Sensitivity analysis of incomplete longitudinal data departing from the missing at random assumption: Methodology and application in a clinical trial with drop-outs
M Moreno-Betancur, M Chavance
STATISTICAL METHODS IN MEDICAL RESEARCH | SAGE PUBLICATIONS LTD | Published : 2016
Statistical analyses of longitudinal data with drop-outs based on direct likelihood, and using all the available data, provide unbiased and fully efficient estimates under some assumptions about the drop-out mechanism. Unfortunately, these assumptions can never be tested from the data. Thus, sensitivity analyses should be routinely performed to assess the robustness of inferences to departures from these assumptions. However, each specific scientific context requires different considerations when setting up such an analysis, no standard method exists and this is still an active area of research. We propose a flexible procedure to perform sensitivity analyses when dealing with continuous outc..View full abstract
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Moreno-Betancur's research was supported by a doctoral grant from Universite Paris-Sud. Dr. Chavance's research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.