Ben Djulbegovic

University of South Florida, Tampa, Florida, United States

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Publications (5)56.55 Total impact

  • Amy I Price · Ben Djulbegovic · Rakesh Biswas · Pranab Chatterjee
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    ABSTRACT: In a recent list-serve, the way forward for evidence-based medicine was discussed. The purpose of this paper was to share the reflections and multiple perspectives discussed in this peer-to-peer encounter and to invite the reader to think with a mind for positive change in the practice of health care. Let us begin with a simple question. What if we dared to look at evidence-based medicine (EBM) and informed shared decision making like two wheels on a bike? They both need to be full of substance, well connected, lubricated and working in balance, propelled and guided by a competent driver, with good vision to get the bike where we want it to go. We need all the tools in the toolkit for the bike to stay operational and to meet the needs of the driver. By the same rationale, evidence alone is necessary but not sufficient for decision making; values are necessary and if neglected, may default to feelings based on social pressures and peer influence. Medical decisions, even shared ones, lack focus without evidence and application. Just as a bike may need a tune up from time to time to maintain optimal performance, EBM may benefit from a tune up where we challenge ourselves to move away from general assumptions and traditions and instead think clearly about the issues we face and how to ask well-formed, specific questions to get the answers to meet the needs we face in health care.
    No preview · Article · Sep 2015 · Journal of Evaluation in Clinical Practice
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    ABSTRACT: The increase in annual global investment in biomedical research--reaching US$240 billion in 2010--has resulted in important health dividends for patients and the public. However, much research does not lead to worthwhile achievements, partly because some studies are done to improve understanding of basic mechanisms that might not have relevance for human health. Additionally, good research ideas often do not yield the anticipated results. As long as the way in which these ideas are prioritised for research is transparent and warranted, these disappointments should not be deemed wasteful; they are simply an inevitable feature of the way science works. However, some sources of waste cannot be justified. In this report, we discuss how avoidable waste can be considered when research priorities are set. We have four recommendations. First, ways to improve the yield from basic research should be investigated. Second, the transparency of processes by which funders prioritise important uncertainties should be increased, making clear how they take account of the needs of potential users of research. Third, investment in additional research should always be preceded by systematic assessment of existing evidence. Fourth, sources of information about research that is in progress should be strengthened and developed and used by researchers. Research funders have primary responsibility for reductions in waste resulting from decisions about what research to do.
    No preview · Article · Jan 2014 · The Lancet
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    ABSTRACT: In the GRADE approach, the strength of a recommendation reflects the extent to which we can be confident that the composite desirable effects of a management strategy outweigh the composite undesirable effects. This article addresses GRADE's approach to determining the direction and strength of a recommendation. The GRADE describes the balance of desirable and undesirable outcomes of interest among alternative management strategies depending on four domains, namely estimates of effect for desirable and undesirable outcomes of interest, confidence in the estimates of effect, estimates of values and preferences, and resource use. Ultimately, guideline panels must use judgment in integrating these factors to make a strong or weak recommendation for or against an intervention.
    No preview · Article · Apr 2013 · Journal of clinical epidemiology
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    ABSTRACT: In the GRADE approach, randomized trials start as high-quality evidence and observational studies as low-quality evidence, but both can be rated down if a body of evidence is associated with a high risk of publication bias. Even when individual studies included in best-evidence summaries have a low risk of bias, publication bias can result in substantial overestimates of effect. Authors should suspect publication bias when available evidence comes from a number of small studies, most of which have been commercially funded. A number of approaches based on examination of the pattern of data are available to help assess publication bias. The most popular of these is the funnel plot; all, however, have substantial limitations. Publication bias is likely frequent, and caution in the face of early results, particularly with small sample size and number of events, is warranted.
    Full-text · Article · Jul 2011 · Journal of clinical epidemiology
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    ABSTRACT: In the GRADE approach, randomized trials start as high-quality evidence and observational studies as low-quality evidence, but both can be rated down if most of the relevant evidence comes from studies that suffer from a high risk of bias. Well-established limitations of randomized trials include failure to conceal allocation, failure to blind, loss to follow-up, and failure to appropriately consider the intention-to-treat principle. More recently recognized limitations include stopping early for apparent benefit and selective reporting of outcomes according to the results. Key limitations of observational studies include use of inappropriate controls and failure to adequately adjust for prognostic imbalance. Risk of bias may vary across outcomes (e.g., loss to follow-up may be far less for all-cause mortality than for quality of life), a consideration that many systematic reviews ignore. In deciding whether to rate down for risk of bias--whether for randomized trials or observational studies--authors should not take an approach that averages across studies. Rather, for any individual outcome, when there are some studies with a high risk, and some with a low risk of bias, they should consider including only the studies with a lower risk of bias.
    Full-text · Article · Apr 2011 · Journal of clinical epidemiology