Strategies for analysing missing item response data with an application to lung cancer.
Missing data problems persist in many scientific investigations. Although various strategies for analyzing missing data have been proposed, they are mainly limited to data on continuous measurements. In this paper, we focus on implementing some of the available strategies to analyze item response data. In particular, we investigate the effects of popular missing data methods on various missing data mechanisms. We examine large sample behaviors of estimators in a simulation study that evaluates and compares their performance. We use data from a quality of life study with lung cancer patients to illustrate the utility of these methods.
Sheng
Xiaoming
X
DFPM--Health Research Center, University of Utah School of Medicine, Salt Lake City, Utah 84108, USA.
Carrière
K C
KC
D000368:Aged; D003198:Computer Simulation; D018849:Controlled Clinical Trials as Topic / Q000379:methods* / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D004526:Efficiency; D005192:Family Health; D005260:Female; D006801:Humans; D016018:Least-Squares Analysis; D008175:Lung Neoplasms / Q000628:therapy; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D017060:Patient Satisfaction / Q000706:statistics & numerical data