Liu M, Daniels MJ, Perri MG. Quantile regression in the presence of monotone missingness with sensitivity analysis.
Biostatistics 2015;
17:108-21. [PMID:
26041008 DOI:
10.1093/biostatistics/kxv023]
[Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2014] [Accepted: 05/04/2015] [Indexed: 11/14/2022] Open
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.
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