Sensitivity analysis for clinical trials with missing continuous outcome data using controlled multiple imputation: A practical guide.
Stat Med
; 39(21): 2815-2842, 2020 09 20.
Article
in En
| MEDLINE
| ID: mdl-32419182
ABSTRACT
Missing data due to loss to follow-up or intercurrent events are unintended, but unfortunately inevitable in clinical trials. Since the true values of missing data are never known, it is necessary to assess the impact of untestable and unavoidable assumptions about any unobserved data in sensitivity analysis. This tutorial provides an overview of controlled multiple imputation (MI) techniques and a practical guide to their use for sensitivity analysis of trials with missing continuous outcome data. These include δ- and reference-based MI procedures. In δ-based imputation, an offset term, δ, is typically added to the expected value of the missing data to assess the impact of unobserved participants having a worse or better response than those observed. Reference-based imputation draws imputed values with some reference to observed data in other groups of the trial, typically in other treatment arms. We illustrate the accessibility of these methods using data from a pediatric eczema trial and a chronic headache trial and provide Stata code to facilitate adoption. We discuss issues surrounding the choice of δ in δ-based sensitivity analysis. We also review the debate on variance estimation within reference-based analysis and justify the use of Rubin's variance estimator in this setting, since as we further elaborate on within, it provides information anchored inference.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Data Interpretation, Statistical
Limits:
Child
/
Humans
Language:
En
Journal:
Stat Med
Year:
2020
Type:
Article
Affiliation country:
United kingdom