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A sensitivity analysis approach for informative dropout using shared parameter models

Su, L; Li, Q; Barrett, JK; Daniels, MJ; (2019) A sensitivity analysis approach for informative dropout using shared parameter models. Biometrics , 75 (3) pp. 917-926. 10.1111/biom.13027. Green open access

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Abstract

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.

Type: Article
Title: A sensitivity analysis approach for informative dropout using shared parameter models
Open access status: An open access version is available from UCL Discovery
DOI: 10.1111/biom.13027
Publisher version: http://dx.doi.org/10.1111/biom.13027
Language: English
Additional information: © 2019 The Authors. Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: Bayesian inference, joint models, longitudinal data, missing data, random effects
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute for Global Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute for Global Health > Infection and Population Health
URI: https://discovery.ucl.ac.uk/id/eprint/10090679
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