A method for using real world data in breast cancer modeling.
J Biomed Inform
; 60: 385-94, 2016 Apr.
Article
em En
| MEDLINE
| ID: mdl-26854868
ABSTRACT
OBJECTIVES:
Today, hospitals and other health care-related institutions are accumulating a growing bulk of real world clinical data. Such data offer new possibilities for the generation of disease models for the health economic evaluation. In this article, we propose a new approach to leverage cancer registry data for the development of Markov models. Records of breast cancer patients from a clinical cancer registry were used to construct a real world data driven disease model.METHODS:
We describe a model generation process which maps database structures to disease state definitions based on medical expert knowledge. Software was programmed in Java to automatically derive a model structure and transition probabilities. We illustrate our method with the reconstruction of a published breast cancer reference model derived primarily from clinical study data. In doing so, we exported longitudinal patient data from a clinical cancer registry covering eight years. The patient cohort (n=892) comprised HER2-positive and HER2-negative women treated with or without Trastuzumab.RESULTS:
The models generated with this method for the respective patient cohorts were comparable to the reference model in their structure and treatment effects. However, our computed disease models reflect a more detailed picture of the transition probabilities, especially for disease free survival and recurrence.CONCLUSIONS:
Our work presents an approach to extract Markov models semi-automatically using real world data from a clinical cancer registry. Health care decision makers may benefit from more realistic disease models to improve health care-related planning and actions based on their own data.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Informática Médica
/
Neoplasias da Mama
Tipo de estudo:
Etiology_studies
/
Health_economic_evaluation
/
Incidence_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Female
/
Humans
Idioma:
En
Revista:
J Biomed Inform
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2016
Tipo de documento:
Article