Non-response models for the analysis of non-monotone ignorable missing data.
We discuss a new class of ignorable non-monotone missing data models-the randomized monotone missingness (RMM) models. We argue that the RMM models represent the most general plausible physical mechanism for generating non-monotone ignorable data. We show that there exists ignorable missing data processes that are not RMM. We argue that it may therefore be inappropriate to analyse non-monotone missing data under the assumption that the missingness mechanism is ignorable, if a statistical test has rejected the hypothesis that the missing data process is RMM representable. We use RMM models to analyse data from a case-control study of the effects of radiation on breast cancer.
Robins
J M
JM
Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.
Gill
R D
RD
D000465:Algorithms; D001943:Breast Neoplasms / Q000532:radiotherapy; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D015233:Models, Statistical; D009381:Neoplasms, Radiation-Induced / Q000209:etiology; D016609:Neoplasms, Second Primary / Q000209:etiology; D016032:Randomized Controlled Trials as Topic / Q000379:methods*