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Causal inference for bivariate longitudinal quality of life data in presence of death by using global odds ratios
Author(s) -
Lee Keunbaik,
Daniels Michael J.
Publication year - 2013
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/sim.5857
Subject(s) - bivariate analysis , dropout (neural networks) , odds , missing data , econometrics , causal inference , statistics , joint probability distribution , inference , psychology , demography , computer science , mathematics , logistic regression , artificial intelligence , sociology , machine learning
In longitudinal clinical trials, if a subject drops out due to death, certain responses, such as those measuring quality of life (QoL), will not be defined after the time of death. Thus, standard missing data analyses, e.g., under ignorable dropout, are problematic because these approaches implicitly ‘impute’ values of the response after death. In this paper we define a new survivor average causal effect for a bivariate response in a longitudinal quality of life study that had a high dropout rate with the dropout often due to death (or tumor progression). We show how principal stratification, with a few sensitivity parameters, can be used to draw causal inferences about the joint distribution of these two ordinal quality of life measures. Copyright © 2013 John Wiley & Sons, Ltd.