Spline Pattern-Mixture Models for Missing Data

We consider a continuous outcome subject to nonresponse and a fully observed covariate. We propose a spline proxy pattern-mixture model (S-PPMA), an extension of the proxy pattern-mixture model (PPMA) (Andridge and Little, 2011), to estimate the mean of the outcome under varying assumptions about no...

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Veröffentlicht in:Journal of Data Science 2021-01, Vol.19 (1), p.75-95
Hauptverfasser: Yang, Ye, Little, Roderick J.A.
Format: Artikel
Sprache:eng
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Zusammenfassung:We consider a continuous outcome subject to nonresponse and a fully observed covariate. We propose a spline proxy pattern-mixture model (S-PPMA), an extension of the proxy pattern-mixture model (PPMA) (Andridge and Little, 2011), to estimate the mean of the outcome under varying assumptions about nonresponse. S-PPMA improves the robustness of PPMA, which assumes bivariate normality between the outcome and the covariate, by modeling the relationship via a spline. Simulations indicate that S-PPMA outperforms PPMA when the data deviate from normality and are missing not at random, with minor losses of efficiency when the data are normal.
ISSN:1683-8602
1680-743X
1683-8602
DOI:10.6339/21-JDS1008