This paper presents a discrete mixture model as a suitable approach for the analysis of chronic pain data, when they are expressed by means of ordered scores (ratings). The model is developed to allow for covariates effects; parameters estimation by maximum likelihood (using an E—M algorithm) and related inferential issues are discussed. A case-study concerning the assessment of pain (for a given pathology) and the effect of patients' covariates (e.g., gender, depression state) is illustrated. Besides, the same covariates are considered also for explaining the disruption in everyday lifestyle due to the chronic pain condition.
Get full access to this article
View all access options for this article.
References
1.
Dworkin SF, LeResche L.Research diagnostic criteria for temporomandibular disorders: review, criteria, examinations and specifications, critique. Journal of Craniomandibular Disorders: Facial and Oral Pain1992; 6: 301-55.
2.
Mishra KD, Gatchel RJ, Gardea MAThe relative efficacy of three cognitive-behavioral treatment approaches to temporomandibular disorders. Journal of Behavioral Medicine2000; 23: 293-309.
3.
Gatchel RJ, Garofalo JP, Ellis E., Holt C.Major psychological disorders in acute and chronic TMD: an initial examination . Journal of the American Dental Association1996; 127: 1365-74.
4.
Vassend O., Krogstad BS, Dahl BLNegative affectivity, somatic complaints, and symptoms of temporomandibular disorders. Journal of Psychosomatic Research1995; 39: 889-99.
5.
Murray TJChronic pain. Research Paper, Workers' Compensation Board of Nova Scotia 1995 . Retrieved from http://www.wcb.ns.ca/chronicpain.pdf .
6.
Latham J., Davis BDThe socioeconomic impact of chronic pain. Disability and Rehabilitation1994; 16: 39-44.
7.
Derogatis LRSCL-90-R: administration, scoring and procedures manual. National Computer Systems, 1994.
8.
Bernstein DN , Gatchel RJBiobehavioral predictor variables of treatment outcome in patients with temporomandibular disorders. Journal of Applied Biobehavioral Research2000; 5: 101-13.
9.
Matthews JNS , Morris KPAn application of Bradley-Terry-type models to the measurement of pain. Applied Statistics1995; 44: 243-55.
10.
D'Elia A., Piccolo D.A mixture model for preferences data analysis. Computational Statistics and Data Analysis2005; 49: 917-34.
11.
McLachlan G. , Peel GJFinite mixture models. Wiley, 2000.
12.
Piccolo D.Observed information matrix for MUB models. Quaderni di Statistica2006; 8: 33-78.
13.
D'Elia A.Finite sample performance of the E-M algorithm for ranks data modelling. Statistica2003 ; LXIII: 41-51.
14.
D'Elia A.New developments in ranks data modelling with covariates. In Atti della XLII Riunione Scientifica SIS CLEUP , 2004: 233-44.
