Using the random forest method to detect a response shift in the quality of life of multiple sclerosis patients: a cohort study

BMC Medical Research Methodology

Pascal Auquier¹  Badih Ghattas²  Jean Pelletier³  Patricia Minaya-Flores¹  Karine Baumstarck¹  Anderson Loundou¹  Mohamed Boucekine¹ 

[1] EA3279, Self-perceived Health Assessment Research Unit, School of Medicine, Université de la Méditerranée, 27 bd Jean Moulin, Marseille cedex 05, F-13385, France;Department of Mathematics, Faculté des Sciences de Luminy, Aix-Marseille University, Marseille, France;Departments of Neurology and CRMBM CNRS6612, Timone University Hospital, APHM, Marseille, France

关键词: Variable importance;    Longitudinal studies;    SF-36;    MusiQoL;    Random forest;    Response shift;    Quality of life;    Multiple sclerosis;   

Others  :  1126196
DOI  :  10.1186/1471-2288-13-20

 received in 2012-11-05, accepted in 2013-02-13,  发布年份 2013

【 摘 要 】

Background

Multiple sclerosis (MS), a common neurodegenerative disease, has well-described associations with quality of life (QoL) impairment. QoL changes found in longitudinal studies are difficult to interpret due to the potential response shift (RS) corresponding to respondents’ changing standards, values, and conceptualization of QoL. This study proposes to test the capacity of Random Forest (RF) for detecting RS reprioritization as the relative importance of QoL domains’ changes over time.

Methods

This was a longitudinal observational study. The main inclusion criteria were patients 18 years old or more with relapsing-remitting multiple sclerosis. Every 6 months up to month 24, QoL was recorded using generic and MS-specific questionnaires (MusiQoL and SF-36). At 24 months, individuals were divided into two ‘disability change’ groups: worsened and not-worsened patients. The RF method was performed based on Breiman’s description. Analyses were performed to determine which QoL scores of SF-36 predicted the MusiQoL index. The average variable importance (AVI) was estimated.

Results

A total of 417 (79.6%) patients were defined as not-worsened and 107 (20.4%) as worsened. A clear RS was identified in worsened patients. While the mental score AVI was almost one third higher than the physical score AVI at 12 months, it was 1.5 times lower at 24 months.

Conclusion

This work confirms that the RF method offers a useful statistical approach for RS detection. How to integrate the RS in the interpretation of QoL scores remains a challenge for future research.

Trial registration

ClinicalTrials.gov identifier:NCT00702065

【 授权许可】

   
2013 Boucekine et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

【 参考文献 】

• [1]Mitchell AJ, Benito-Leon J, Gonzalez JM, Rivera-Navarro J: Quality of life and its assessment in multiple sclerosis: integrating physical and psychological components of wellbeing. Lancet Neurol 2005, 4(9):556-566.
• [2]Solari A: Role of health-related quality of life measures in the routine care of people with multiple sclerosis. Health Qual Life Outcomes 2005, 3:16. BioMed Central Full Text
• [3]Miller DM, Allen R: Quality of life in multiple sclerosis: determinants, measurement, and use in clinical practice. Curr Neurol Neurosci Rep 2010, 10(5):397-406.
• [4]Rudick RA, Miller DM: Health-related quality of life in multiple sclerosis: current evidence, measurement and effects of disease severity and treatment. CNS Drugs 2008, 22(10):827-839.
• [5]Sprangers MA, Schwartz CE: Integrating response shift into health-related quality of life research: a theoretical model. Soc Sci Med 1999, 48(11):1507-1515.
• [6]Schwartz CE, Sprangers MA: Methodological approaches for assessing response shift in longitudinal health-related quality-of-life research. Soc Sci Med 1999, 48(11):1531-1548.
• [7]King-Kallimanis BL, Oort FJ, Nolte S, Schwartz CE, Sprangers MA: Using structural equation modeling to detect response shift in performance and health-related quality of life scores of multiple sclerosis patients. Qual Life Res 2011, 20(10):1527-1540.
• [8]Ahmed S, Mayo N, Scott S, Kuspinar A, Schwartz C: Using latent trajectory analysis of residuals to detect response shift in general health among patients with multiple sclerosis. Qual Life Res 2011, 20(10):1555-1560.
• [9]Li Y, Schwartz CE: Data mining for response shift patterns in multiple sclerosis patients using recursive partitioning tree analysis. Qual Life Res 2011, 20(10):1543-1553.
• [10]Schwartz CE, Sprangers MA, Oort F, Ahmed S, Bode R, Li Y, Vollmer T: Response shift in patients with multiple sclerosis: an application of three statistical techniques. Qual Life Res 2011, 20(10):1561-1572.
• [11]Breiman L: Random Forests. Mach Learn 2001, 45:5-32.
• [12]Breiman L, Cutler A, Random Forestshttp://www.math.usu.edu/ webcite~adele/forests (last access Feb 15 2013)
• [13]Verikas A, Gelzinis A, Bacauskiene M: Mining data with random forests: a survey and results of new tests. Pattern Recognition 2011, 44(2):330-349.
• [14]Touw WG, Bayjanov JR, Overmars L, Backus L, Boekhorst J, Wels M, van Hijum SA: Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle? Brief Bioinform 2012.
• [15]Chen X, Ishwaran H: Random forests for genomic data analysis. Genomics 2012, 99(6):323-329.
• [16]Goldstein BA, Polley EC, Briggs FBS: Random forests for genetic association studies. Stat Appl Genet Mol Biol 2011, 10:1-34.
• [17]Flachenecker P, Baumstarck-Barrau K, Butzkueven H, Fernández O, Idiman E, Pelletier J, Stecchi S, Verdun di Cantogno E, Milner A, Auquier P: and the MusiQoL Responsiveness study group. MusiQoL responsiveness exploratory analyses. In 5th Joint Triennial Congress of the European and Americas Committees for Treatment and Research in Multiple Sclerosis (ECTRIMS/ACTRIMS). Amsterdam, The Netherlands: ; 2011.
• [18]McDonald WI, Compston A, Edan G, Goodkin D, Hartung HP, Lublin FD, McFarland HF, Paty DW, Polman CH, Reingold SC: Recommended diagnostic criteria for multiple sclerosis: guidelines from the International Panel on the diagnosis of multiple sclerosis. Ann Neurol 2001, 50(1):121-127.
• [19]Polman CH, Wolinsky JS, Reingold SC: Multiple sclerosis diagnostic criteria: three years later. Mult Scler 2005, 11(1):5-12.
• [20]Kurtzke JF: Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 1983, 33(11):1444-1452.
• [21]Simeoni M, Auquier P, Fernandez O, Flachenecker P, Stecchi S, Constantinescu C, Idiman E, Boyko A, Beiske A, Vollmer T: Validation of the Multiple Sclerosis International Quality of Life questionnaire. Mult Scler 2008, 14(2):219-230.
• [22]Ware JE Jr, Sherbourne CD: The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care 1992, 30(6):473-483.
• [23]Amato MP, Grimaud J, Achiti I, Bartolozzi ML, Adeleine P, Hartung HP, Kappos L, Thompson A, Trojano M, Vukusic S: European validation of a standardized clinical description of multiple sclerosis. J Neurol 2004, 251(12):1472-1480.
• [24]Goodkin DE: EDSS reliability. Neurology 1991, 41((2 Pt 1)):332.
• [25]Breiman L, Friedman JH, Olshen RA, Stone CJ: Classification and regression trees. Monterey,USA: Wadsworth, Inc; 1984.
• [26]Dempster M, Carney R, McClements R: Response shift in the assessment of quality of life among people attending cardiac rehabilitation. Br J Health Psychol 2010, 15(Pt 2):307-319.
• [27]Ring L, Hofer S, Heuston F, Harris D, O'Boyle CA: Response shift masks the treatment impact on patient reported outcomes (PROs): the example of individual quality of life in edentulous patients. Health Qual Life Outcomes 2005, 3:55. BioMed Central Full Text
• [28]Fernandez O, Baumstarck-Barrau K, Simeoni MC, Auquier P: Patient characteristics and determinants of quality of life in an international population with multiple sclerosis: Assessment using the MusiQoL and SF-36 questionnaires. Mult Scler 2011, 17(10):1238-1249.
• [29]Norman G: Hi! How are you? Response shift, implicit theories and differing epistemologies. Qual Life Res 2003, 12(3):239-249.
• [30]Oort FJ: Using structural equation modeling to detect response shifts and true change. Qual Life Res 2005, 14(3):587-598.
• [31]Oort FJ, Visser MR, Sprangers MA: An application of structural equation modeling to detect response shifts and true change in quality of life data from cancer patients undergoing invasive surgery. Qual Life Res 2005, 14(3):599-609.
• [32]Li Y, Rapkin B: Classification and regression tree uncovered hierarchy of psychosocial determinants underlying quality-of-life response shift in HIV/AIDS. J Clin Epidemiol 2009, 62(11):1138-1147.
• [33]Mayo NE, Scott SC, Dendukuri N, Ahmed S, Wood-Dauphinee S: Identifying response shift statistically at the individual level. Qual Life Res 2008, 17(4):627-639.
• [34]Lunetta KL, Hayward LB, Segal J, Van Eerdewegh P: Screening large-scale association study data: exploiting interactions using random forests. BMC Genet 2004, 5(1):32. BioMed Central Full Text
• [35]The R Development Core Team: R: A Language and Environment for Statistical Computing Version 2.11.1 (2010-05-31) Reference Index. 2012. http://www.lsw.uni-heidelberg.de/users/christlieb/teaching/UKStaSS10/R-refman.pdf webcite
• [36]Liaw A, Wiener M: Classification and regression by random Forest. R News 2002, 2:18-22.
• [37]Boyer L, Auquier P: The lack of impact of quality-of-life measures in schizophrenia: a shared responsibility? PharmacoEconomics 2012, 30(6):531-532. author reply 532–533
• [38]Awad AG: Quality-of-life assessment in schizophrenia: the unfulfilled promise. Expert Rev Pharmacoecon Outcomes Res 2011, 11(5):491-493.
• [39]Schwartz CE, Rapkin BD: Reconsidering the psychometrics of quality of life assessment in light of response shift and appraisal. Health Qual Life Outcomes 2004, 2:16. BioMed Central Full Text
• [40]Rapkin BD, Schwartz CE: Toward a theoretical model of quality-of-life appraisal: Implications of findings from studies of response shift. Health Qual Life Outcomes 2004, 15(2):14.