction (plus any covariates), with a within-subject covariance over the repeated time points. Uses the mmrm package when available - with Kenward-Roger or Satterthwaite adjusted degrees of freedom - and falls back to nlme::gls otherwise. The fallback reproduces the mmrm fixed effects for the unstructured covariance (its primary use) and approximates the homogeneous cs/ar1/toeplitz structures; it reports between-within (containment) degrees of freedom and emits a message, since Kenward-Roger / Satterthwaite df need mmrm.

Usage

fitMMRM(
  data,
  response,
  treatment,
  time,
  subject,
  covariates = NULL,
  covariance = c("unstructured", "ar1", "compound-symmetry", "toeplitz"),
  df = c("kenward-roger", "satterthwaite")
)

Arguments

data

A long-format data.frame with one row per subject-time.

response, treatment, time, subject

Column names of the outcome, the treatment factor, the categorical time factor and the subject id.

covariates

Optional character vector of extra fixed-effect covariate columns (e.g. baseline value, stratifiers).

covariance

Within-subject covariance: "unstructured" (default), "ar1", "compound-symmetry" or "toeplitz".

df

Denominator-df method: "kenward-roger" (default) or "satterthwaite" (used by the mmrm path only).

Value

An AnalysisResult (type = "mmrm") whose estimate is the fixed-effect coefficient vector, with result$coefficients (estimate, SE, df, t, p), result$backend ("mmrm" or "gls"), result$fit and result$formula.

References

Mallinckrodt CH et al. (2008). Recommendations for the primary analysis of continuous endpoints in longitudinal clinical trials. Drug Information Journal, 42. Sabanes Bove D et al. (2023). mmrm: Mixed Models for Repeated Measures. R package.

See also

fitMixedModel(), estimatedMarginalMeans()

Examples

if (requireNamespace("mmrm", quietly = TRUE)) {
  data(fev_data, package = "mmrm")
  fitMMRM(fev_data, "FEV1", "ARMCD", "AVISIT", "USUBJID",
          covariates = c("RACE", "SEX"))
}
#> <AnalysisResult> mmrm 
#>   estimate: 30.77747548,  1.53049977,  5.64356535,  0.32606192,  3.77423004,  4.83958845, 10.34211288, 15.05389826, -0.04192625, -0.69368537,  0.62422703 
#>   method: MMRM (unstructured, kenward-roger df, mmrm) 
#>   fields: coefficients, backend, fit, formula, covariance 
#>   provenance: 1 entr(ies)