Covariates missing by design: comparison of the efficient score to other weighted methods.
This paper addresses the modelling of missing covariate data with the logistic regression model. The aim of this paper is to evaluate the properties of an efficient score for logistic regression in a two-phase design. Simulation studies show that the efficient score is more efficient than two other pseudo-likelihood methods when the correlation between the missing covariate and its surrogate is high or the sampling proportion is small. These methods are illustrated with data from the National Wilms Tumor Study Group. Results from the example confirm the simulation study findings with the exception that the pseudo-likelihood approach produces more reliable estimates than the weighted pseudo-likelihood approach.
D'Angelo
Gina
G
Department of Biostatistics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA 15261, USA. gmdst17@pitt.edu
Weissfeld
Lisa
L
D035843:Biomedical Research / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D009369:Neoplasms; D014481:United States