For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
16
views
33
references
 Top references
cited by
110
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      1,733
      similar
       All similar
      • Record: found
      • Abstract: not found
      • Article: not found

      Association of Pharmacological Treatments With Long-term Pain Control in Patients With Knee Osteoarthritis : A Systematic Review and Meta-analysis

      Read this article at

      ScienceOpenPublisher
          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

          Even though osteoarthritis is a chronic and progressive disease, pharmacological agents are mainly studied over short-term periods, resulting in unclear recommendations for long-term disease management.

          Related collections

          Most cited references33

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

          Effect of Intra-articular Triamcinolone vs Saline on Knee Cartilage Volume and Pain in Patients With Knee Osteoarthritis: A Randomized Clinical Trial.

          Timothy McAlindon, Michael LaValley, William F Harvey … (2017)
          Synovitis is common and is associated with progression of structural characteristics of knee osteoarthritis. Intra-articular corticosteroids could reduce cartilage damage associated with synovitis but might have adverse effects on cartilage and periarticular bone.
          Article 0 comments Cited 238 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Small study effects in meta-analyses of osteoarthritis trials: meta-epidemiological study

            Eveline Nüesch, Sven Trelle, Stephan Reichenbach … (2010)
            Objective To examine the presence and extent of small study effects in clinical osteoarthritis research. Design Meta-epidemiological study. Data sources 13 meta-analyses including 153 randomised trials (41 605 patients) that compared therapeutic interventions with placebo or non-intervention control in patients with osteoarthritis of the hip or knee and used patients’ reported pain as an outcome. Methods We compared estimated benefits of treatment between large trials (at least 100 patients per arm) and small trials, explored funnel plots supplemented with lines of predicted effects and contours of significance, and used three approaches to estimate treatment effects: meta-analyses including all trials irrespective of sample size, meta-analyses restricted to large trials, and treatment effects predicted for large trials. Results On average, treatment effects were more beneficial in small than in large trials (difference in effect sizes −0.21, 95% confidence interval −0.34 to −0.08, P=0.001). Depending on criteria used, six to eight funnel plots indicated small study effects. In six of 13 meta-analyses, the overall pooled estimate suggested a clinically relevant, significant benefit of treatment, whereas analyses restricted to large trials and predicted effects in large trials yielded smaller non-significant estimates. Conclusions Small study effects can often distort results of meta-analyses. The influence of small trials on estimated treatment effects should be routinely assessed.
            Article 0 comments Cited 187 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Evidence synthesis for decision making 3: heterogeneity--subgroups, meta-regression, bias, and bias-adjustment.

              Philip Ades, Nicky Welton, Sofia Dias … (2013)
              In meta-analysis, between-study heterogeneity indicates the presence of effect-modifiers and has implications for the interpretation of results in cost-effectiveness analysis and decision making. A distinction is usually made between true variability in treatment effects due to variation in patient populations or settings and biases related to the way in which trials were conducted. Variability in relative treatment effects threatens the external validity of trial evidence and limits the ability to generalize from the results; imperfections in trial conduct represent threats to internal validity. We provide guidance on methods for meta-regression and bias-adjustment, in pairwise and network meta-analysis (including indirect comparisons), using illustrative examples. We argue that the predictive distribution of a treatment effect in a "new" trial may, in many cases, be more relevant to decision making than the distribution of the mean effect. Investigators should consider the relative contribution of true variability and random variation due to biases when considering their response to heterogeneity. In network meta-analyses, various types of meta-regression models are possible when trial-level effect-modifying covariates are present or suspected. We argue that a model with a single interaction term is the one most likely to be useful in a decision-making context. Illustrative examples of Bayesian meta-regression against a continuous covariate and meta-regression against "baseline" risk are provided. Annotated WinBUGS code is set out in an appendix.
              Article 0 comments Cited 167 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Journal
                Title: JAMA
                Abbreviated Title: JAMA
                Publisher: American Medical Association (AMA)
                ISSN (Print): 0098-7484
                Publication date Created: December 25 2018
                Publication date (Print): December 25 2018
                Volume: 320
                Issue: 24
                Page: 2564
                Affiliations
                [1 ]Unit of Biostatistics, Epidemiology, and Public Health, Department of Cardiac, Thoracic, and Vascular Sciences, University of Padova, Padova, Italy
                [2 ]Department of Biostatistics, Rottapharm Biotech, Monza, Italy
                [3 ]Scientific Information and Library Services, Rottapharm Biotech, Monza, Italy
                [4 ]Department of Clinical Research, Rottapharm Biotech, Monza, Italy
                [5 ]School of Medicine and Surgery, University of Milano – Bicocca, Monza, Italy
                Article
                DOI: 10.1001/jama.2018.19319
                SO-VID: 9f7046fe-a4db-4db6-b2d2-df49114b2bee
                Copyright © © 2018
                History

                Data availability:

                Comments

                Comment on this article

                scite_

                Similar content1,733

                See all similar

                Cited by108

                See all cited by

                Most referenced authors2,549

                See all reference authors