151. Use of a Markov transition model to analyse longitudinal low-back pain data
- Author
-
Eric L. Hurwitz, Hal Morgenstern, Fei Yu, and Thomas R Berlin
- Subjects
Statistics and Probability ,medicine.medical_specialty ,Epidemiology ,Logistic regression ,01 natural sciences ,California ,law.invention ,010104 statistics & probability ,03 medical and health sciences ,0302 clinical medicine ,Health Information Management ,Randomized controlled trial ,law ,Surveys and Questionnaires ,Humans ,Medicine ,Longitudinal Studies ,030212 general & internal medicine ,0101 mathematics ,Categorical variable ,Physical Therapy Modalities ,Models, Statistical ,Markov chain ,business.industry ,Managed Care Programs ,Polytomous Rasch model ,Chiropractic ,Low back pain ,Markov Chains ,Self Efficacy ,Outcome and Process Assessment, Health Care ,Telephone interview ,Data Interpretation, Statistical ,Physical therapy ,Clinical Medicine ,medicine.symptom ,business ,Low Back Pain - Abstract
In a randomized clinical trial to assess the effectiveness of different strategies for treating low-back pain in a managed-care setting, 681 adult patients presenting with low-back pain were randomized to four treatment groups: medical care with and without physical therapy; and chiropractic care with and without physical modalities. Follow-up information was obtained by questionnaires at two and six weeks, six, 12 and 18 months and by a telephone interview at four weeks. One outcome measurement at each follow-up is the patient’s self-report on the perception of low-back pain improvement from the previous survey, recorded as ‘A lot better,’ ‘A little better,’ ‘About the same’ and ‘Worse.’ Since the patient’s perception of improvement may be influenced by past experience, the outcome is analysed using a transition (first-order Markov) model. Although one could collapse categories to the point that logistic regression analysis with repeated measurements could be used, here we allow for multiple categories by relating transition probabilities to covariates and previous outcomes through a polytomous logistic regression model with Markov structure. This approach allows us to assess not only the effects of treatment assignment and baseline characteristics but also the effects of past outcomes in analysing longitudinal categorical data.
- Published
- 2003