Sensitivity Analyses in Empirical Studies Plagued with Missing Data
Contributors: Rubin, Donald B.; Rubin, Donald B.; Campbell, Gregory; Pillai, Natesh
Published: 2013
Language: eng
Description: Analyses of data with missing values often require assumptions about missingness mechanisms that cannot be assessed empirically, highlighting the need for sensitivity analyses. However, universal recommendations for reporting missing data and conducting sensitivity analyses in empirical studies are scarce. Both steps are often neglected by practitioners due to the lack of clear guidelines for summarizing missing data and systematic explorations of alternative assumptions, as well as the typical attendant complexity of missing not at random (MNAR) models.
Notes: Keywords: clinical trial, graphical sensitivity analysis, missing not at random, multiple imputation, principle stratification, tipping-point analysis.

Thesis Ph.D. Harvard University 2013