Skip to main content

Generalized Additive Selection Models for the Analysis of Studies with Potentially Nonignorable Missing Outcome Data

Buy Article:

$43.00 plus tax (Refund Policy)


Rotnitzky, Robins, and Scharfstein (1998, Journal of the American Statistical Association93, 1321–1339) developed a methodology for conducting sensitivity analysis of studies in which longitudinal outcome data are subject to potentially nonignorable missingness. In their approach, they specify a class of fully parametric selection models, indexed by a non- or weakly identified selection bias function that indicates the degree to which missingness depends on potentially unobservable outcomes. Estimation of the parameters of interest proceeds by varying the selection bias function over a range considered plausible by subject-matter experts. In this article, we focus on cross-sectional, univariate outcome data and extend their approach to a class of semiparametric selection models, using generalized additive restrictions. We propose a backfitting algorithm to estimate the parameters of the generalized additive selection model. For estimation of the mean outcome, we propose three types of estimating functions: simple inverse weighted, doubly robust, and orthogonal. We present the results of a data analysis and a simulation study.
No References
No Citations
No Supplementary Data
No Article Media
No Metrics

Keywords: Backfitting; Double robustness; Inverse weighting; Sensitivity analysis; Smoothing

Document Type: Research Article

Publication date: 2003-09-01

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more