24
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          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.

          Results

          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.

          Conclusions

          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

          Related collections

          Most cited references6

          • Record: found
          • Abstract: found
          • Article: not found

          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.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            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.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Quantification of the completeness of follow-up.

              Completeness of follow-up is important, especially in clinical trials, since unequal follow-up in the treatment groups can bias the analysis of results. In survival studies, information on participants who do not complete the study is often omitted because their data can be included up to the time at which they were lost to follow-up. We propose a simple measure of completeness that is the ratio of the total observed person-time and the potential person-time of follow-up in a study. Our measure is easy to calculate, can be illustrated pictorially, and can be used to identify subgroups with especially poor follow-up.
                Bookmark

                Author and article information

                Contributors
                Journal
                Trials
                Trials
                Trials
                BioMed Central
                1745-6215
                2013
                9 August 2013
                : 14
                : 248
                Affiliations
                [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
                Article
                1745-6215-14-248
                10.1186/1745-6215-14-248
                3750911
                23937999
                4d5e05f6-c515-40d6-89e4-7727722b5962
                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.

                History
                : 4 June 2013
                : 31 July 2013
                Categories
                Update

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

                Comments

                Comment on this article