Annals of Internal Medicine: Research and Reporting Methods Topic Collection
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en-usTue, 15 Aug 2017 00:00:00 GMTMon, 14 Aug 2017 16:45:49 GMTSilverchaireditor@annals.orgwebmaster@annals.orgCONSORT Extension for Chinese Herbal Medicine Formulas 2017: Recommendations, Explanation, and Elaboration
http://annals.org/aim/article/2633843/consort-extension-chinese-herbal-medicine-formulas-2017-recommendations-explanation-elaboration
Tue, 18 Jul 2017 00:00:00 GMTCheng C, Wu T, Shang H, et al. <span class="paragraphSection"><div class="boxTitle"></div>Chinese herbal medicine (CHM) formulas are the major components of traditional Chinese medicine (TCM) interventions. The general reporting quality of randomized controlled trials (RCTs) of CHM formulas is disappointing, although CONSORT (Consolidated Standards of Reporting Trials) Statement extensions for herbal medicinal interventions and acupuncture interventions are available. A group of TCM clinical experts, methodologists, epidemiologists, and editors has developed this CONSORT Extension for CHM Formulas (CONSORT-CHM Formulas 2017) through a comprehensive process, including publication of the draft version, solicitation of comments, revision, and finalization.The CONSORT 2010 Statement was extended by introducing the idea of TCM <span style="font-style:italic;">Pattern</span> and the features of CHM formulas. One new checklist subitem, keywords, was added to facilitate indexing and data searching. Seven of the 25 CONSORT checklist items, namely title and abstract, background and objectives, participants, interventions, outcomes, generalizability, and interpretation, are now elaborated, and the explanation of harms specific to CHM formulas is revised. Illustrative examples and explanations are also provided. The group hopes that CONSORT-CHM Formulas 2017 can improve the reporting quality of RCTs of CHM formulas.</span>http://annals.org/aim/article/2633843/consort-extension-chinese-herbal-medicine-formulas-2017-recommendations-explanation-elaborationCONSORT Extension for Chinese Herbal Medicine Formulas 2017: Recommendations, Explanation, and Elaboration (Traditional Chinese Version)
http://annals.org/aim/article/2635061/consort-extension-chinese-herbal-medicine-formulas-2017-recommendations-explanation-elaboration
Tue, 18 Jul 2017 00:00:00 GMTCheng C, Wu T, Shang H, et al. <span class="paragraphSection"><div class="boxTitle"></div><span style="font-style:italic;">Editors' Note:</span> This article is the traditional Chinese version of the CONSORT Extension for Chinese Herbal Medicine Formulas 2017: Recommendations, Explanation, and Elaboration. (Cheng C, Wu T, Shang H, Li, Y, Altman D, Moher D; CONSORT-CHM Formulas 2017 Group. CONSORT Extension for Chinese Herbal Medicine Formulas 2017: Recommendations, Explanation, and Elaboration. Ann Intern Med. 2017;167:112-21. [Epub 27 June 2017]. doi:10.7326/M16-2977).</span>http://annals.org/aim/article/2635061/consort-extension-chinese-herbal-medicine-formulas-2017-recommendations-explanation-elaborationCONSORT Extension for Chinese Herbal Medicine Formulas 2017: Recommendations, Explanation, and Elaboration (Simplified Chinese Version)
http://annals.org/aim/article/2635062/consort-extension-chinese-herbal-medicine-formulas-2017-recommendations-explanation-elaboration
Tue, 18 Jul 2017 00:00:00 GMTCheng C, Wu T, Shang H, et al. <span class="paragraphSection"><div class="boxTitle"></div><span style="font-style:italic;">Editors' Note:</span> This article is the simplified Chinese version of the CONSORT Extension for Chinese Herbal Medicine Formulas 2017: Recommendations, Explanation, and Elaboration. (Cheng C, Wu T, Shang H, Li, Y, Altman D, Moher D; CONSORT-CHM Formulas 2017 Group. CONSORT Extension for Chinese Herbal Medicine Formulas 2017: Recommendations, Explanation, and Elaboration. Ann Intern Med. 2017;167:112-21. [Epub 27 June 2017]. doi:10.7326/M16-2977).</span>http://annals.org/aim/article/2635062/consort-extension-chinese-herbal-medicine-formulas-2017-recommendations-explanation-elaborationSensitivity Analysis in Observational Research: Introducing the E-Value
http://annals.org/aim/article/2643434/sensitivity-analysis-observational-research-introducing-e-value
Tue, 15 Aug 2017 00:00:00 GMTVanderWeele TJ, Ding P. <span class="paragraphSection"><div class="boxTitle"></div>Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This article introduces a new measure called the “E-value,” which is related to the evidence for causality in observational studies that are potentially subject to confounding. The E-value is defined as the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment–outcome association, conditional on the measured covariates. A large E-value implies that considerable unmeasured confounding would be needed to explain away an effect estimate. A small E-value implies little unmeasured confounding would be needed to explain away an effect estimate. The authors propose that in all observational studies intended to produce evidence for causality, the E-value be reported or some other sensitivity analysis be used. They suggest calculating the E-value for both the observed association estimate (after adjustments for measured confounders) and the limit of the confidence interval closest to the null. If this were to become standard practice, the ability of the scientific community to assess evidence from observational studies would improve considerably, and ultimately, science would be strengthened.</span>http://annals.org/aim/article/2643434/sensitivity-analysis-observational-research-introducing-e-value