A shiny app for modeling the lifetime in primary breast cancer patients through phase-type distributions
Tallennettuna:
| Julkaisussa: | arXiv.org (Dec 5, 2024), p. n/a |
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| Päätekijä: | |
| Muut tekijät: | , , |
| Julkaistu: |
Cornell University Library, arXiv.org
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| Aiheet: | |
| Linkit: | Citation/Abstract Full text outside of ProQuest |
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| 001 | 3141682372 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 024 | 7 | |a 10.3934/mbe.2024065 10.3934/mbe.2024065 |2 doi | |
| 035 | |a 3141682372 | ||
| 045 | 0 | |b d20241205 | |
| 100 | 1 | |a Acal, Christian | |
| 245 | 1 | |a A shiny app for modeling the lifetime in primary breast cancer patients through phase-type distributions | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 5, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Phase-type distributions (PHDs), which are defined as the distribution of the lifetime up to the absorption in an absorbent Markov chain, are an appropriate candidate to model the lifetime of any system, since any non-negative probability distribution can be approximated by a PHD with sufficient precision. Despite PHD potential, friendly statistical programs do not have a module implemented in their interfaces to handle PHD. Thus, researchers must consider others statistical software such as R, Matlab or Python that work with the compilation of code chunks and functions. This fact might be an important handicap for those researchers who do not have sufficient knowledge in programming environments. In this paper, a new interactive web application developed with shiny is introduced in order to adjust PHD to an experimental dataset. This open access app does not require any kind of knowledge about programming or major mathematical concepts. Users can easily compare the graphic fit of several PHDs while estimating their parameters and assess the goodness of fit with just several clicks. All these functionalities are exhibited by means of a numerical simulation and modeling the time to live since the diagnostic in primary breast cancer patients. | |
| 653 | |a Python | ||
| 653 | |a Programming environments | ||
| 653 | |a Markov chains | ||
| 653 | |a Parameter estimation | ||
| 653 | |a Applications programs | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Goodness of fit | ||
| 653 | |a Computer graphics | ||
| 653 | |a Breast cancer | ||
| 653 | |a User requirements | ||
| 700 | 1 | |a Contreras, Elena | |
| 700 | 1 | |a Montero, Ismael | |
| 700 | 1 | |a Ruiz-Castro, Juan Eloy | |
| 773 | 0 | |t arXiv.org |g (Dec 5, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3141682372/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.03975 |