Can You Conduct a Systematic Review Using Pubmed Only
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A step by step guide for conducting a systematic review and meta-analysis with simulation data
Tropical Medicine and Health volume 47, Article number:46 (2019) Cite this commodity
Abstract
Background
The massive abundance of studies relating to tropical medicine and health has increased strikingly over the last few decades. In the field of tropical medicine and health, a well-conducted systematic review and meta-analysis (SR/MA) is considered a feasible solution for keeping clinicians abreast of electric current evidence-based medicine. Understanding of SR/MA steps is of paramount importance for its conduction. It is not easy to be done equally there are obstacles that could face up the researcher. To solve those hindrances, this methodology study aimed to provide a step-by-stride approach mainly for beginners and inferior researchers, in the field of tropical medicine and other health care fields, on how to properly acquit a SR/MA, in which all the steps here depicts our experience and expertise combined with the already well-known and accepted international guidance.
Nosotros suggest that all steps of SR/MA should be done independently past 2–3 reviewers' give-and-take, to ensure information quality and accurateness.
Conclusion
SR/MA steps include the development of enquiry question, forming criteria, search strategy, searching databases, protocol registration, title, abstract, total-text screening, transmission searching, extracting information, quality assessment, data checking, statistical analysis, double data checking, and manuscript writing.
Introduction
The amount of studies published in the biomedical literature, especially tropical medicine and health, has increased strikingly over the final few decades. This massive affluence of literature makes clinical medicine increasingly complex, and noesis from diverse researches is often needed to inform a detail clinical decision. Even so, available studies are often heterogeneous with regard to their blueprint, operational quality, and subjects under report and may handle the research question in a unlike way, which adds to the complexity of evidence and conclusion synthesis [1].
Systematic review and meta-analyses (SR/MAs) have a high level of show equally represented by the evidence-based pyramid. Therefore, a well-conducted SR/MA is considered a feasible solution in keeping health clinicians ahead regarding contemporary bear witness-based medicine.
Differing from a systematic review, unsystematic narrative review tends to be descriptive, in which the authors select frequently manufactures based on their signal of view which leads to its poor quality. A systematic review, on the other hand, is divers every bit a review using a systematic method to summarize show on questions with a detailed and comprehensive plan of study. Furthermore, despite the increasing guidelines for effectively conducting a systematic review, we institute that basic steps often start from framing question, then identifying relevant work which consists of criteria development and search for articles, assess the quality of included studies, summarize the evidence, and interpret the results [2, 3]. Yet, those simple steps are not easy to be reached in reality. There are many troubles that a researcher could exist struggled with which has no detailed indication.
Conducting a SR/MA in tropical medicine and wellness may exist hard particularly for immature researchers; therefore, understanding of its essential steps is crucial. Information technology is not easy to be done equally there are obstacles that could face up the researcher. To solve those hindrances, we recommend a catamenia diagram (Fig. 1) which illustrates a detailed and pace-by-step the stages for SR/MA studies. This methodology study aimed to provide a stride-by-footstep approach mainly for beginners and junior researchers, in the field of tropical medicine and other health intendance fields, on how to properly and succinctly conduct a SR/MA; all the steps here depicts our feel and expertise combined with the already well known and accustomed international guidance.
Detailed flow diagram guideline for systematic review and meta-analysis steps. Note: Star icon refers to "ii–3 reviewers screen independently"
Methods and results
Detailed steps for conducting any systematic review and meta-assay
We searched the methods reported in published SR/MA in tropical medicine and other healthcare fields as well the published guidelines like Cochrane guidelines {Higgins, 2011 #7} [four] to collect the best depression-bias method for each stride of SR/MA conduction steps. Furthermore, we used guidelines that we utilize in studies for all SR/MA steps. We combined these methods in order to conclude and comport a detailed menstruum diagram that shows the SR/MA steps how being conducted.
Whatever SR/MA must follow the widely accepted Preferred Reporting Items for Systematic Review and Meta-analysis statement (PRISMA checklist 2009) (Additional file 5: Table S1) [5].
We proposed our methods according to a valid explanatory simulation instance choosing the topic of "evaluating safety of Ebola vaccine," as it is known that Ebola is a very rare tropical disease but fatal. All the explained methods feature the standards followed internationally, with our compiled feel in the conduct of SR beside it, which we remember proved some validity. This is a SR under conduct by a couple of researchers teaming in a research grouping, moreover, as the outbreak of Ebola which took place (2013–2016) in Africa resulted in a significant mortality and morbidity. Furthermore, since there are many published and ongoing trials assessing the safe of Ebola vaccines, nosotros thought this would provide a keen opportunity to tackle this hotly debated issue. Moreover, Ebola started to burn down again and new fatal outbreak appeared in the Autonomous Republic of Congo since August 2018, which caused infection to more than than m people according to the Globe Health Organisation, and 629 people take been killed till now. Hence, it is considered the second worst Ebola outbreak, after the starting time one in Westward Africa in 2014, which infected more than 26,000 and killed almost xi,300 people along outbreak course.
Research question and objectives
Like other written report designs, the research question of SR/MA should be feasible, interesting, novel, ethical, and relevant. Therefore, a clear, logical, and well-defined research question should be formulated. Ordinarily, two common tools are used: PICO or SPIDER. PICO (Population, Intervention, Comparison, Consequence) is used generally in quantitative evidence synthesis. Authors demonstrated that PICO holds more sensitivity than the more specific SPIDER approach [half dozen]. SPIDER (Sample, Phenomenon of Involvement, Blueprint, Evaluation, Research type) was proposed as a method for qualitative and mixed methods search.
Nosotros here recommend a combined approach of using either one or both the SPIDER and PICO tools to retrieve a comprehensive search depending on time and resource limitations. When we apply this to our assumed research topic, being of qualitative nature, the use of SPIDER approach is more valid.
PICO is usually used for systematic review and meta-analysis of clinical trial study. For the observational study (without intervention or comparator), in many tropical and epidemiological questions, it is normally enough to use P (Patient) and O (outcome) simply to formulate a research question. We must indicate conspicuously the population (P), then intervention (I) or exposure. Next, information technology is necessary to compare (C) the indicated intervention with other interventions, i.e., placebo. Finally, nosotros need to clarify which are our relevant outcomes.
To facilitate comprehension, we choose the Ebola virus disease (EVD) as an example. Currently, the vaccine for EVD is being adult and under phase I, Ii, and Three clinical trials; we want to know whether this vaccine is safe and can induce sufficient immunogenicity to the subjects.
An example of a inquiry question for SR/MA based on PICO for this upshot is as follows: How is the safety and immunogenicity of Ebola vaccine in homo? (P: good for you subjects (man), I: vaccination, C: placebo, O: prophylactic or adverse furnishings)
Preliminary research and idea validation
We recommend a preliminary search to identify relevant articles, ensure the validity of the proposed thought, avoid duplication of previously addressed questions, and assure that nosotros take enough articles for conducting its analysis. Moreover, themes should focus on relevant and important health-care bug, consider global needs and values, reflect the current science, and be consistent with the adopted review methods. Gaining familiarity with a deep agreement of the report field through relevant videos and discussions is of paramount importance for better retrieval of results. If we ignore this step, our study could exist canceled whenever we find out a similar report published before. This means we are wasting our time to deal with a problem that has been tackled for a long fourth dimension.
To do this, we tin can get-go by doing a simple search in PubMed or Google Scholar with search terms Ebola AND vaccine. While doing this step, we identify a systematic review and meta-analysis of determinant factors influencing antibody response from vaccination of Ebola vaccine in not-human primate and human being [7], which is a relevant paper to read to get a deeper insight and identify gaps for better formulation of our research question or purpose. We can still comport systematic review and meta-assay of Ebola vaccine because we evaluate safe as a different effect and different population (only human).
Inclusion and exclusion criteria
Eligibility criteria are based on the PICO arroyo, written report design, and engagement. Exclusion criteria mostly are unrelated, duplicated, unavailable full texts, or abstract-only papers. These exclusions should be stated in accelerate to refrain the researcher from bias. The inclusion criteria would be articles with the target patients, investigated interventions, or the comparison betwixt 2 studied interventions. Briefly, it would be articles which contain information answering our research question. But the virtually important is that it should be clear and sufficient information, including positive or negative, to reply the question.
For the topic we have called, we tin can make inclusion criteria: (1) any clinical trial evaluating the safety of Ebola vaccine and (2) no restriction regarding country, patient age, race, gender, publication language, and date. Exclusion criteria are every bit follows: (1) study of Ebola vaccine in not-human subjects or in vitro studies; (2) report with data not reliably extracted, duplicate, or overlapping information; (three) abstruse-merely papers every bit preceding papers, conference, editorial, and writer response theses and books; (iv) articles without available full text available; and (5) case reports, example series, and systematic review studies. The PRISMA flow diagram template that is used in SR/MA studies tin can be found in Fig. 2.
PRISMA flow diagram of studies' screening and selection
Search strategy
A standard search strategy is used in PubMed, so later it is modified according to each specific database to get the best relevant results. The basic search strategy is built based on the research question conception (i.e., PICO or PICOS). Search strategies are constructed to include gratuitous-text terms (e.k., in the title and abstract) and whatever appropriate subject indexing (e.g., MeSH) expected to retrieve eligible studies, with the help of an good in the review topic field or an data specialist. Additionally, we advise not to apply terms for the Outcomes as their inclusion might hinder the database being searched to think eligible studies because the used outcome is not mentioned apparently in the articles.
The improvement of the search term is made while doing a trial search and looking for another relevant term within each concept from retrieved papers. To search for a clinical trial, we tin can use these descriptors in PubMed: "clinical trial"[Publication Type] OR "clinical trials as topic"[MeSH terms] OR "clinical trial"[All Fields]. Afterwards some rounds of trial and refinement of search term, we formulate the concluding search term for PubMed as follows: (ebola OR ebola virus OR ebola virus affliction OR EVD) AND (vaccine OR vaccination OR vaccinated OR immunization) AND ("clinical trial"[Publication Type] OR "clinical trials as topic"[MeSH Terms] OR "clinical trial"[All Fields]). Because the study for this topic is limited, we do not include outcome term (safety and immunogenicity) in the search term to capture more than studies.
Search databases, import all results to a library, and exporting to an excel canvass
Co-ordinate to the AMSTAR guidelines, at least two databases take to be searched in the SR/MA [8], but every bit y'all increment the number of searched databases, yous go much yield and more accurate and comprehensive results. The ordering of the databases depends more often than not on the review questions; being in a study of clinical trials, you lot volition rely generally on Cochrane, mRCTs, or International Clinical Trials Registry Platform (ICTRP). Here, nosotros propose 12 databases (PubMed, Scopus, Web of Science, EMBASE, GHL, VHL, Cochrane, Google Scholar, Clinical trials.gov, mRCTs, POPLINE, and SIGLE), which assist to embrace well-nigh all published articles in tropical medicine and other health-related fields. Amongst those databases, POPLINE focuses on reproductive health. Researchers should consider to cull relevant database according to the research topic. Some databases practice not support the use of Boolean or quotation; otherwise, there are some databases that have special searching way. Therefore, nosotros demand to change the initial search terms for each database to go appreciated results; therefore, manipulation guides for each online database searches are presented in Additional file 5: Table S2. The detailed search strategy for each database is found in Boosted file 5: Tabular array S3. The search term that we created in PubMed needs customization based on a specific characteristic of the database. An example for Google Scholar advanced search for our topic is as follows:
- 1.
With all of the words: ebola virus
With at to the lowest degree ane of the words: vaccine vaccination vaccinated immunization
Where my words occur: in the title of the article
- two.
With all of the words: EVD
With at least one of the words: vaccine vaccination vaccinated immunization
Where my words occur: in the title of the article
Finally, all records are collected into one Endnote library in order to delete duplicates then to it consign into an excel canvass. Using remove duplicating function with two options is mandatory. All references which have (one) the same title and writer, and published in the same year, and (2) the same title and author, and published in the aforementioned periodical, would be deleted. References remaining after this step should be exported to an excel file with essential information for screening. These could exist the authors' names, publication year, journal, DOI, URL link, and abstruse.
Protocol writing and registration
Protocol registration at an early stage guarantees transparency in the research procedure and protects from duplication problems. Besides, information technology is considered a documented proof of squad programme of action, enquiry question, eligibility criteria, intervention/exposure, quality assessment, and pre-assay programme. It is recommended that researchers ship information technology to the main investigator (PI) to revise it, then upload it to registry sites. At that place are many registry sites available for SR/MA similar those proposed by Cochrane and Campbell collaborations; however, we recommend registering the protocol into PROSPERO equally it is easier. The layout of a protocol template, co-ordinate to PROSPERO, can be found in Boosted file v: File S1.
Title and abstract screening
Decisions to select retrieved articles for farther cess are based on eligibility criteria, to minimize the chance of including non-relevant manufactures. According to the Cochrane guidance, two reviewers are a must to do this step, but as for beginners and junior researchers, this might be deadening; thus, we propose based on our experience that at least three reviewers should work independently to reduce the risk of mistake, particularly in teams with a big number of authors to add more scrutiny and ensure proper conduct. Mostly, the quality with three reviewers would exist better than 2, as two only would have different opinions from each other, then they cannot decide, while the third opinion is crucial. And hither are some examples of systematic reviews which we conducted following the same strategy (past a different group of researchers in our research group) and published successfully, and they feature relevant ideas to tropical medicine and affliction [9,ten,xi].
In this step, duplications will be removed manually whenever the reviewers observe them out. When there is a doubt about an article decision, the team should be inclusive rather than exclusive, until the main leader or PI makes a determination later discussion and consensus. All excluded records should be given exclusion reasons.
Full text downloading and screening
Many search engines provide links for complimentary to access full-text manufactures. In case not plant, we can search in some research websites as ResearchGate, which offering an option of direct full-text request from authors. Additionally, exploring archives of wanted journals, or contacting PI to purchase it if available. Similarly, two–three reviewers piece of work independently to make up one's mind about included full texts co-ordinate to eligibility criteria, with reporting exclusion reasons of articles. In instance any disagreement has occurred, the terminal decision has to be fabricated by discussion.
Transmission search
One has to exhaust all possibilities to reduce bias past performing an explicit hand-searching for retrieval of reports that may take been dropped from commencement search [12]. We apply 5 methods to make manual searching: searching references from included studies/reviews, contacting authors and experts, and looking at related articles/cited articles in PubMed and Google Scholar.
We describe here iii consecutive methods to increase and refine the yield of manual searching: firstly, searching reference lists of included articles; secondly, performing what is known as citation tracking in which the reviewers runway all the articles that cite each one of the included articles, and this might involve electronic searching of databases; and thirdly, like to the citation tracking, we follow all "related to" or "similar" articles. Each of the abovementioned methods can be performed by 2–iii contained reviewers, and all the possible relevant commodity must undergo further scrutiny against the inclusion criteria, after following the same records yielded from electronic databases, i.eastward., title/abstract and full-text screening.
We propose an independent reviewing by assigning each member of the teams a "tag" and a distinct method, to compile all the results at the end for comparison of differences and give-and-take and to maximize the retrieval and minimize the bias. Similarly, the number of included articles has to be stated before add-on to the overall included records.
Data extraction and quality assessment
This step entitles data collection from included full-texts in a structured extraction excel sheet, which is previously pilot-tested for extraction using some random studies. We recommend extracting both adjusted and non-adapted information because information technology gives the most immune misreckoning factor to be used in the analysis past pooling them later [thirteen]. The process of extraction should be executed by 2–iii independent reviewers. Mostly, the sheet is classified into the study and patient characteristics, outcomes, and quality assessment (QA) tool.
Information presented in graphs should be extracted past software tools such as Web plot digitizer [14]. Most of the equations that tin be used in extraction prior to analysis and estimation of standard deviation (SD) from other variables is found inside Boosted file 5: File S2 with their references as Hozo et al. [15], Xiang et al. [xvi], and Rijkom et al. [17]. A multifariousness of tools are available for the QA, depending on the pattern: ROB-2 Cochrane tool for randomized controlled trials [eighteen] which is presented as Additional file 1: Figure S1 and Additional file 2: Figure S2—from a previous published commodity data—[19], NIH tool for observational and cross-exclusive studies [20], ROBINS-I tool for non-randomize trials [21], QUADAS-2 tool for diagnostic studies, QUIPS tool for prognostic studies, Care tool for case reports, and ToxRtool for in vivo and in vitro studies. We recommend that 2–3 reviewers independently appraise the quality of the studies and add together to the data extraction grade before the inclusion into the analysis to reduce the risk of bias. In the NIH tool for observational studies—accomplice and cross-sectional—as in this EBOLA instance, to evaluate the risk of bias, reviewers should rate each of the 14 items into dichotomous variables: yes, no, or non applicable. An overall score is calculated by adding all the items scores as yes equals one, while no and NA equals cipher. A score volition exist given for every paper to allocate them as poor, fair, or adept conducted studies, where a score from 0–5 was considered poor, 6–9 every bit off-white, and x–fourteen every bit good.
In the EBOLA case example in a higher place, authors can extract the post-obit information: name of authors, land of patients, year of publication, study blueprint (case report, accomplice study, or clinical trial or RCT), sample size, the infected bespeak of fourth dimension after EBOLA infection, follow-upwardly interval after vaccination time, efficacy, safety, agin effects after vaccinations, and QA sheet (Boosted file six: Information S1).
Data checking
Due to the expected human being fault and bias, nosotros recommend a data checking step, in which every included article is compared with its counterpart in an extraction sheet by evidence photos, to discover mistakes in data. We advise assigning manufactures to ii–3 independent reviewers, ideally not the ones who performed the extraction of those articles. When resources are limited, each reviewer is assigned a dissimilar commodity than the ane he extracted in the previous stage.
Statistical analysis
Investigators use different methods for combining and summarizing findings of included studies. Before assay, in that location is an of import step chosen cleaning of information in the extraction canvass, where the annotator organizes extraction sail information in a course that can be read by analytical software. The assay consists of 2 types namely qualitative and quantitative analysis. Qualitative assay by and large describes data in SR studies, while quantitative analysis consists of two principal types: MA and network meta-analysis (NMA). Subgroup, sensitivity, cumulative analyses, and meta-regression are advisable for testing whether the results are consistent or not and investigating the effect of certain confounders on the upshot and finding the best predictors. Publication bias should be assessed to investigate the presence of missing studies which can affect the summary.
To illustrate basic meta-assay, we provide an imaginary data for the inquiry question about Ebola vaccine safety (in terms of adverse events, 14 days after injection) and immunogenicity (Ebola virus antibodies rising in geometric mean titer, 6 months after injection). Assuming that from searching and data extraction, we decided to practice an analysis to evaluate Ebola vaccine "A" safety and immunogenicity. Other Ebola vaccines were not meta-analyzed because of the limited number of studies (instead, it will exist included for narrative review). The imaginary data for vaccine safety meta-analysis tin can be accessed in Additional file 7: Data S2. To practise the meta-analysis, we tin can use free software, such as RevMan [22] or R packet meta [23]. In this example, we volition employ the R package meta. The tutorial of meta package can be accessed through "Full general Package for Meta-Analysis" tutorial pdf [23]. The R codes and its guidance for meta-analysis washed can be found in Additional file 5: File S3.
For the analysis, we presume that the study is heterogenous in nature; therefore, we cull a random outcome model. We did an analysis on the rubber of Ebola vaccine A. From the data table, we can see some adverse events occurring after intramuscular injection of vaccine A to the subject of the study. Suppose that we include six studies that fulfill our inclusion criteria. Nosotros can do a meta-analysis for each of the adverse events extracted from the studies, for case, arthralgia, from the results of random issue meta-assay using the R meta package.
From the results shown in Additional file 3: Effigy S3, we can encounter that the odds ratio (OR) of arthralgia is 1.06 (0.79; 1.42), p value = 0.71, which ways that there is no clan betwixt the intramuscular injection of Ebola vaccine A and arthralgia, equally the OR is about i, and besides, the P value is insignificant as it is > 0.05.
In the meta-assay, we tin as well visualize the results in a woods plot. It is shown in Fig. 3 an example of a forest plot from the false assay.
Random upshot model forest plot for comparison of vaccine A versus placebo
From the wood plot, we tin can see 6 studies (A to F) and their respective OR (95% CI). The green box represents the issue size (in this case, OR) of each study. The bigger the box means the study weighted more (i.eastward., bigger sample size). The bluish diamond shape represents the pooled OR of the six studies. We can see the blue diamond cantankerous the vertical line OR = 1, which indicates no significance for the association every bit the diamond most equalized in both sides. We can ostend this also from the 95% confidence interval that includes one and the p value > 0.05.
For heterogeneity, we run into that I ii = 0%, which means no heterogeneity is detected; the study is relatively homogenous (it is rare in the real written report). To evaluate publication bias related to the meta-analysis of adverse events of arthralgia, nosotros tin use the metabias function from the R meta package (Additional file 4: Figure S4) and visualization using a funnel plot. The results of publication bias are demonstrated in Fig. iv. We see that the p value associated with this examination is 0.74, indicating symmetry of the funnel plot. Nosotros tin confirm it past looking at the funnel plot.
Publication bias funnel plot for comparison of vaccine A versus placebo
Looking at the funnel plot, the number of studies at the left and correct side of the funnel plot is the same; therefore, the plot is symmetry, indicating no publication bias detected.
Sensitivity assay is a procedure used to discover how different values of an contained variable volition influence the significance of a particular dependent variable by removing one report from MA. If all included written report p values are < 0.05, hence, removing any study will non change the significant association. It is only performed when there is a significant association, so if the p value of MA done is 0.7—more than one—the sensitivity analysis is not needed for this instance study example. If there are two studies with p value > 0.05, removing any of the 2 studies will result in a loss of the significance.
Double data checking
For more than balls on the quality of results, the analyzed information should exist rechecked from full-text data by bear witness photos, to allow an obvious check for the PI of the study.
Manuscript writing, revision, and submission to a journal
Writing based on four scientific sections: introduction, methods, results, and word, mostly with a conclusion. Performing a characteristic table for written report and patient characteristics is a mandatory step which can exist establish every bit a template in Additional file 5: Table S3.
Afterward finishing the manuscript writing, characteristics table, and PRISMA flow diagram, the team should send it to the PI to revise information technology well and respond to his comments and, finally, choose a suitable journal for the manuscript which fits with considerable impact gene and fitting field. We need to pay attention past reading the writer guidelines of journals earlier submitting the manuscript.
Discussion
The role of testify-based medicine in biomedical research is rapidly growing. SR/MAs are besides increasing in the medical literature. This paper has sought to provide a comprehensive arroyo to enable reviewers to produce high-quality SR/MAs. We promise that readers could gain full general knowledge about how to comport a SR/MA and take the conviction to perform i, although this kind of study requires complex steps compared to narrative reviews.
Having the basic steps for conduction of MA, there are many advanced steps that are applied for certain specific purposes. 1 of these steps is meta-regression which is performed to investigate the clan of whatsoever confounder and the results of the MA. Furthermore, there are other types rather than the standard MA like NMA and MA. In NMA, we investigate the divergence between several comparisons when there were not plenty information to enable standard meta-assay. It uses both direct and indirect comparisons to conclude what is the best between the competitors. On the other paw, mega MA or MA of patients tend to summarize the results of independent studies past using its individual subject data. As a more detailed analysis can be done, it is useful in conducting repeated measure analysis and time-to-event analysis. Moreover, information technology tin can perform analysis of variance and multiple regression analysis; however, it requires homogenous dataset and it is fourth dimension-consuming in conduct [24].
Conclusions
Systematic review/meta-analysis steps include development of research question and its validation, forming criteria, search strategy, searching databases, importing all results to a library and exporting to an excel sheet, protocol writing and registration, title and abstruse screening, full-text screening, transmission searching, extracting data and assessing its quality, data checking, conducting statistical assay, double data checking, manuscript writing, revising, and submitting to a journal.
Availability of data and materials
Non applicable.
Abbreviations
- NMA:
-
Network meta-analysis
- PI:
-
Chief investigator
- PICO:
-
Population, Intervention, Comparing, Outcome
- PRISMA:
-
Preferred Reporting Items for Systematic Review and Meta-analysis argument
- QA:
-
Quality assessment
- SPIDER:
-
Sample, Phenomenon of Interest, Design, Evaluation, Research type
- SR/MAs:
-
Systematic review and meta-analyses
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Funding
This report was conducted (in part) at the Joint Usage/Research Center on Tropical Disease, Constitute of Tropical Medicine, Nagasaki University, Japan.
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NTH and GMT were responsible for the idea and its design. The figure was done by GMT. All authors contributed to the manuscript writing and approval of the final version.
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Additional files
Additional file ane:
Figure S1. Chance of bias assessment graph of included randomized controlled trials. (TIF 20 kb)
Boosted file 2:
Effigy S2. Risk of bias assessment summary. (TIF 69 kb)
Additional file 3:
Effigy S3. Arthralgia results of random issue meta-analysis using R meta packet. (TIF 20 kb)
Additional file 4:
Effigy S4. Arthralgia linear regression test of funnel plot asymmetry using R meta packet. (TIF xiii kb)
Additional file 5:
Table S1. PRISMA 2009 Checklist. Table S2. Manipulation guides for online database searches. Table S3. Detailed search strategy for twelve database searches. Table S4. Baseline characteristics of the patients in the included studies. File S1. PROSPERO protocol template file. File S2. Extraction equations that tin be used prior to analysis to get missed variables. File S3. R codes and its guidance for meta-analysis done for comparison between EBOLA vaccine A and placebo. (DOCX 49 kb)
Additional file six:
Data S1. Extraction and quality assessment data sheets for EBOLA case example. (XLSX 1368 kb)
Additional file 7:
Data S2. Imaginary data for EBOLA case example. (XLSX ten kb)
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Tawfik, G.M., Dila, Thou.A.S., Mohamed, M.Y.F. et al. A step by pace guide for conducting a systematic review and meta-analysis with simulation data. Trop Med Health 47, 46 (2019). https://doi.org/x.1186/s41182-019-0165-6
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DOI : https://doi.org/10.1186/s41182-019-0165-6
Keywords
- Search
- Data
- Extraction
- Analysis
- Study
- Results
Source: https://tropmedhealth.biomedcentral.com/articles/10.1186/s41182-019-0165-6
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