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Oct 7, 2000 · The rise of evidence based health care has highlighted the use of ineffective interventions, the risks of uncoordinated research, and the consequences of relying on studies published in prestigious journals while ignoring unpublished ones that have negative findings.1-5 Systematic reviews of the best evidence are now recognised as fundamental tools in overcoming these problems because they ...
- Silvio Garattini, Alessandro Liberati
- 2000
The results of the research that get published are expected to be replicable by others. This depends on its precision in terms of the methodology including the sample size; the results extrapolated from the study should also be applicable to the larger population so that the observed effects are near identical to true effects.
Oct 10, 2000 · The rise of evidence based health care has highlighted the use of ineffective interventions, the risks of uncoordinated research, and the consequences of relying on studies published in prestigious journals while ignoring unpublished ones that have negative findings. 1 – 5 Systematic reviews of the best evidence are now recognised as fundamental tools in overcoming these problems because ...
- Silvio Garattini, Alessandro Liberati
- 2000
- 1 Introduction#Section-13-1
- 2 Minimizing Risk of Bias Due to Missing Evidence#Section-13-2
- 4 Summary#Section-13-4
- 5 Chapter Information#Section-13-5
- 6 References#Section-13-6
Systematic reviews seek to identify all research that meets pre-specified eligibility criteria. This goal can be compromised if decisions about how, when or where to report results of eligible studies are influenced by the P value, magnitude or direction of the study’s results. For example, ‘statistically significant’ results that suggest an interv...
The convincing evidence for the presence of non-reporting biases, summarized in Chapter 7, Section 7.2.3, should be of great concern to review authors. Regardless of whether an entire study report or a particular study result is unavailable selectively (e.g. because the P value, magnitude or direction of the results were considered unfavourable by ...
There is clear evidence that selective dissemination of study reports and results leads to an over-estimate of the benefits and under-estimate of the harms of interventions in systematic reviews and meta-analyses. However, overcoming, detecting and correcting for bias due to missing evidence is difficult. Comprehensive searches are important, but a...
Authors: Matthew J Page, Julian PT Higgins, Jonathan AC Sterne Acknowledgments:We thank Douglas Altman, Isabelle Boutron, James Carpenter, Matthias Egger, Roger Harbord, David Jones, David Moher, Alex Sutton, Jennifer Tetzlaff and Lucy Turner for their contributions to previous versions of this chapter. We thank Lisa Bero, Isabelle Boutron, Adam Du...
Alqaidoom Z, Nguyen P, Awadh M, Page M. Impact of searching clinical trials registers in systematic reviews of pharmaceutical and non-pharmaceutical interventions: Reanalysis of meta-analyses. Research Synthesis Methods 2023; 14: 52-67. Askie LM, Darlow BA, Finer N, Schmidt B, Stenson B, Tarnow-Mordi W, Davis PG, Carlo WA, Brocklehurst P, Davies LC...
Example (of low risk of bias): “All patients completed the study and there were no losses to follow up, no treatment withdrawals, no trial group changes and no major adverse events”. Acceptable reasons for missing data
Aug 26, 2020 · Objective To assess the risk of bias associated with missing outcome data in systematic reviews. Design Imputation study. Setting Systematic reviews. Population 100 systematic reviews that included a group level meta-analysis with a statistically significant effect on a patient important dichotomous efficacy outcome. Main outcome measures Median percentage change in the relative effect ...
Jun 1, 2019 · Our simulation study was limited by its single sample size, simple analysis model, and that we considered missingness in only one variable. In real-world data sets, auxiliary variables are often correlated, which will reduce the independent contribution of each variable to the imputation model but may aid in prediction of missing values in an auxiliary variable itself.
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