A new prognosis factor analysis based on nonhomogeneous Markov description

Takeo Shibata, Hiroshi Tanaka, Yoshihiro Tsujimoto, Kimio Yoshimura, Takashi Fukutomi, Takeshi Nanasawa, Naohito Yamaguchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

To evaluate prognosis factors, Cox's proportional hazard model has been used. But it was found that the analytical ability was not sufficient. So we propose a new evaluation method combining Markov chain model and multiple logistic regression analysis to estimate the prognosis factors. Stage II breast cancer was chosen as the subject. The data was retrospective data gathered in National Cancer Center Central Hospital. As first step, a simple Markov chain model was constructed to describe the state transition of a breast cancer. Then the multiple property of each state transition was investigated in detail. And the patients who had gotten a recurrence for the first two and a half years were discriminated as the poor prognosis group by a nonparametric test (p<0.05). And the result proved to corresponding with the clinical experience. As second step, three factors (n classification of pathological diagnosis, ductal spread, and estrogen receptor) were selected as the prognosis factors for the early death in Stage II breast cancer by a multiple logistic regression analysis. This new prognosis factor analysis could find out some scientific evidences. Especially, it was found to be remarkable efficient in proving clinically experienced observation.

Original languageEnglish
Title of host publicationStudies in Health Technology and Informatics
Pages543-546
Number of pages4
Volume84
DOIs
Publication statusPublished - 2001
Externally publishedYes
Event10th World Congress on Medical Informatics, MEDINFO 2001 - London, United Kingdom
Duration: 2005 Sep 22005 Sep 5

Other

Other10th World Congress on Medical Informatics, MEDINFO 2001
CountryUnited Kingdom
CityLondon
Period05/9/205/9/5

Fingerprint

Factor analysis
Statistical Factor Analysis
Regression analysis
Markov processes
Logistics
Markov Chains
Breast Neoplasms
Logistic Models
Regression Analysis
Hazards
Proportional Hazards Models
Estrogen Receptors
Observation
Recurrence
Neoplasms

Keywords

  • breast cancer
  • Markov chain model
  • multiple logistic regression analysis
  • prognosis factor

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

Cite this

Shibata, T., Tanaka, H., Tsujimoto, Y., Yoshimura, K., Fukutomi, T., Nanasawa, T., & Yamaguchi, N. (2001). A new prognosis factor analysis based on nonhomogeneous Markov description. In Studies in Health Technology and Informatics (Vol. 84, pp. 543-546) https://doi.org/10.3233/978-1-60750-928-8-543

A new prognosis factor analysis based on nonhomogeneous Markov description. / Shibata, Takeo; Tanaka, Hiroshi; Tsujimoto, Yoshihiro; Yoshimura, Kimio; Fukutomi, Takashi; Nanasawa, Takeshi; Yamaguchi, Naohito.

Studies in Health Technology and Informatics. Vol. 84 2001. p. 543-546.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Shibata, T, Tanaka, H, Tsujimoto, Y, Yoshimura, K, Fukutomi, T, Nanasawa, T & Yamaguchi, N 2001, A new prognosis factor analysis based on nonhomogeneous Markov description. in Studies in Health Technology and Informatics. vol. 84, pp. 543-546, 10th World Congress on Medical Informatics, MEDINFO 2001, London, United Kingdom, 05/9/2. https://doi.org/10.3233/978-1-60750-928-8-543
Shibata T, Tanaka H, Tsujimoto Y, Yoshimura K, Fukutomi T, Nanasawa T et al. A new prognosis factor analysis based on nonhomogeneous Markov description. In Studies in Health Technology and Informatics. Vol. 84. 2001. p. 543-546 https://doi.org/10.3233/978-1-60750-928-8-543
Shibata, Takeo ; Tanaka, Hiroshi ; Tsujimoto, Yoshihiro ; Yoshimura, Kimio ; Fukutomi, Takashi ; Nanasawa, Takeshi ; Yamaguchi, Naohito. / A new prognosis factor analysis based on nonhomogeneous Markov description. Studies in Health Technology and Informatics. Vol. 84 2001. pp. 543-546
@inproceedings{8264287c1fa94c0fb3b82b23a05dfe56,
title = "A new prognosis factor analysis based on nonhomogeneous Markov description",
abstract = "To evaluate prognosis factors, Cox's proportional hazard model has been used. But it was found that the analytical ability was not sufficient. So we propose a new evaluation method combining Markov chain model and multiple logistic regression analysis to estimate the prognosis factors. Stage II breast cancer was chosen as the subject. The data was retrospective data gathered in National Cancer Center Central Hospital. As first step, a simple Markov chain model was constructed to describe the state transition of a breast cancer. Then the multiple property of each state transition was investigated in detail. And the patients who had gotten a recurrence for the first two and a half years were discriminated as the poor prognosis group by a nonparametric test (p<0.05). And the result proved to corresponding with the clinical experience. As second step, three factors (n classification of pathological diagnosis, ductal spread, and estrogen receptor) were selected as the prognosis factors for the early death in Stage II breast cancer by a multiple logistic regression analysis. This new prognosis factor analysis could find out some scientific evidences. Especially, it was found to be remarkable efficient in proving clinically experienced observation.",
keywords = "breast cancer, Markov chain model, multiple logistic regression analysis, prognosis factor",
author = "Takeo Shibata and Hiroshi Tanaka and Yoshihiro Tsujimoto and Kimio Yoshimura and Takashi Fukutomi and Takeshi Nanasawa and Naohito Yamaguchi",
year = "2001",
doi = "10.3233/978-1-60750-928-8-543",
language = "English",
isbn = "1586031945",
volume = "84",
pages = "543--546",
booktitle = "Studies in Health Technology and Informatics",

}

TY - GEN

T1 - A new prognosis factor analysis based on nonhomogeneous Markov description

AU - Shibata, Takeo

AU - Tanaka, Hiroshi

AU - Tsujimoto, Yoshihiro

AU - Yoshimura, Kimio

AU - Fukutomi, Takashi

AU - Nanasawa, Takeshi

AU - Yamaguchi, Naohito

PY - 2001

Y1 - 2001

N2 - To evaluate prognosis factors, Cox's proportional hazard model has been used. But it was found that the analytical ability was not sufficient. So we propose a new evaluation method combining Markov chain model and multiple logistic regression analysis to estimate the prognosis factors. Stage II breast cancer was chosen as the subject. The data was retrospective data gathered in National Cancer Center Central Hospital. As first step, a simple Markov chain model was constructed to describe the state transition of a breast cancer. Then the multiple property of each state transition was investigated in detail. And the patients who had gotten a recurrence for the first two and a half years were discriminated as the poor prognosis group by a nonparametric test (p<0.05). And the result proved to corresponding with the clinical experience. As second step, three factors (n classification of pathological diagnosis, ductal spread, and estrogen receptor) were selected as the prognosis factors for the early death in Stage II breast cancer by a multiple logistic regression analysis. This new prognosis factor analysis could find out some scientific evidences. Especially, it was found to be remarkable efficient in proving clinically experienced observation.

AB - To evaluate prognosis factors, Cox's proportional hazard model has been used. But it was found that the analytical ability was not sufficient. So we propose a new evaluation method combining Markov chain model and multiple logistic regression analysis to estimate the prognosis factors. Stage II breast cancer was chosen as the subject. The data was retrospective data gathered in National Cancer Center Central Hospital. As first step, a simple Markov chain model was constructed to describe the state transition of a breast cancer. Then the multiple property of each state transition was investigated in detail. And the patients who had gotten a recurrence for the first two and a half years were discriminated as the poor prognosis group by a nonparametric test (p<0.05). And the result proved to corresponding with the clinical experience. As second step, three factors (n classification of pathological diagnosis, ductal spread, and estrogen receptor) were selected as the prognosis factors for the early death in Stage II breast cancer by a multiple logistic regression analysis. This new prognosis factor analysis could find out some scientific evidences. Especially, it was found to be remarkable efficient in proving clinically experienced observation.

KW - breast cancer

KW - Markov chain model

KW - multiple logistic regression analysis

KW - prognosis factor

UR - http://www.scopus.com/inward/record.url?scp=84888030666&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84888030666&partnerID=8YFLogxK

U2 - 10.3233/978-1-60750-928-8-543

DO - 10.3233/978-1-60750-928-8-543

M3 - Conference contribution

C2 - 11604799

AN - SCOPUS:84888030666

SN - 1586031945

SN - 9781586031947

VL - 84

SP - 543

EP - 546

BT - Studies in Health Technology and Informatics

ER -