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Estimating and testing sequential causal effects based on alternative G-formula: an observational study of the influence of early diagnosis on survival of cardia cancer
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electrical Engineering, Mathematics and Science, Mathematics.
2024 (English)In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 53, no 4, p. 1917-1931Article in journal (Refereed) Published
Abstract [en]

Cancer diagnosis is part of a complex stochastic process, in which patients' personal and social characteristics influence the choice of diagnosing methods, diagnosing methods in turn influence the initial assessment of cancer stage, cancer stage in turn influences the choice of treating methods, and treating methods in turn influence cancer outcomes such as cancer survival. To evaluate the performance of diagnoses, one needs to estimate and test the sequential causal effect (SCE) under a specified regime of diagnoses and treatments in such a complex observational study, where the data-generating mechanism is unknown and modeling is needed for statistical inference. In this article, we introduce a method of statistical modeling to estimate and test SCEs under regimes of treatments (diagnoses and treatments in cancer diagnosis) in complex observational studies. By applying the alternative G-formula, we express the SCE in terms of the point effects of treatments in the sequence, so that the modeling can be conducted via the point effects in the framework of single-point causal inference. We illustrate our method by a medical example of cancer diagnosis with data from a Swedish prognosis study of cardia cancer.

Place, publisher, year, edition, pages
Taylor & Francis , 2024. Vol. 53, no 4, p. 1917-1931
Keywords [en]
Cancer diagnosis, G-formula, Point effect, Sequential causal effect, Statistical modeling
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:hig:diva-38421DOI: 10.1080/03610918.2022.2060511ISI: 000781684200001Scopus ID: 2-s2.0-85129213832OAI: oai:DiVA.org:hig-38421DiVA, id: diva2:1652360
Funder
Swedish Research Council Formas, 2019-02913Swedish Research CouncilAvailable from: 2022-04-19 Created: 2022-04-19 Last updated: 2024-03-25Bibliographically approved

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Wang, Xiaoqin

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CiteExportLink to record
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Citation style
  • apa
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  • modern-language-association-8th-edition
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Language
  • sv-SE
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  • de-DE
  • Other locale
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Output format
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