title: A Factor Analysis Approach for Clustering Patient Reported Outcomes.
abstract: 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.
  
  
  OBJECTIVE
  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.
authors: Jung Hun Oh, PhD, Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1250 First Avenue, New York, NY 10065, USA, E-mail: ohj@mskcc.org.
authors: Jung Hun
authors: JH
authors: Oh
authors: Maria
authors: M
authors: Thor
authors: Caroline
authors: C
authors: Olsson
authors: Viktor
authors: V
authors: Skokic
authors: Rebecka
authors: R
authors: Jörnsten
authors: David
authors: D
authors: Alsadius
authors: Niclas
authors: N
authors: Pettersson
authors: Gunnar
authors: G
authors: Steineck
authors: Joseph O
authors: JO
authors: Deasy
mesh_terms: D016000:Cluster Analysis; D015331:Cohort Studies; D003198:Computer Simulation; D000068598:Data Accuracy; D019992:Databases as Topic; D005163:Factor Analysis, Statistical; D006801:Humans; D016014:Linear Models; D000071066:Patient Reported Outcome Measures; D011829:Radiation Dosage; D015203:Reproducibility of Results
keywords: Confirmatory factor analysis; exploratory factor analysis; factor analysis; patient reported outcomes; radiotherapy; toxicity