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Estimating and testing the influence of early diagnosis on cancer survival via point effects of diagnoses and treatments
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Electrical Engineering, Mathematics and Science, Mathematics.
Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden.
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Sweden.
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Sweden.ORCID iD: 0000-0003-0410-8513
2022 (English)In: Statistical Methods in Medical Research, ISSN 0962-2802, E-ISSN 1477-0334, Vol. 31, no 8, p. 1538-1548Article in journal (Refereed) Published
Abstract [en]

A cancer diagnosis is part of a complex stochastic process, which involves patient's characteristics, diagnosing methods, an initial assessment of cancer progression, treatments and a certain outcome of interest. To evaluate the performance of diagnoses, one needs not only a consistent estimation of the causal effect under a specified regime of diagnoses and treatments but also reliable confidence interval, P-value and hypothesis testing of the causal effect. In this article, we identify causal effects under various regimes of diagnoses and treatments by the point effects of diagnoses and treatments and thus are able to estimate and test these causal effects by estimating and testing point effects in the familiar framework of single-point causal inference. Specifically, using data from a Swedish prognosis study of stomach cancer, we estimate and test the causal effects on cancer survival under various regimes of diagnosing and treating hospitals including the optimal regime. We also estimate and test the modification of the causal effect by age. With its simple setting, one can readily extend the example to a large variety of settings in the area of cancer diagnosis: different personal characteristics such as family history, different diagnosing procedures such as multistage screening, and different cancer outcomes such as cancer progression.

Place, publisher, year, edition, pages
Sage , 2022. Vol. 31, no 8, p. 1538-1548
Keywords [en]
Blip effect, cancer diagnosis, causal effect, G-formula, point effect, treatment regime
National Category
Clinical Medicine
Identifiers
URN: urn:nbn:se:hig:diva-38683DOI: 10.1177/09622802221098429ISI: 000796293600001PubMedID: 35509212Scopus ID: 2-s2.0-85132632705OAI: oai:DiVA.org:hig-38683DiVA, id: diva2:1663168
Funder
Swedish Research Council, 2019-02913Available from: 2022-06-02 Created: 2022-06-02 Last updated: 2022-08-15Bibliographically approved

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Wang, Xiaoqin

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