Chinese patent medicine (Jinlong Capsule) for gastric cancer

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Abstract

Background:

JLC has been widely applied as a promising adjunctive drug for GC. However, the exact effects and safety of JLC have yet to be systematically investigated. We aimed to summarize the efficacy and safety of JLC for the treatment of advanced GC through the meta-analysis, in order to provide scientific reference for the design of future clinical trials.

Methods:

The protocol followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols. Relevant randomized controlled trials were searched from Cochrane Library, PubMed, Web of Science (WOS), Excerpt Medica Database (Embase), Chinese Biomedical Literature Database (CBM), China National Knowledge Infrastructure (CNKI), China Scientific Journal Database (VIP), and Wanfang Database. Papers in English or Chinese published from their inception to January 2020 will be included without any restrictions.

Study selection and data extraction will be performed independently by 2 investigators. The clinical outcomes including overall response rate, complete response rate, overall survival, Disease-free survival, quality of life (QoL), immune function, and adverse events, were systematically evaluated. Review Manager 5.3 and Stata 14.0 were used for data analysis, and the quality of the studies was also evaluated.

Results and conclusion:

The findings of this research will be published in a peer-reviewed journal, and provide more evidence-based guidance in clinical practice.

International Platform of Registered Systematic Review and Meta-Analysis Protocols (INPLASY) registration number:

INPLASY202040105. URL: https://inplasy.com/inplasy-2020–4–0105/

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Most cited references 24

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Quantifying the impact of between-study heterogeneity in multivariate meta-analyses

Dan Jackson, Ian R White, Richard Riley (2012)

Measures that quantify the impact of heterogeneity in univariate meta-analysis, including the very popular I 2 statistic, are now well established. Multivariate meta-analysis, where studies provide multiple outcomes that are pooled in a single analysis, is also becoming more commonly used. The question of how to quantify heterogeneity in the multivariate setting is therefore raised. It is the univariate R 2 statistic, the ratio of the variance of the estimated treatment effect under the random and fixed effects models, that generalises most naturally, so this statistic provides our basis. This statistic is then used to derive a multivariate analogue of I 2, which we call . We also provide a multivariate H 2 statistic, the ratio of a generalisation of Cochran's heterogeneity statistic and its associated degrees of freedom, with an accompanying generalisation of the usual I 2 statistic, . Our proposed heterogeneity statistics can be used alongside all the usual estimates and inferential procedures used in multivariate meta-analysis. We apply our methods to some real datasets and show how our statistics are equally appropriate in the context of multivariate meta-regression, where study level covariate effects are included in the model. Our heterogeneity statistics may be used when applying any procedure for fitting the multivariate random effects model. Copyright © 2012 John Wiley & Sons, Ltd.

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The trim-and-fill method for publication bias: practical guidelines and recommendations based on a large database of meta-analyses

Linyu Shi, Lifeng Lin, Stefano Omboni (2019)

Abstract Publication bias is a type of systematic error when synthesizing evidence that cannot represent the underlying truth. Clinical studies with favorable results are more likely published and thus exaggerate the synthesized evidence in meta-analyses. The trim-and-fill method is a popular tool to detect and adjust for publication bias. Simulation studies have been performed to assess this method, but they may not fully represent realistic settings about publication bias. Based on real-world meta-analyses, this article provides practical guidelines and recommendations for using the trim-and-fill method. We used a worked illustrative example to demonstrate the idea of the trim-and-fill method, and we reviewed three estimators (R 0, L 0, and Q 0) for imputing missing studies. A resampling method was proposed to calculate P values for all 3 estimators. We also summarized available meta-analysis software programs for implementing the trim-and-fill method. Moreover, we applied the method to 29,932 meta-analyses from the Cochrane Database of Systematic Reviews, and empirically evaluated its overall performance. We carefully explored potential issues occurred in our analysis. The estimators L 0 and Q 0 detected at least one missing study in more meta-analyses than R 0, while Q 0 often imputed more missing studies than L 0. After adding imputed missing studies, the significance of heterogeneity and overall effect sizes changed in many meta-analyses. All estimators generally converged fast. However, L 0 and Q 0 failed to converge in a few meta-analyses that contained studies with identical effect sizes. Also, P values produced by different estimators could yield different conclusions of publication bias significance. Outliers and the pre-specified direction of missing studies could have influential impact on the trim-and-fill results. Meta-analysts are recommended to perform the trim-and-fill method with great caution when using meta-analysis software programs. Some default settings (e.g., the choice of estimators and the direction of missing studies) in the programs may not be optimal for a certain meta-analysis; they should be determined on a case-by-case basis. Sensitivity analyses are encouraged to examine effects of different estimators and outlying studies. Also, the trim-and-fill estimator should be routinely reported in meta-analyses, because the results depend highly on it.

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Traditional Chinese medicine and cancer: History, present situation, and development

Jie Liu, Shuo Wang, Ying-ying Zhang … (2015)

Cancer treatment with traditional Chinese medicine (TCM) has a long history. Heritage provides general conditions for the innovation and development of TCM in oncology. This article reviews the development of TCM in oncology, interprets the position and function of TCM for cancer prevention and treatment, summarizes the innovations of TCM in oncology over nearly fifty years, and suggests the development direction.

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Author and article information

Journal

Journal ID (nlm-ta): Medicine (Baltimore)

Journal ID (iso-abbrev): Medicine (Baltimore)

Journal ID (publisher-id): MEDI

Title: Medicine

Publisher: Wolters Kluwer Health

ISSN (Print): 0025-7974

ISSN (Electronic): 1536-5964

Publication date Collection: 05 June 2020

Publication date (Electronic): 05 June 2020

Volume: 99

Issue: 23

Electronic Location Identifier: e20532

Affiliations

[a ]Department of Gastroenterology

[b ]Infection Control Office

[c ]Department of Emergency, People's Hospital of Weifang Binhai Economic and Technological Development Zone

[d ]Department of Gastroenterology, Weifang People's Hospital, Weifang

[e ]Department of Gastroenterology, Liaocheng People's Hospital, Liaocheng

[f ]Quality Management Office, People's Hospital of Weifang Binhai Economic and Technological Development Zone, Weifang, Shandong Province, China.

Author notes

[∗ ]Correspondence: Jingxia Chi, Quality Management Office, People's Hospital of Weifang Binhai Economic and Technological Development Zone, Xihai Road, No. 05441, Weifang 262737, Shandong Province, China (e-mail: chijx1976@ 123456163.com ).

Author information

Jingxia Chi http://orcid.org/0000-0001-5415-0272

Article

Publisher ID: MD-D-20-03568 Accession ID: 20532

DOI: 10.1097/MD.0000000000020532

PMC ID: 7306397

PubMed ID: 32502010

SO-VID: 46d571f2-cd78-4810-9134-ba31206f14fb

License:

This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. http://creativecommons.org/licenses/by/4.0

History

Date received : 27 April 2020

Date accepted : 1 May 2020

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Keywords: efficacy,gastric cancer,jinlong capsule,meta–analysis,safety

Data availability:

Keywords: efficacy, gastric cancer, jinlong capsule, meta–analysis, safety

Chinese patent medicine (Jinlong Capsule) for gastric cancer : Protocol for a systematic review and meta-analysis

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Abstract

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Methods:

Results and conclusion:

International Platform of Registered Systematic Review and Meta-Analysis Protocols (INPLASY) registration number:

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Karger: Oncology

Most cited references 24

Quantifying the impact of between-study heterogeneity in multivariate meta-analyses

The trim-and-fill method for publication bias: practical guidelines and recommendations based on a large database of meta-analyses

Traditional Chinese medicine and cancer: History, present situation, and development

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