Spline Pattern-Mixture Models for Missing Data

Ye Yang ； Roderick J.A. Little

《Journal of Data Science》 19卷1期 (2021/01) Pp. 75-95

https://doi.org/10.6339/21-JDS1008

摘要

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.

關鍵字

missing data ； missing not at random ； nonignorable nonresponse ； nonresponse bias