Skip to contents

Analyse an estimand under multiple imputation for dropout

Source: R/imputation-pool.R
analyseEstimand.Rd

End-to-end analysis of a declared defineEstimand estimand: multiply-impute the dropout-missing longitudinal response (in wide form, so the within-subject correlation is respected), fit an MMRM (fitMMRM) to each completed data set, and pool the fixed effects with Rubin's rules (poolEstimates()). Treatment-policy and hypothetical strategies use the standard MAR imputation; other strategies warn that they need a bespoke imputation model.

Usage

analyseEstimand(
  data,
  estimand,
  response,
  treatment,
  time,
  subject,
  covariates = NULL,
  m = 20,
  method = "norm",
  seed = NULL,
  covariance = "unstructured"
)

Arguments

data

Long-format data (subject x time rows) with the response, treatment, time, subject, and any (subject-level) covariates.

estimand

An "estimand" from defineEstimand().

response, treatment, time, subject

Column names.

covariates

Optional subject-level covariate column names.

m

Number of imputations (default 20).

method

mice method (default "norm", appropriate for a continuous longitudinal response).

seed

Optional integer seed.

covariance

MMRM covariance structure (default "unstructured").

Value

An AnalysisResult (type "estimand_analysis") carrying the estimand attributes and the pooled fixed-effect table.

References

ICH E9(R1); Rubin 1987; Mallinckrodt 2008 (MMRM).

See also

defineEstimand(), multipleImputation(), poolEstimates()