A Factor Analysis Approach for Clustering Patient Reported Outcomes^[*]

 

 Buy Article Permissions and Reprints

Summary

Background: In the field of radiation oncology, the use of extensive patient reported outcomes is increasingly common to meas -ure 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.

¹ Supplementary material published on our web-site http://dx.doi.org/10.3414/ME16-01-0035

• References

• 1 Farnell DJ, Mandall P, Anandadas C, Routledge J, Burns MP, Logue JP. et al. Development of a patient-reported questionnaire for collecting toxicity data following prostate brachytherapy. Radiother Oncol 2010; 97 (Suppl. 01) 136-42.
  CrossrefPubMedGoogle Scholar
• 2 Brandt CA, Cohen DB, Shifman MA, Miller PL, Nadkarni PM, Frawley SJ. Approaches and informatics tools to assist in the integration of similar clinical research questionnaires. Methods Inf Med 2004; 43 (Suppl. 02) 156-62.
  Article in Thieme ConnectPubMedGoogle Scholar
• 3 Hatakeyama Y, Miyano I, Kataoka H, Nakajima N, Watabe T, Yasuda N. et al. Use of a Latent Topic Model for Characteristic Extraction from Health Checkup Questionnaire Data. Methods Inf Med 2015; 54 (Suppl. 06) 515-21.
  Article in Thieme ConnectPubMedGoogle Scholar
• 4 Mulaik S. Foundations of Factor Analysis, Second Edition. Boca Raton (FL): Chapman and Hall/ CRC; 2009
  Google Scholar
• 5 Kuku S, Fragkos C, McCormack M, Forbes A. Radiation-induced bowel injury: the impact of radiotherapy on survivorship after treatment for gynaecological cancers. Br J Cancer 2013; 109 (Suppl. 06) 1504-12.
  CrossrefPubMedGoogle Scholar
• 6 Xiao C, Hanlon A, Zhang Q, Ang K, Rosenthal DI, Nguyen-Tan PF. et al. Symptom clusters in patients with head and neck cancer receiving concurrent chemoradiotherapy. Oral Oncol 2013; 49 (Suppl. 04) 360-6.
  CrossrefPubMedGoogle Scholar
• 7 Kim HJ, Barsevick AM, Tulman L, McDermott PA. Treatment-related symptom clusters in breast cancer: a secondary analysis. J Pain Symptom Manage 2008; 36 (Suppl. 05) 468-79.
  CrossrefPubMedGoogle Scholar
• 8 Khan L, Cramarossa G, Lemke M, Nguyen J, Zhang L, Chen E. et al. Symptom clusters using the Spitzer quality of life index in patients with brain metastases – a reanalysis comparing different statistical methods. Support Care Cancer 2013; 21 (Suppl. 02) 467-73.
  CrossrefPubMedGoogle Scholar
• 9 Kim E, Jahan T, Aouizerat BE, Dodd MJ, Cooper BA, Paul SM. et al. Changes in symptom clusters in patients undergoing radiation therapy. Support Care Cancer 2009; 17 (Suppl. 11) 1383-91.
  CrossrefPubMedGoogle Scholar
• 10 Hird A, Wong J, Zhang L, Tsao M, Barnes E, Danjoux C. et al. Exploration of symptoms clusters within cancer patients with brain metastases using the Spitzer Quality of Life Index. Support Care Cancer 2010; 18 (Suppl. 03) 335-42.
  CrossrefPubMedGoogle Scholar
• 11 Steineck G, Bergmark K, Henningsohn L, alAbany M, Dickman PW, Helgason A. Symptom documentation in cancer survivors as a basis for therapy modifications. Acta Oncol 2002; 41 (Suppl. 03) 244-52.
  CrossrefPubMedGoogle Scholar
• 12 Steineck G, Hunt H, Adolfsson J. A hierarchical step-model for causation of bias-evaluating cancer treatment with epidemiological methods. Acta Oncol 2006; 45 (Suppl. 04) 421-9.
  CrossrefPubMedGoogle Scholar
• 13 Chen B, Chen M, Paisley J, Zaas A, Woods C, Ginsburg GS. et al. Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies. BMC Bioinform 2010; 11: 552.
  CrossrefPubMedGoogle Scholar
• 14 Preacher KJ, Zhang G, Kim C, Mels G. Choosing the Optimal Number ofFactors in Exploratory Factor Analysis: A Model Selection Perspective. Multivariate Behav Res 2013; 48: 28-56.
  CrossrefPubMedGoogle Scholar
• 15 Ruscio J, Roche B. Determining the number of factors to retain in an exploratory factor analysis using comparison data of known factorial structure. Psychol Assess 2012; 24 (Suppl. 02) 282-92.
  CrossrefPubMedGoogle Scholar
• 16 Skerman HM, Yates PM, Battistutta D. Multivariate methods to identify cancer-related symptom clusters. Res Nurs Health 2009; 32 (Suppl. 03) 345-60.
  CrossrefPubMedGoogle Scholar
• 17 Alsadius D, Olsson C, Pettersson N, Tucker SL, Wilderäng U, Steineck G. Patient-reported gastrointestinal symptoms among long-term survivors after radiation therapy for prostate cancer. Radiother Oncol 2014; 112 (Suppl. 02) 237-43.
  CrossrefPubMedGoogle Scholar
• 18 Cattell RB. The scree test for the number of factors. Multivariate Behav Res 1966; 1: 245-76.
  CrossrefPubMedGoogle Scholar
• 19 Kaiser HF. The application of electronic computers to factor analysis. Educational and Psychological Measurement 1960; 20: 141-51.
  CrossrefPubMedGoogle Scholar
• 20 Horn JL. A rationale and test for the number of factors in factor analysis. Psychometrika 1965; 30: 179-85.
  CrossrefPubMedGoogle Scholar
• 21 Raiche G, Riopel M, Blais J-G. Nongraphical solutions for the Cattell’s scree test. The annual meeting of the Psychometric Society Montreal, Quebec, Canada: 2006
  PubMedGoogle Scholar
• 22 Revelle W. psych: Procedures for Personality and Psychological Research. Northwestern University Evanston, Illinois, USA: 2014
  PubMedGoogle Scholar
• 23 Rosseel Y. lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software 2012; 48: 21-36.
  PubMedGoogle Scholar
• 24 Poppert Cordts K, Steele RG. An Evaluation of the Children’s Report of Sleep Patterns Using Confirmatory and Exploratory Factor Analytic Approaches. J Pediatr Psychol. 2016
  PubMedGoogle Scholar
• 25 Waldenström AC, Alsadius D, Pettersson N, Johansson KA, Holmberg E, Steineck G. et al. Variation in position and volume of organs at risk in the small pelvis. Acta Oncol 2010; 49 (Suppl. 04) 491-9.
  CrossrefPubMedGoogle Scholar
• 26 Fabrigar LR, Wegener DT, MacCallum RC, ve Strahan EJ. Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods 1999; 4: 272-99.
  CrossrefPubMedGoogle Scholar
• 27 Vordermark D, Schwab M, Ness-Dourdoumas R, Sailer M, Flentje M, Koelbl O. Association of anorectal dose-volume histograms and impaired fecal continence after 3D conformal radiotherapy for carcinoma of the prostate. Radiother Oncol 2003; 69 (Suppl. 02) 209-14.
  CrossrefPubMedGoogle Scholar

• Supplementary Material