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NON‐RESPONSE MODELS FOR THE ANALYSIS OF NON‐MONOTONE IGNORABLE MISSING DATA
Author(s) -
ROBINS JAMES M.,
GILL RICHARD D.
Publication year - 1997
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19970115)16:1<39::aid-sim535>3.0.co;2-d
Subject(s) - missing data , monotone polygon , class (philosophy) , econometrics , computer science , statistics , mathematics , artificial intelligence , geometry
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. © 1997 by John Wiley & Sons, Ltd.