A propensity score adjustment method for longitudinal time series models under nonignorable nonresponse

Zhan Liu () and Chun Yip Yau
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Zhan Liu: Hubei University
Chun Yip Yau: Chinese University of Hong Kong

Statistical Papers, 2022, vol. 63, issue 1, No 13, 317-342

Abstract: Abstract Analysis of data with nonignorable nonresponse is an important and challenging task. Although some methods have been developed for inference under nonignorable nonresponse, they are only available for independent data. In this paper, we develop a two-stage propensity score adjustment method to estimate longitudinal time series models with nonignorable missingness. In particular, the response probability or propensity score is first estimated via solving the mean score equation based on the observed sample. Then, the inverse propensity scores are employed to conduct weighting adjustment for a composite likelihood based estimation. The propensity scores weighted estimation equations are shown to yield consistent and asymptotic normal estimators. Simulation studies and application to AIDS Clinical Trial data are presented to evaluate the performance of the proposed method.

Keywords: Composite likelihood; Consecutive pairwise likelihood; Estimating equation; Missing not at random (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00362-021-01261-0

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