A sensitivity analysis approach for informative dropout using shared parameter models

• Published in: Biometrics Vol. 75; no. 3; pp. 917 - 926
• Main Authors: Su, Li, Li, Qiuju, Barrett, Jessica K., Daniels, Michael J.
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.09.2019; Blackwell Publishing Ltd; John Wiley and Sons Inc
• Subjects: Anchoring; Bayesian inference; Bias; BIOMETRIC PRACTICE; biometry; Correlation analysis; Data Interpretation, Statistical; Economic models; Epidemiology; Extrapolation; HIV; HIV Infections - epidemiology; Human immunodeficiency virus; Humans; Joint models; longitudinal data; Longitudinal Studies; Mathematical models; Missing data; Models, Statistical; Normal distribution; Parameter identification; Parameter sensitivity; Patient Dropouts - statistics & numerical data; random effects; Sensitivity analysis; Skewed distributions; random effects; Bayesian inference; Joint models; missing data; longitudinal data
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1111/biom.13027

Shared parameter models (SPMs) are a useful approach to addressing bias from informative dropout in longitudinal studies. In SPMs it is typically assumed that the longitudinal outcome process and the dropout time are independent, given random effects and observed covariates. However, this conditional independence assumption is unverifiable. Currently, sensitivity analysis strategies for this unverifiable assumption of SPMs are underdeveloped. In principle, parameters that can and cannot be identified by the observed data should be clearly separated in sensitivity analyses, and sensitivity parameters should not influence the model fit to the observed data. For SPMs this is difficult because it is not clear how to separate the observed data likelihood from the distribution of the missing data given the observed data (i.e., 'extrapolation distribution'). In this article, we propose a new approach for transparent sensitivity analyses for informative dropout that separates the observed data likelihood and the extrapolation distribution, using a typical SPM as a working model for the complete data generating mechanism. For this model, the default extrapolation distribution is a skew-normal distribution (i.e., it is available in a closed form). We propose anchoring the sensitivity analysis on the default extrapolation distribution under the specified SPM and calibrate the sensitivity parameters using the observed data for subjects who drop out. The proposed approach is used to address informative dropout in the HIV Epidemiology Research Study.