Non-response models for the analysis of non-monotone non-ignorable missing data.
I introduce a new class of non-ignorable non-monotone missing data models. These models are useful for investigating the sensitivity of one's estimates to untestable assumptions about the missing data process. I use the new models to analyse data from a case-control study of the effect of radiation on breast cancer.
Robins
J M
JM
Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.
D001943:Breast Neoplasms / Q000532:radiotherapy; D016022:Case-Control Studies; D002986:Clinical Trials as Topic / Q000379:methods*; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D015233:Models, Statistical; D016016:Proportional Hazards Models; D018709:Statistics, Nonparametric