Reference-based sensitivity analysis via multiple imputation for longitudinal trials with protocol deviation
Suzie Cro
MRC Clinical Trials Unit at UCL
London School of Hygiene and Tropical Medicine
London, UK
[email protected]
|
Tim P. Morris
MRC Clinical Trials Unit at UCL
London School of Hygiene and Tropical Medicine
London, UK
[email protected]
|
Michael G. Kenward
London School of Hygiene and Tropical Medicine
London, UK
[email protected]
|
James R. Carpenter
MRC Clinical Trials Unit at UCL
London School of Hygiene and Tropical Medicine
London, UK
[email protected]
|
Abstract. Randomized controlled trials provide essential evidence for the evaluation of
new and existing medical treatments. Unfortunately, the statistical analysis is
often complicated by the occurrence of protocol deviations, which mean we
cannot always measure the intended outcomes for individuals who deviate,
resulting in a missing-data problem. In such settings, however one approaches
the analysis, an untestable assumption about the distribution of the unobserved
data must be made. To understand how far the results depend on these
assumptions, the primary analysis should be supplemented by a range of
sensitivity analyses, which explore how the conclusions vary over a range of
different credible assumptions for the missing data. In this article, we
describe a new command, mimix, that can be used to perform
reference-based sensitivity analyses for randomized controlled trials with
longitudinal quantitative outcome data, using the approach proposed by
Carpenter, Roger, and Kenward (2013, Journal of Biopharmaceutical
Statistics 23: 1352–1371). Under this approach, we make qualitative
assumptions about how individuals’ missing outcomes relate to those observed in
relevant groups in the trial, based on plausible clinical scenarios.
Statistical analysis then proceeds using the method of multiple imputation.
View all articles by these authors:
Suzie Cro, Tim P. Morris, Michael G. Kenward, James R. Carpenter
View all articles with these keywords:
mimix, clinical trial, protocol deviation, missing data, multiple imputation, sensitivity analysis
Download citation: BibTeX RIS
Download citation and abstract: BibTeX RIS
|
