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      PACE – the first placebo controlled trial of paracetamol for acute low back pain: statistical analysis plan


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          Paracetamol (acetaminophen) is recommended in most clinical practice guidelines as the first choice of treatment for low back pain, however there is limited evidence to support this recommendation. The PACE trial is the first placebo controlled trial of paracetamol for acute low back pain. This article describes the statistical analysis plan.


          PACE is a randomized double dummy placebo controlled trial that investigates and compares the effect of paracetamol taken in two regimens for the treatment of low back pain. The protocol has been published. The analysis plan was completed blind to study group and finalized prior to initiation of analyses. All data collected as part of the trial were reviewed, without stratification by group, and classified by baseline characteristics, process of care and trial outcomes. Trial outcomes were classified as primary and secondary outcomes. Appropriate descriptive statistics and statistical testing of between-group differences, where relevant, have been planned and described.


          A standard analysis plan was developed for the results of the PACE study. This plan comprehensively describes the data captured and pre-determined statistical tests of relevant outcome measures. The plan demonstrates transparent and verifiable use of the data collected. This a priori plan will be followed to ensure rigorous standards of data analysis are strictly adhered to.

          Trial registration

          Australia and New Zealand Clinical Trials Registry ACTRN12609000966291

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          Most cited references 6

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          The estimation of a preference-based measure of health from the SF-12.

          The SF-12 is a multidimensional generic measure of health-related quality of life. It has become widely used in clinical trials and routine outcome assessment because of its brevity and psychometric performance, but it cannot be used in economic evaluation in its current form. We sought to derive a preference-based measure of health from the SF-12 for use in economic evaluation and to compare it with the original SF-36 preference-based index. The SF-12 was revised into a 6-dimensional health state classification (SF-6D [SF-12]) based on an item selection process designed to ensure the minimum loss of descriptive information. A sample of 241 states defined by the SF-6D (of 7500) have been valued by a representative sample of 611 members of the UK general population using the standard gamble (SG) technique. Models are estimated of the relationship between the SF-6D (SF-12) and SG values and evaluated in terms of their coefficients, overall fit, and the ability to predict SG values for all health states. The models have produced significant coefficients for levels of the SF-6D (SF-12), which are robust across model specification. The coefficients are similar to those of the SF-36 version and achieve similar levels of fit. There are concerns with some inconsistent estimates and these have been merged to produce the final recommended model. As for the SF-36 model, there is evidence of over prediction of the value of the poorest health states. The SF-12 index provides a useful tool for researchers and policy makers wishing to assess the cost-effectiveness of interventions.
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            What is the prognosis of back pain?

            Understanding prognosis is important in managing low back pain. In this article, we discuss the available evidence on low back pain prognosis and describe how prognostic evidence can be used to inform clinical decision making. We describe three main types of related prognosis questions: 'What is the most likely course?' (Course studies); 'What factors are associated with, or determine, outcome?' (Prognostic factor or explanatory studies); and 'Can we identify risk groups who are likely to have different outcomes?' (Risk group or outcome prediction studies). Most low back pain episodes are mild and rarely disabling, with only a small proportion of individuals seeking care. Among those presenting for care, there is variability in outcome according to patient characteristics. Most new episodes recover within a few weeks. However, recurrences are common and individuals with chronic, long-standing low back pain tend to show a more persistent course. Studies of mixed primary care populations indicate 60-80% of health-care consulters will continue to have pain after a year. Important low back pain prognostic factors are related to the back pain episode, the individual and psychological characteristics, as well as the work and social environment. Although numerous studies have developed prediction models in the field, most models/tools explain less than 50% of outcome variability and few have been tested in independent samples. We discuss limitations and future directions for research in the area of low back pain prognosis. Copyright 2009 Elsevier Ltd. All rights reserved.
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              Responsiveness of pain, disability, and physical impairment outcomes in patients with low back pain.

              Cohort study. To conduct a head-to-head comparison of the responsiveness of pain, disability, and physical impairment measures in patients with low back pain. Pain, disability, and physical impairment measures are routinely measured in clinical practice and clinical research. However, to date, a head-to-head comparison has not been performed. A numerical pain scale (0-10), the 24-item and 2 modified 18-item versions of the Roland Morris questionnaire, the patient specific functional scale, and physical impairment measures were completed by 155 patients with low back pain at baseline and then again after 6 weeks together with an 11-point global perceived effect scale. Responsiveness was evaluated by using effect sizes and t tests, correlating the change scores for each outcome with the global perceived effect score and by calculating the Guyatt responsiveness index. The most responsive outcome proved to be the patient specific functional scale (effect size = 1.6), followed by the numerical pain scale (effect size = 1.3) and 24-item Roland Morris questionnaire (effect size = 0.8). The responsiveness of the two 18-item Roland Morris versions was equal to the 24-item version. However, the physical impairment measures were not very responsive (effect size 0.1-0.6). The ranking of the responsiveness indices was consistent across all statistical analyses. Physical impairments are routinely measured in clinical practice and clinical research, but the lower responsiveness indicates that this approach is not optimal. Our findings suggest that more emphasis should be placed on change in pain and disability scores than on change in physical impairments.

                Author and article information

                BioMed Central
                9 August 2013
                : 14
                : 248
                [1 ]The George Institute for Global Health and Sydney Medical School, University of Sydney, PO Box M201, Missenden Rd, 2040 Camperdown, NSW, Australia
                [2 ]Faculty of Pharmacy and Centre for Education and Research in Ageing, University of Sydney, 2006 Sydney, NSW, Australia
                [3 ]Faculty of Human Sciences, Macquarie University, 75 Talavera Rd, 2113 Sydney, NSW, Australia
                [4 ]Clinical Pharmacology UNSW and St Vincent’s Hospital, 2010 Darlinghurst, NSW, Australia
                Copyright © 2013 Williams et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.



                acetaminophen, back pain, paracetamol, statistical analysis plan, randomised controlled trial


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