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Estimating and testing the influence of early diagnosis on cancer survival via point effects of diagnoses and treatments
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 estimati...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9315175/ https://www.ncbi.nlm.nih.gov/pubmed/35509212 http://dx.doi.org/10.1177/09622802221098429 |
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author | Wang, Xiaoqin Blom, Johannes Ye, Weimin Yin, Li |
author_facet | Wang, Xiaoqin Blom, Johannes Ye, Weimin Yin, Li |
author_sort | Wang, Xiaoqin |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9315175 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-93151752022-07-27 Estimating and testing the influence of early diagnosis on cancer survival via point effects of diagnoses and treatments Wang, Xiaoqin Blom, Johannes Ye, Weimin Yin, Li Stat Methods Med Res Original Research Articles 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. SAGE Publications 2022-05-04 2022-08 /pmc/articles/PMC9315175/ /pubmed/35509212 http://dx.doi.org/10.1177/09622802221098429 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Research Articles Wang, Xiaoqin Blom, Johannes Ye, Weimin Yin, Li Estimating and testing the influence of early diagnosis on cancer survival via point effects of diagnoses and treatments |
title | Estimating and testing the influence of early diagnosis on cancer
survival via point effects of diagnoses and treatments |
title_full | Estimating and testing the influence of early diagnosis on cancer
survival via point effects of diagnoses and treatments |
title_fullStr | Estimating and testing the influence of early diagnosis on cancer
survival via point effects of diagnoses and treatments |
title_full_unstemmed | Estimating and testing the influence of early diagnosis on cancer
survival via point effects of diagnoses and treatments |
title_short | Estimating and testing the influence of early diagnosis on cancer
survival via point effects of diagnoses and treatments |
title_sort | estimating and testing the influence of early diagnosis on cancer
survival via point effects of diagnoses and treatments |
topic | Original Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9315175/ https://www.ncbi.nlm.nih.gov/pubmed/35509212 http://dx.doi.org/10.1177/09622802221098429 |
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