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The reviewer is often confronted with selective reporting of primary findings, incorrect primary data analysis, and inadequate descriptions of original studies ( 10). Consequently, new or unpopular data tend also to be underreported in the published literature.ĭata available from primary research studies may be inadequate for the literature reviewer. Results of a study of 75 journal reviewers asked to referee identical experimental procedures showed poor interrater agreement and a bias against results contrary to their theoretical perspective ( 9). Second, another form of publication bias, the confirmatory bias, tends to emphasize and believe experiences that support one's views and to ignore or discredit those that do not. At the same time, one should not uncritically assume that methods are better in published studies, as the quality of published papers varies dramatically ( 8). Positive randomized controlled trials were significantly more likely to be published than negative trials (77 percent versus 42 percent, P <. In a survey, for example, 58 investigators indicated that they had conducted 921 randomized controlled trials, and that 96 (21.3 percent) were unpublished. First, because authors and journal editors tend to report statistically significant findings, a review limited to published studies will tend to overestimate the effect size. Two types of bias in the published literature must concern a reviewer. These concerns are applicable to any form of literature review. The limitations of any approach to literature review can be summarized as follows ( 6): (a) sampling bias due to reporting and publication policies (b) the absence in published studies of specific data desired for review (c) biased exclusion of studies by the investigator (d) the uneven quality of the primary data and (e) biased outcome interpretation. Finally, I discuss current research issues related to meta-analysis and highlight future research directions. Then, I describe the strengths and weakness of meta-analysis and approaches to its evaluation. In particular, I examine meta-analysis, a quantitative method to combine data, and illustrate with a clinical example its application to the medical literature. In this article I summarize the constraints on reviewers of the medical literature and review alternative methods for synthesizing scientific studies. Scientific research is founded on integration and replication of results with the possible exception of a new discovery, a single study rarely makes a dramatic contribution to the advancement of knowledge ( 5). Consequently, as the number of studies in any discipline increases, so does the probability that erroneous conclusions will be reached in a narrative review ( 4). Second, the narrative reviewer does not synthesize data quantitatively across literature. As a result, no explicit standards exist to assess the quality of a review. First, no systematic approach is prescribed to obtain primary data or to integrate findings rather, the subjective judgment of the reviewer is used. Such narrative reviews have two basic weaknesses ( 2, 3). Topics for further research may also be proposed. An expert in a field will review studies, decide which are relevant, and highlight his or her findings, both in terms of results and, to a lesser degree, methodology. Traditionally, the medical literature has been integrated in the narrative form. Already registered? Log in now.The goal of an integrative literature review is to summarize the accumulated knowledge concerning a field of interest and to highlight important issues that researchers have left unresolved ( 1). In-person meta-analysis workshops are suspended for the duration of the public-health crisis. Dalenberg, PhD, Alliant International University I look forward to the intermediate and advanced sessions.Ĭonstance J. I picked up details on the statistics, the use of CMA, and the theory behind the statistics, but I also (hopefully) will model the style and clarity of his teaching style in my own work in the future. Borenstein is quite simply a master teacher, so he offers complex material in a completely comprehensible form, the best of both worlds.
#Comprehensive meta analysis rapidshare series#
As I finish the series now, however, I am awestruck by the quality of this offering.
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Borenstein’s impressive credentials and knowledge base) or too basic (given that I was already teaching a course). As a professor of statistics, I was unsure whether to take the initial online course in meta-analysis, thinking that it might either be too dense and jargon-filled to be interesting (given Dr.