The effect of omitted covariates in marginal and partially conditional recurrent event analyses.
Publication Date
2019-04Journal Title
Lifetime Data Anal
ISSN
1380-7870
Publisher
Springer Science and Business Media LLC
Volume
25
Issue
2
Pages
280-300
Language
eng
Type
Article
Physical Medium
Print-Electronic
Metadata
Show full item recordCitation
Zhong, Y., & Cook, R. J. (2019). The effect of omitted covariates in marginal and partially conditional recurrent event analyses.. Lifetime Data Anal, 25 (2), 280-300. https://doi.org/10.1007/s10985-018-9430-y
Abstract
There have been many advances in statistical methodology for the analysis of recurrent event data in recent years. Multiplicative semiparametric rate-based models are widely used in clinical trials, as are more general partially conditional rate-based models involving event-based stratification. The partially conditional model provides protection against extra-Poisson variation as well as event-dependent censoring, but conditioning on outcomes post-randomization can induce confounding and compromise causal inference. The purpose of this article is to examine the consequences of model misspecification in semiparametric marginal and partially conditional rate-based analysis through omission of prognostic variables. We do so using estimating function theory and empirical studies.
Keywords
Humans, Recurrence, Data Interpretation, Statistical, Models, Statistical, Markov Chains, Sensitivity and Specificity, Biometry, Data Accuracy, Data Analysis
Sponsorship
MRC (unknown)
Embargo Lift Date
2100-01-01
Identifiers
External DOI: https://doi.org/10.1007/s10985-018-9430-y
This record's URL: https://www.repository.cam.ac.uk/handle/1810/277785
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