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Causal inference for bivariate longitudinal quality of life data in presence of death by using global odds ratios.

ABSTRACT: 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.

SUBMITTER: Lee K 

PROVIDER: S-EPMC3935993 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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Causal inference for bivariate longitudinal quality of life data in presence of death by using global odds ratios.

Lee Keunbaik K   Daniels Michael J MJ  

Statistics in medicine 20130530 24

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 rat  ...[more]