title: Quantile regression in the presence of monotone missingness with sensitivity analysis.
abstract: In this paper, we develop methods for longitudinal quantile regression when there is monotone missingness. In particular, we propose pattern mixture models with a constraint that provides a straightforward interpretation of the marginal quantile regression parameters. Our approach allows sensitivity analysis which is an essential component in inference for incomplete data. To facilitate computation of the likelihood, we propose a novel way to obtain analytic forms for the required integrals. We conduct simulations to examine the robustness of our approach to modeling assumptions and compare its performance to competing approaches. The model is applied to data from a recent clinical trial on weight management.
authors.affiliation: Department of Statistics, University of Florida, FL 32601, USA.
authors.forename: Minzhao
authors.initials: M
authors.lastname: Liu
authors.affiliation: Department of Integrative Biology, Department of Statistics & Data Sciences, The University of Texas at Austin, 141MC Patterson Hall, Austin, TX 78712, USA mjdaniels@austin.utexas.edu.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Daniels
authors.affiliation: Department of Clinical and Health Psychology, University of Florida, Gainesville, FL 32611, USA.
authors.forename: Michael G
authors.initials: MG
authors.lastname: Perri
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic; D012044:Regression Analysis; D015431:Weight Loss
keywords: Marginalized models; Non-ignorable missingness; Pattern mixture models