BACKGROUND: In the field of radiation oncology, the use of extensive patient reported outcomes is increasingly common to measure adverse side effects after radiotherapy in cancer patients. Factor analysis has the potential to identify an optimal number of latent factors (i.e., symptom groups). However, the ultimate goal of treatment response modeling is to understand the relationship between treatment variables such as radiation dose and symptom groups resulting from FA. Hence, it is crucial to identify clinically more relevant symptom groups and improved response variables from those symptom groups for a quantitative analysis. OBJECTIVES: The goal of this study is to design a computational method for finding clinically relevant symptom groups from PROs and to test associations between symptom groups and radiation dose. METHODS: We propose a novel approach where exploratory factor analysis is followed by confirmatory factor analysis to determine the relevant number of symptom groups. We also propose to use a combination of symptoms in a symptom group identified as a new response variable in linear regression analysis to investigate the relationship between the symptom group and dose-volume variables. RESULTS: We analyzed patient-reported gastrointestinal symptom profiles from 3 datasets in prostate cancer patients treated with radiotherapy. The final structural model of each dataset was validated using the other two datasets and compared to four other existing FA methods. Our systematic EFA-CFA approach provided clinically more relevant solutions than other methods, resulting in new clinically relevant outcome variables that enabled a quantitative analysis. As a result, statistically significant correlations were found between some dose-volume variables to relevant anatomic structures and symptom groups identified by FA. CONCLUSIONS: Our proposed method can aid in the process of understanding PROs and provide a basis for improving our understanding of radiation-induced side effects.
BACKGROUND: In the field of radiation oncology, the use of extensive patient reported outcomes is increasingly common to measure adverse side effects after radiotherapy in cancerpatients. Factor analysis has the potential to identify an optimal number of latent factors (i.e., symptom groups). However, the ultimate goal of treatment response modeling is to understand the relationship between treatment variables such as radiation dose and symptom groups resulting from FA. Hence, it is crucial to identify clinically more relevant symptom groups and improved response variables from those symptom groups for a quantitative analysis. OBJECTIVES: The goal of this study is to design a computational method for finding clinically relevant symptom groups from PROs and to test associations between symptom groups and radiation dose. METHODS: We propose a novel approach where exploratory factor analysis is followed by confirmatory factor analysis to determine the relevant number of symptom groups. We also propose to use a combination of symptoms in a symptom group identified as a new response variable in linear regression analysis to investigate the relationship between the symptom group and dose-volume variables. RESULTS: We analyzed patient-reported gastrointestinal symptom profiles from 3 datasets in prostate cancerpatients treated with radiotherapy. The final structural model of each dataset was validated using the other two datasets and compared to four other existing FA methods. Our systematic EFA-CFA approach provided clinically more relevant solutions than other methods, resulting in new clinically relevant outcome variables that enabled a quantitative analysis. As a result, statistically significant correlations were found between some dose-volume variables to relevant anatomic structures and symptom groups identified by FA. CONCLUSIONS: Our proposed method can aid in the process of understanding PROs and provide a basis for improving our understanding of radiation-induced side effects.
Authors: Gunnar Steineck; Karin Bergmark; Lars Henningsohn; Massoud al-Abany; Paul W Dickman; Asgeir Helgason Journal: Acta Oncol Date: 2002 Impact factor: 4.089
Authors: Amanda Hird; Jennifer Wong; Liying Zhang; May Tsao; Elizabeth Barnes; Cyril Danjoux; Edward Chow Journal: Support Care Cancer Date: 2009-05-31 Impact factor: 3.603
Authors: Gunnar Steineck; Viktor Skokic; Fei Sjöberg; Cecilia Bull; Eleftheria Alevronta; Gail Dunberger; Karin Bergmark; Ulrica Wilderäng; Jung Hun Oh; Joseph O Deasy; Rebecka Jörnsten Journal: PLoS One Date: 2017-02-03 Impact factor: 3.240
Authors: Rosa M S Visscher; Nina Feddermann-Demont; Fausto Romano; Dominik Straumann; Giovanni Bertolini Journal: PLoS One Date: 2019-04-02 Impact factor: 3.240