a systematic review of literature follows a certain

• Research Guides
• Health Sciences Library

Systematic Reviews & Library Assistance

• Systematic Reviews

What is a Systematic Review?

Sample timeline, standards & guidelines, parts of a systematic review.

• Steps & Helpful Tools
• Preparing for your Systematic Review
• Finding the Search Terms
• Running the Search
• Screening & Selecting Studies
• Synthesizing Date & Writing Review
• Other Types of Reviews
• HSL Systematic Review Policy

Systematic Review Self-Paced Module

Prefer a module over a guide? Want to learn a bit more? Check out our new module:

• So you want to conduct a systematic review...

Get Library Help

Need help? Use the buttons to access information on frequently asked questions.

Ask a Question

Library Workshops

Request Articles/Books

How To Tutorials

Review eBooks

A systematic review is a type of literature review that follows certain standards and guidelines. The review involves a rigorous, well documented, transparent, and reproducible search and selection process, where researchers are attempting to gather and synthesize all evidence that answers a specific clinical question.

What is required?

• A team: A systematic review cannot be completed by 1 person.
• Time: Systematic reviews typically take 12 to 18 months to complete (see sample timeline below). Use PredicTER to estimate the amount of time needed.
• A clear question: Systematic reviews are geared to answer clearly defined clinical questions.
• Comprehensive Literature Searches : With a systematic review you are attempting to find and synthesize all relevant information with a reproducible search.

Where do I start?

• Explore the various standards & guidelines of systematic reviews .
• Examine other review types to determine the best option for your project.

Project Management Tools to assist with the systematic review process:

• Systematic Review Timeline Roles & Responsibilities Checklist

There a variety of standards and guidelines geared towards conducting and writing systematic reviews. Some publishers may require that certain guidelines and/or standards are followed.

• PRISMA Guidelines
• Cochrane Handbook for Systematic Reviews of Interventions
• National Academies Standards for Systematic Reviews

Systematic Reviews contain 5 major parts with many steps in each of those sections. Use the table below to learn more about these specific steps and tools that can help you through the review process.

Prefer a visual flowchart? Check out: A 24-step guide on how to design, conduct, and successfully publish a systematic review and meta-analysis in medical research .

• Next: Preparing for your Systematic Review >>
• Last Updated: May 29, 2024 2:32 PM
• URL: https://guides.libraries.uc.edu/ReviewAssistance

University of Cincinnati Libraries

PO Box 210033 Cincinnati, Ohio 45221-0033

Phone: 513-556-1424

Contact Us | Staff Directory

University of Cincinnati

Alerts | Clery and HEOA Notice | Notice of Non-Discrimination | eAccessibility Concern | Privacy Statement | Copyright Information

© 2021 University of Cincinnati

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• Systematic Review | Definition, Example, & Guide

Systematic Review | Definition, Example & Guide

Published on June 15, 2022 by Shaun Turney . Revised on November 20, 2023.

A systematic review is a type of review that uses repeatable methods to find, select, and synthesize all available evidence. It answers a clearly formulated research question and explicitly states the methods used to arrive at the answer.

They answered the question “What is the effectiveness of probiotics in reducing eczema symptoms and improving quality of life in patients with eczema?”

In this context, a probiotic is a health product that contains live microorganisms and is taken by mouth. Eczema is a common skin condition that causes red, itchy skin.

Table of contents

What is a systematic review, systematic review vs. meta-analysis, systematic review vs. literature review, systematic review vs. scoping review, when to conduct a systematic review, pros and cons of systematic reviews, step-by-step example of a systematic review, other interesting articles, frequently asked questions about systematic reviews.

A review is an overview of the research that’s already been completed on a topic.

What makes a systematic review different from other types of reviews is that the research methods are designed to reduce bias . The methods are repeatable, and the approach is formal and systematic:

• Formulate a research question
• Develop a protocol
• Search for all relevant studies
• Apply the selection criteria
• Extract the data
• Synthesize the data
• Write and publish a report

Although multiple sets of guidelines exist, the Cochrane Handbook for Systematic Reviews is among the most widely used. It provides detailed guidelines on how to complete each step of the systematic review process.

Systematic reviews are most commonly used in medical and public health research, but they can also be found in other disciplines.

Systematic reviews typically answer their research question by synthesizing all available evidence and evaluating the quality of the evidence. Synthesizing means bringing together different information to tell a single, cohesive story. The synthesis can be narrative ( qualitative ), quantitative , or both.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

Systematic reviews often quantitatively synthesize the evidence using a meta-analysis . A meta-analysis is a statistical analysis, not a type of review.

A meta-analysis is a technique to synthesize results from multiple studies. It’s a statistical analysis that combines the results of two or more studies, usually to estimate an effect size .

A literature review is a type of review that uses a less systematic and formal approach than a systematic review. Typically, an expert in a topic will qualitatively summarize and evaluate previous work, without using a formal, explicit method.

Although literature reviews are often less time-consuming and can be insightful or helpful, they have a higher risk of bias and are less transparent than systematic reviews.

Similar to a systematic review, a scoping review is a type of review that tries to minimize bias by using transparent and repeatable methods.

However, a scoping review isn’t a type of systematic review. The most important difference is the goal: rather than answering a specific question, a scoping review explores a topic. The researcher tries to identify the main concepts, theories, and evidence, as well as gaps in the current research.

Sometimes scoping reviews are an exploratory preparation step for a systematic review, and sometimes they are a standalone project.

A systematic review is a good choice of review if you want to answer a question about the effectiveness of an intervention , such as a medical treatment.

To conduct a systematic review, you’ll need the following:

• A precise question , usually about the effectiveness of an intervention. The question needs to be about a topic that’s previously been studied by multiple researchers. If there’s no previous research, there’s nothing to review.
• If you’re doing a systematic review on your own (e.g., for a research paper or thesis ), you should take appropriate measures to ensure the validity and reliability of your research.
• Access to databases and journal archives. Often, your educational institution provides you with access.
• Time. A professional systematic review is a time-consuming process: it will take the lead author about six months of full-time work. If you’re a student, you should narrow the scope of your systematic review and stick to a tight schedule.
• Bibliographic, word-processing, spreadsheet, and statistical software . For example, you could use EndNote, Microsoft Word, Excel, and SPSS.

A systematic review has many pros .

• They minimize research bias by considering all available evidence and evaluating each study for bias.
• Their methods are transparent , so they can be scrutinized by others.
• They’re thorough : they summarize all available evidence.
• They can be replicated and updated by others.

Systematic reviews also have a few cons .

• They’re time-consuming .
• They’re narrow in scope : they only answer the precise research question.

The 7 steps for conducting a systematic review are explained with an example.

Step 1: Formulate a research question

Formulating the research question is probably the most important step of a systematic review. A clear research question will:

• Allow you to more effectively communicate your research to other researchers and practitioners
• Guide your decisions as you plan and conduct your systematic review

A good research question for a systematic review has four components, which you can remember with the acronym PICO :

• Population(s) or problem(s)
• Intervention(s)
• Comparison(s)

You can rearrange these four components to write your research question:

• What is the effectiveness of I versus C for O in P ?

Sometimes, you may want to include a fifth component, the type of study design . In this case, the acronym is PICOT .

• Type of study design(s)
• The population of patients with eczema
• The intervention of probiotics
• In comparison to no treatment, placebo , or non-probiotic treatment
• The outcome of changes in participant-, parent-, and doctor-rated symptoms of eczema and quality of life
• Randomized control trials, a type of study design

Their research question was:

• What is the effectiveness of probiotics versus no treatment, a placebo, or a non-probiotic treatment for reducing eczema symptoms and improving quality of life in patients with eczema?

Step 2: Develop a protocol

A protocol is a document that contains your research plan for the systematic review. This is an important step because having a plan allows you to work more efficiently and reduces bias.

Your protocol should include the following components:

• Background information : Provide the context of the research question, including why it’s important.
• Research objective (s) : Rephrase your research question as an objective.
• Selection criteria: State how you’ll decide which studies to include or exclude from your review.
• Search strategy: Discuss your plan for finding studies.
• Analysis: Explain what information you’ll collect from the studies and how you’ll synthesize the data.

If you’re a professional seeking to publish your review, it’s a good idea to bring together an advisory committee . This is a group of about six people who have experience in the topic you’re researching. They can help you make decisions about your protocol.

It’s highly recommended to register your protocol. Registering your protocol means submitting it to a database such as PROSPERO or ClinicalTrials.gov .

Step 3: Search for all relevant studies

Searching for relevant studies is the most time-consuming step of a systematic review.

To reduce bias, it’s important to search for relevant studies very thoroughly. Your strategy will depend on your field and your research question, but sources generally fall into these four categories:

• Databases: Search multiple databases of peer-reviewed literature, such as PubMed or Scopus . Think carefully about how to phrase your search terms and include multiple synonyms of each word. Use Boolean operators if relevant.
• Handsearching: In addition to searching the primary sources using databases, you’ll also need to search manually. One strategy is to scan relevant journals or conference proceedings. Another strategy is to scan the reference lists of relevant studies.
• Gray literature: Gray literature includes documents produced by governments, universities, and other institutions that aren’t published by traditional publishers. Graduate student theses are an important type of gray literature, which you can search using the Networked Digital Library of Theses and Dissertations (NDLTD) . In medicine, clinical trial registries are another important type of gray literature.
• Experts: Contact experts in the field to ask if they have unpublished studies that should be included in your review.

At this stage of your review, you won’t read the articles yet. Simply save any potentially relevant citations using bibliographic software, such as Scribbr’s APA or MLA Generator .

• Databases: EMBASE, PsycINFO, AMED, LILACS, and ISI Web of Science
• Handsearch: Conference proceedings and reference lists of articles
• Gray literature: The Cochrane Library, the metaRegister of Controlled Trials, and the Ongoing Skin Trials Register
• Experts: Authors of unpublished registered trials, pharmaceutical companies, and manufacturers of probiotics

Step 4: Apply the selection criteria

Applying the selection criteria is a three-person job. Two of you will independently read the studies and decide which to include in your review based on the selection criteria you established in your protocol . The third person’s job is to break any ties.

To increase inter-rater reliability , ensure that everyone thoroughly understands the selection criteria before you begin.

If you’re writing a systematic review as a student for an assignment, you might not have a team. In this case, you’ll have to apply the selection criteria on your own; you can mention this as a limitation in your paper’s discussion.

You should apply the selection criteria in two phases:

• Based on the titles and abstracts : Decide whether each article potentially meets the selection criteria based on the information provided in the abstracts.
• Based on the full texts: Download the articles that weren’t excluded during the first phase. If an article isn’t available online or through your library, you may need to contact the authors to ask for a copy. Read the articles and decide which articles meet the selection criteria.

It’s very important to keep a meticulous record of why you included or excluded each article. When the selection process is complete, you can summarize what you did using a PRISMA flow diagram .

Next, Boyle and colleagues found the full texts for each of the remaining studies. Boyle and Tang read through the articles to decide if any more studies needed to be excluded based on the selection criteria.

When Boyle and Tang disagreed about whether a study should be excluded, they discussed it with Varigos until the three researchers came to an agreement.

Step 5: Extract the data

Extracting the data means collecting information from the selected studies in a systematic way. There are two types of information you need to collect from each study:

• Information about the study’s methods and results . The exact information will depend on your research question, but it might include the year, study design , sample size, context, research findings , and conclusions. If any data are missing, you’ll need to contact the study’s authors.
• Your judgment of the quality of the evidence, including risk of bias .

You should collect this information using forms. You can find sample forms in The Registry of Methods and Tools for Evidence-Informed Decision Making and the Grading of Recommendations, Assessment, Development and Evaluations Working Group .

Extracting the data is also a three-person job. Two people should do this step independently, and the third person will resolve any disagreements.

They also collected data about possible sources of bias, such as how the study participants were randomized into the control and treatment groups.

Step 6: Synthesize the data

Synthesizing the data means bringing together the information you collected into a single, cohesive story. There are two main approaches to synthesizing the data:

• Narrative ( qualitative ): Summarize the information in words. You’ll need to discuss the studies and assess their overall quality.
• Quantitative : Use statistical methods to summarize and compare data from different studies. The most common quantitative approach is a meta-analysis , which allows you to combine results from multiple studies into a summary result.

Generally, you should use both approaches together whenever possible. If you don’t have enough data, or the data from different studies aren’t comparable, then you can take just a narrative approach. However, you should justify why a quantitative approach wasn’t possible.

Boyle and colleagues also divided the studies into subgroups, such as studies about babies, children, and adults, and analyzed the effect sizes within each group.

Step 7: Write and publish a report

The purpose of writing a systematic review article is to share the answer to your research question and explain how you arrived at this answer.

Your article should include the following sections:

• Abstract : A summary of the review
• Introduction : Including the rationale and objectives
• Methods : Including the selection criteria, search method, data extraction method, and synthesis method
• Results : Including results of the search and selection process, study characteristics, risk of bias in the studies, and synthesis results
• Discussion : Including interpretation of the results and limitations of the review
• Conclusion : The answer to your research question and implications for practice, policy, or research

To verify that your report includes everything it needs, you can use the PRISMA checklist .

Once your report is written, you can publish it in a systematic review database, such as the Cochrane Database of Systematic Reviews , and/or in a peer-reviewed journal.

In their report, Boyle and colleagues concluded that probiotics cannot be recommended for reducing eczema symptoms or improving quality of life in patients with eczema. Note Generative AI tools like ChatGPT can be useful at various stages of the writing and research process and can help you to write your systematic review. However, we strongly advise against trying to pass AI-generated text off as your own work.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.

A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other  academic texts , with an introduction , a main body, and a conclusion .

An  annotated bibliography is a list of  source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a  paper .  

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Turney, S. (2023, November 20). Systematic Review | Definition, Example & Guide. Scribbr. Retrieved June 29, 2024, from https://www.scribbr.com/methodology/systematic-review/

Is this article helpful?

Shaun Turney

Other students also liked, how to write a literature review | guide, examples, & templates, how to write a research proposal | examples & templates, what is critical thinking | definition & examples, get unlimited documents corrected.

✔ Free APA citation check included ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Korean J Anesthesiol
• v.71(2); 2018 Apr

Introduction to systematic review and meta-analysis

1 Department of Anesthesiology and Pain Medicine, Inje University Seoul Paik Hospital, Seoul, Korea

2 Department of Anesthesiology and Pain Medicine, Chung-Ang University College of Medicine, Seoul, Korea

Systematic reviews and meta-analyses present results by combining and analyzing data from different studies conducted on similar research topics. In recent years, systematic reviews and meta-analyses have been actively performed in various fields including anesthesiology. These research methods are powerful tools that can overcome the difficulties in performing large-scale randomized controlled trials. However, the inclusion of studies with any biases or improperly assessed quality of evidence in systematic reviews and meta-analyses could yield misleading results. Therefore, various guidelines have been suggested for conducting systematic reviews and meta-analyses to help standardize them and improve their quality. Nonetheless, accepting the conclusions of many studies without understanding the meta-analysis can be dangerous. Therefore, this article provides an easy introduction to clinicians on performing and understanding meta-analyses.

Introduction

A systematic review collects all possible studies related to a given topic and design, and reviews and analyzes their results [ 1 ]. During the systematic review process, the quality of studies is evaluated, and a statistical meta-analysis of the study results is conducted on the basis of their quality. A meta-analysis is a valid, objective, and scientific method of analyzing and combining different results. Usually, in order to obtain more reliable results, a meta-analysis is mainly conducted on randomized controlled trials (RCTs), which have a high level of evidence [ 2 ] ( Fig. 1 ). Since 1999, various papers have presented guidelines for reporting meta-analyses of RCTs. Following the Quality of Reporting of Meta-analyses (QUORUM) statement [ 3 ], and the appearance of registers such as Cochrane Library’s Methodology Register, a large number of systematic literature reviews have been registered. In 2009, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [ 4 ] was published, and it greatly helped standardize and improve the quality of systematic reviews and meta-analyses [ 5 ].

Levels of evidence.

In anesthesiology, the importance of systematic reviews and meta-analyses has been highlighted, and they provide diagnostic and therapeutic value to various areas, including not only perioperative management but also intensive care and outpatient anesthesia [6–13]. Systematic reviews and meta-analyses include various topics, such as comparing various treatments of postoperative nausea and vomiting [ 14 , 15 ], comparing general anesthesia and regional anesthesia [ 16 – 18 ], comparing airway maintenance devices [ 8 , 19 ], comparing various methods of postoperative pain control (e.g., patient-controlled analgesia pumps, nerve block, or analgesics) [ 20 – 23 ], comparing the precision of various monitoring instruments [ 7 ], and meta-analysis of dose-response in various drugs [ 12 ].

Thus, literature reviews and meta-analyses are being conducted in diverse medical fields, and the aim of highlighting their importance is to help better extract accurate, good quality data from the flood of data being produced. However, a lack of understanding about systematic reviews and meta-analyses can lead to incorrect outcomes being derived from the review and analysis processes. If readers indiscriminately accept the results of the many meta-analyses that are published, incorrect data may be obtained. Therefore, in this review, we aim to describe the contents and methods used in systematic reviews and meta-analyses in a way that is easy to understand for future authors and readers of systematic review and meta-analysis.

Study Planning

It is easy to confuse systematic reviews and meta-analyses. A systematic review is an objective, reproducible method to find answers to a certain research question, by collecting all available studies related to that question and reviewing and analyzing their results. A meta-analysis differs from a systematic review in that it uses statistical methods on estimates from two or more different studies to form a pooled estimate [ 1 ]. Following a systematic review, if it is not possible to form a pooled estimate, it can be published as is without progressing to a meta-analysis; however, if it is possible to form a pooled estimate from the extracted data, a meta-analysis can be attempted. Systematic reviews and meta-analyses usually proceed according to the flowchart presented in Fig. 2 . We explain each of the stages below.

Flowchart illustrating a systematic review.

Formulating research questions

A systematic review attempts to gather all available empirical research by using clearly defined, systematic methods to obtain answers to a specific question. A meta-analysis is the statistical process of analyzing and combining results from several similar studies. Here, the definition of the word “similar” is not made clear, but when selecting a topic for the meta-analysis, it is essential to ensure that the different studies present data that can be combined. If the studies contain data on the same topic that can be combined, a meta-analysis can even be performed using data from only two studies. However, study selection via a systematic review is a precondition for performing a meta-analysis, and it is important to clearly define the Population, Intervention, Comparison, Outcomes (PICO) parameters that are central to evidence-based research. In addition, selection of the research topic is based on logical evidence, and it is important to select a topic that is familiar to readers without clearly confirmed the evidence [ 24 ].

Protocols and registration

In systematic reviews, prior registration of a detailed research plan is very important. In order to make the research process transparent, primary/secondary outcomes and methods are set in advance, and in the event of changes to the method, other researchers and readers are informed when, how, and why. Many studies are registered with an organization like PROSPERO ( http://www.crd.york.ac.uk/PROSPERO/ ), and the registration number is recorded when reporting the study, in order to share the protocol at the time of planning.

Defining inclusion and exclusion criteria

Information is included on the study design, patient characteristics, publication status (published or unpublished), language used, and research period. If there is a discrepancy between the number of patients included in the study and the number of patients included in the analysis, this needs to be clearly explained while describing the patient characteristics, to avoid confusing the reader.

Literature search and study selection

In order to secure proper basis for evidence-based research, it is essential to perform a broad search that includes as many studies as possible that meet the inclusion and exclusion criteria. Typically, the three bibliographic databases Medline, Embase, and Cochrane Central Register of Controlled Trials (CENTRAL) are used. In domestic studies, the Korean databases KoreaMed, KMBASE, and RISS4U may be included. Effort is required to identify not only published studies but also abstracts, ongoing studies, and studies awaiting publication. Among the studies retrieved in the search, the researchers remove duplicate studies, select studies that meet the inclusion/exclusion criteria based on the abstracts, and then make the final selection of studies based on their full text. In order to maintain transparency and objectivity throughout this process, study selection is conducted independently by at least two investigators. When there is a inconsistency in opinions, intervention is required via debate or by a third reviewer. The methods for this process also need to be planned in advance. It is essential to ensure the reproducibility of the literature selection process [ 25 ].

Quality of evidence

However, well planned the systematic review or meta-analysis is, if the quality of evidence in the studies is low, the quality of the meta-analysis decreases and incorrect results can be obtained [ 26 ]. Even when using randomized studies with a high quality of evidence, evaluating the quality of evidence precisely helps determine the strength of recommendations in the meta-analysis. One method of evaluating the quality of evidence in non-randomized studies is the Newcastle-Ottawa Scale, provided by the Ottawa Hospital Research Institute 1) . However, we are mostly focusing on meta-analyses that use randomized studies.

If the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) system ( http://www.gradeworkinggroup.org/ ) is used, the quality of evidence is evaluated on the basis of the study limitations, inaccuracies, incompleteness of outcome data, indirectness of evidence, and risk of publication bias, and this is used to determine the strength of recommendations [ 27 ]. As shown in Table 1 , the study limitations are evaluated using the “risk of bias” method proposed by Cochrane 2) . This method classifies bias in randomized studies as “low,” “high,” or “unclear” on the basis of the presence or absence of six processes (random sequence generation, allocation concealment, blinding participants or investigators, incomplete outcome data, selective reporting, and other biases) [ 28 ].

The Cochrane Collaboration’s Tool for Assessing the Risk of Bias [ 28 ]

Data extraction

Two different investigators extract data based on the objectives and form of the study; thereafter, the extracted data are reviewed. Since the size and format of each variable are different, the size and format of the outcomes are also different, and slight changes may be required when combining the data [ 29 ]. If there are differences in the size and format of the outcome variables that cause difficulties combining the data, such as the use of different evaluation instruments or different evaluation timepoints, the analysis may be limited to a systematic review. The investigators resolve differences of opinion by debate, and if they fail to reach a consensus, a third-reviewer is consulted.

Data Analysis

The aim of a meta-analysis is to derive a conclusion with increased power and accuracy than what could not be able to achieve in individual studies. Therefore, before analysis, it is crucial to evaluate the direction of effect, size of effect, homogeneity of effects among studies, and strength of evidence [ 30 ]. Thereafter, the data are reviewed qualitatively and quantitatively. If it is determined that the different research outcomes cannot be combined, all the results and characteristics of the individual studies are displayed in a table or in a descriptive form; this is referred to as a qualitative review. A meta-analysis is a quantitative review, in which the clinical effectiveness is evaluated by calculating the weighted pooled estimate for the interventions in at least two separate studies.

The pooled estimate is the outcome of the meta-analysis, and is typically explained using a forest plot ( Figs. 3 and ​ and4). 4 ). The black squares in the forest plot are the odds ratios (ORs) and 95% confidence intervals in each study. The area of the squares represents the weight reflected in the meta-analysis. The black diamond represents the OR and 95% confidence interval calculated across all the included studies. The bold vertical line represents a lack of therapeutic effect (OR = 1); if the confidence interval includes OR = 1, it means no significant difference was found between the treatment and control groups.

Forest plot analyzed by two different models using the same data. (A) Fixed-effect model. (B) Random-effect model. The figure depicts individual trials as filled squares with the relative sample size and the solid line as the 95% confidence interval of the difference. The diamond shape indicates the pooled estimate and uncertainty for the combined effect. The vertical line indicates the treatment group shows no effect (OR = 1). Moreover, if the confidence interval includes 1, then the result shows no evidence of difference between the treatment and control groups.

Forest plot representing homogeneous data.

Dichotomous variables and continuous variables

In data analysis, outcome variables can be considered broadly in terms of dichotomous variables and continuous variables. When combining data from continuous variables, the mean difference (MD) and standardized mean difference (SMD) are used ( Table 2 ).

Summary of Meta-analysis Methods Available in RevMan [ 28 ]

The MD is the absolute difference in mean values between the groups, and the SMD is the mean difference between groups divided by the standard deviation. When results are presented in the same units, the MD can be used, but when results are presented in different units, the SMD should be used. When the MD is used, the combined units must be shown. A value of “0” for the MD or SMD indicates that the effects of the new treatment method and the existing treatment method are the same. A value lower than “0” means the new treatment method is less effective than the existing method, and a value greater than “0” means the new treatment is more effective than the existing method.

When combining data for dichotomous variables, the OR, risk ratio (RR), or risk difference (RD) can be used. The RR and RD can be used for RCTs, quasi-experimental studies, or cohort studies, and the OR can be used for other case-control studies or cross-sectional studies. However, because the OR is difficult to interpret, using the RR and RD, if possible, is recommended. If the outcome variable is a dichotomous variable, it can be presented as the number needed to treat (NNT), which is the minimum number of patients who need to be treated in the intervention group, compared to the control group, for a given event to occur in at least one patient. Based on Table 3 , in an RCT, if x is the probability of the event occurring in the control group and y is the probability of the event occurring in the intervention group, then x = c/(c + d), y = a/(a + b), and the absolute risk reduction (ARR) = x − y. NNT can be obtained as the reciprocal, 1/ARR.

Calculation of the Number Needed to Treat in the Dichotomous table

Fixed-effect models and random-effect models

In order to analyze effect size, two types of models can be used: a fixed-effect model or a random-effect model. A fixed-effect model assumes that the effect of treatment is the same, and that variation between results in different studies is due to random error. Thus, a fixed-effect model can be used when the studies are considered to have the same design and methodology, or when the variability in results within a study is small, and the variance is thought to be due to random error. Three common methods are used for weighted estimation in a fixed-effect model: 1) inverse variance-weighted estimation 3) , 2) Mantel-Haenszel estimation 4) , and 3) Peto estimation 5) .

A random-effect model assumes heterogeneity between the studies being combined, and these models are used when the studies are assumed different, even if a heterogeneity test does not show a significant result. Unlike a fixed-effect model, a random-effect model assumes that the size of the effect of treatment differs among studies. Thus, differences in variation among studies are thought to be due to not only random error but also between-study variability in results. Therefore, weight does not decrease greatly for studies with a small number of patients. Among methods for weighted estimation in a random-effect model, the DerSimonian and Laird method 6) is mostly used for dichotomous variables, as the simplest method, while inverse variance-weighted estimation is used for continuous variables, as with fixed-effect models. These four methods are all used in Review Manager software (The Cochrane Collaboration, UK), and are described in a study by Deeks et al. [ 31 ] ( Table 2 ). However, when the number of studies included in the analysis is less than 10, the Hartung-Knapp-Sidik-Jonkman method 7) can better reduce the risk of type 1 error than does the DerSimonian and Laird method [ 32 ].

Fig. 3 shows the results of analyzing outcome data using a fixed-effect model (A) and a random-effect model (B). As shown in Fig. 3 , while the results from large studies are weighted more heavily in the fixed-effect model, studies are given relatively similar weights irrespective of study size in the random-effect model. Although identical data were being analyzed, as shown in Fig. 3 , the significant result in the fixed-effect model was no longer significant in the random-effect model. One representative example of the small study effect in a random-effect model is the meta-analysis by Li et al. [ 33 ]. In a large-scale study, intravenous injection of magnesium was unrelated to acute myocardial infarction, but in the random-effect model, which included numerous small studies, the small study effect resulted in an association being found between intravenous injection of magnesium and myocardial infarction. This small study effect can be controlled for by using a sensitivity analysis, which is performed to examine the contribution of each of the included studies to the final meta-analysis result. In particular, when heterogeneity is suspected in the study methods or results, by changing certain data or analytical methods, this method makes it possible to verify whether the changes affect the robustness of the results, and to examine the causes of such effects [ 34 ].

Heterogeneity

Homogeneity test is a method whether the degree of heterogeneity is greater than would be expected to occur naturally when the effect size calculated from several studies is higher than the sampling error. This makes it possible to test whether the effect size calculated from several studies is the same. Three types of homogeneity tests can be used: 1) forest plot, 2) Cochrane’s Q test (chi-squared), and 3) Higgins I 2 statistics. In the forest plot, as shown in Fig. 4 , greater overlap between the confidence intervals indicates greater homogeneity. For the Q statistic, when the P value of the chi-squared test, calculated from the forest plot in Fig. 4 , is less than 0.1, it is considered to show statistical heterogeneity and a random-effect can be used. Finally, I 2 can be used [ 35 ].

I 2 , calculated as shown above, returns a value between 0 and 100%. A value less than 25% is considered to show strong homogeneity, a value of 50% is average, and a value greater than 75% indicates strong heterogeneity.

Even when the data cannot be shown to be homogeneous, a fixed-effect model can be used, ignoring the heterogeneity, and all the study results can be presented individually, without combining them. However, in many cases, a random-effect model is applied, as described above, and a subgroup analysis or meta-regression analysis is performed to explain the heterogeneity. In a subgroup analysis, the data are divided into subgroups that are expected to be homogeneous, and these subgroups are analyzed. This needs to be planned in the predetermined protocol before starting the meta-analysis. A meta-regression analysis is similar to a normal regression analysis, except that the heterogeneity between studies is modeled. This process involves performing a regression analysis of the pooled estimate for covariance at the study level, and so it is usually not considered when the number of studies is less than 10. Here, univariate and multivariate regression analyses can both be considered.

Publication bias

Publication bias is the most common type of reporting bias in meta-analyses. This refers to the distortion of meta-analysis outcomes due to the higher likelihood of publication of statistically significant studies rather than non-significant studies. In order to test the presence or absence of publication bias, first, a funnel plot can be used ( Fig. 5 ). Studies are plotted on a scatter plot with effect size on the x-axis and precision or total sample size on the y-axis. If the points form an upside-down funnel shape, with a broad base that narrows towards the top of the plot, this indicates the absence of a publication bias ( Fig. 5A ) [ 29 , 36 ]. On the other hand, if the plot shows an asymmetric shape, with no points on one side of the graph, then publication bias can be suspected ( Fig. 5B ). Second, to test publication bias statistically, Begg and Mazumdar’s rank correlation test 8) [ 37 ] or Egger’s test 9) [ 29 ] can be used. If publication bias is detected, the trim-and-fill method 10) can be used to correct the bias [ 38 ]. Fig. 6 displays results that show publication bias in Egger’s test, which has then been corrected using the trim-and-fill method using Comprehensive Meta-Analysis software (Biostat, USA).

Funnel plot showing the effect size on the x-axis and sample size on the y-axis as a scatter plot. (A) Funnel plot without publication bias. The individual plots are broader at the bottom and narrower at the top. (B) Funnel plot with publication bias. The individual plots are located asymmetrically.

Funnel plot adjusted using the trim-and-fill method. White circles: comparisons included. Black circles: inputted comparisons using the trim-and-fill method. White diamond: pooled observed log risk ratio. Black diamond: pooled inputted log risk ratio.

Result Presentation

When reporting the results of a systematic review or meta-analysis, the analytical content and methods should be described in detail. First, a flowchart is displayed with the literature search and selection process according to the inclusion/exclusion criteria. Second, a table is shown with the characteristics of the included studies. A table should also be included with information related to the quality of evidence, such as GRADE ( Table 4 ). Third, the results of data analysis are shown in a forest plot and funnel plot. Fourth, if the results use dichotomous data, the NNT values can be reported, as described above.

The GRADE Evidence Quality for Each Outcome

N: number of studies, ROB: risk of bias, PON: postoperative nausea, POV: postoperative vomiting, PONV: postoperative nausea and vomiting, CI: confidence interval, RR: risk ratio, AR: absolute risk.

When Review Manager software (The Cochrane Collaboration, UK) is used for the analysis, two types of P values are given. The first is the P value from the z-test, which tests the null hypothesis that the intervention has no effect. The second P value is from the chi-squared test, which tests the null hypothesis for a lack of heterogeneity. The statistical result for the intervention effect, which is generally considered the most important result in meta-analyses, is the z-test P value.

A common mistake when reporting results is, given a z-test P value greater than 0.05, to say there was “no statistical significance” or “no difference.” When evaluating statistical significance in a meta-analysis, a P value lower than 0.05 can be explained as “a significant difference in the effects of the two treatment methods.” However, the P value may appear non-significant whether or not there is a difference between the two treatment methods. In such a situation, it is better to announce “there was no strong evidence for an effect,” and to present the P value and confidence intervals. Another common mistake is to think that a smaller P value is indicative of a more significant effect. In meta-analyses of large-scale studies, the P value is more greatly affected by the number of studies and patients included, rather than by the significance of the results; therefore, care should be taken when interpreting the results of a meta-analysis.

When performing a systematic literature review or meta-analysis, if the quality of studies is not properly evaluated or if proper methodology is not strictly applied, the results can be biased and the outcomes can be incorrect. However, when systematic reviews and meta-analyses are properly implemented, they can yield powerful results that could usually only be achieved using large-scale RCTs, which are difficult to perform in individual studies. As our understanding of evidence-based medicine increases and its importance is better appreciated, the number of systematic reviews and meta-analyses will keep increasing. However, indiscriminate acceptance of the results of all these meta-analyses can be dangerous, and hence, we recommend that their results be received critically on the basis of a more accurate understanding.

1) http://www.ohri.ca .

2) http://methods.cochrane.org/bias/assessing-risk-bias-included-studies .

3) The inverse variance-weighted estimation method is useful if the number of studies is small with large sample sizes.

4) The Mantel-Haenszel estimation method is useful if the number of studies is large with small sample sizes.

5) The Peto estimation method is useful if the event rate is low or one of the two groups shows zero incidence.

6) The most popular and simplest statistical method used in Review Manager and Comprehensive Meta-analysis software.

7) Alternative random-effect model meta-analysis that has more adequate error rates than does the common DerSimonian and Laird method, especially when the number of studies is small. However, even with the Hartung-Knapp-Sidik-Jonkman method, when there are less than five studies with very unequal sizes, extra caution is needed.

8) The Begg and Mazumdar rank correlation test uses the correlation between the ranks of effect sizes and the ranks of their variances [ 37 ].

9) The degree of funnel plot asymmetry as measured by the intercept from the regression of standard normal deviates against precision [ 29 ].

10) If there are more small studies on one side, we expect the suppression of studies on the other side. Trimming yields the adjusted effect size and reduces the variance of the effects by adding the original studies back into the analysis as a mirror image of each study.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

Systematic Review of the Literature: Best Practices

Affiliations.

• 1 Department of Radiology and Imaging, Rush University Medical Center, 3833, 1653 W Congress Pkwy, Chicago, IL 60612. Electronic address: [email protected].
• 2 Cardiothoracic Imaging, Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, USA.
• 3 Department of Radiology, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL 32224.
• 4 Chief, Cardiothoracic and Emergency Radiology, Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA.
• 5 Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd. Unit 1478, Houston, TX, 77030.
• 6 Department of Medical Imaging, University of Toronto, 263 McCaul Street, 4th Floor, Toronto, ON, M5T 1W7.
• 7 Department of Radiology, Medical University of South Carolina, Charleston, South Carolina, Department of Radiology and Radiological Sciences, Medical University of South Carolina, 96 Jonathan Lucas St. MSC 323, Charleston, SC 29425.
• 8 Department of Radiology and Medical Imaging, University of Virginia Medical Center, 1215 Lee St., Charlottesville, VA 22903.
• PMID: 30442379
• DOI: 10.1016/j.acra.2018.04.025

Reviews of published scientific literature are a valuable resource that can underline best practices in medicine and clarify clinical controversies. Among the various types of reviews, the systematic review of the literature is ranked as the most rigorous since it is a high-level summary of existing evidence focused on answering a precise question. Systematic reviews employ a pre-defined protocol to identify relevant and trustworthy literature. Such reviews can accomplish several critical goals that are not easily achievable with typical empirical studies by allowing identification and discussion of best evidence, contradictory findings, and gaps in the literature. The Association of University Radiologists Radiology Research Alliance Systematic Review Task Force convened to explore the methodology and practical considerations involved in performing a systematic review. This article provides a detailed and practical guide for performing a systematic review and discusses its applications in radiology.

Keywords: effective systematic review; radiology review; research methodology; systematic review.

Copyright © 2018. Published by Elsevier Inc.

PubMed Disclaimer

Similar articles

• How Systematic Review Can Shape Clinical Practice in Radiology. Kim TH, Woo S. Kim TH, et al. AJR Am J Roentgenol. 2024 Jan;222(1):e2329603. doi: 10.2214/AJR.23.29603. Epub 2023 Jul 26. AJR Am J Roentgenol. 2024. PMID: 37493323
• Summarizing systematic reviews: methodological development, conduct and reporting of an umbrella review approach. Aromataris E, Fernandez R, Godfrey CM, Holly C, Khalil H, Tungpunkom P. Aromataris E, et al. Int J Evid Based Healthc. 2015 Sep;13(3):132-40. doi: 10.1097/XEB.0000000000000055. Int J Evid Based Healthc. 2015. PMID: 26360830
• Cervical spondylotic myelopathy: methodological approaches to evaluate the literature and establish best evidence. Skelly AC, Hashimoto RE, Norvell DC, Dettori JR, Fischer DJ, Wilson JR, Tetreault LA, Fehlings MG. Skelly AC, et al. Spine (Phila Pa 1976). 2013 Oct 15;38(22 Suppl 1):S9-18. doi: 10.1097/BRS.0b013e3182a7ebbf. Spine (Phila Pa 1976). 2013. PMID: 24026148
• Decision and Simulation Modeling in Systematic Reviews [Internet]. Kuntz K, Sainfort F, Butler M, Taylor B, Kulasingam S, Gregory S, Mann E, Anderson JM, Kane RL. Kuntz K, et al. Rockville (MD): Agency for Healthcare Research and Quality (US); 2013 Feb. Report No.: 11(13)-EHC037-EF. Rockville (MD): Agency for Healthcare Research and Quality (US); 2013 Feb. Report No.: 11(13)-EHC037-EF. PMID: 23534078 Free Books & Documents. Review.
• Evidence-based medicine, systematic reviews, and guidelines in interventional pain management, part I: introduction and general considerations. Manchikanti L. Manchikanti L. Pain Physician. 2008 Mar-Apr;11(2):161-86. Pain Physician. 2008. PMID: 18354710 Review.
• Identifying and ranking employer brand improvement strategies in post-COVID 19 tourism and hospitality. Bagheri M, Baum T, Mobasheri AA, Nikbakht A. Bagheri M, et al. Tour Hosp Res. 2023 Jul;23(3):391-405. doi: 10.1177/14673584221112607. Epub 2022 Jul 4. Tour Hosp Res. 2023. PMID: 37350846 Free PMC article.
• A bibliometric analysis of systematic reviews and meta-analyses in ophthalmology. Fu Y, Mao Y, Jiang S, Luo S, Chen X, Xiao W. Fu Y, et al. Front Med (Lausanne). 2023 Mar 2;10:1135592. doi: 10.3389/fmed.2023.1135592. eCollection 2023. Front Med (Lausanne). 2023. PMID: 36936241 Free PMC article.
• Developing pre-service teachers' computational thinking: a systematic literature review. Dong W, Li Y, Sun L, Liu Y. Dong W, et al. Int J Technol Des Educ. 2023 Feb 13:1-37. doi: 10.1007/s10798-023-09811-3. Online ahead of print. Int J Technol Des Educ. 2023. PMID: 36816094 Free PMC article.
• Machine learning computational tools to assist the performance of systematic reviews: A mapping review. Cierco Jimenez R, Lee T, Rosillo N, Cordova R, Cree IA, Gonzalez A, Indave Ruiz BI. Cierco Jimenez R, et al. BMC Med Res Methodol. 2022 Dec 16;22(1):322. doi: 10.1186/s12874-022-01805-4. BMC Med Res Methodol. 2022. PMID: 36522637 Free PMC article. Review.
• Review of Microgels for Enhanced Oil Recovery: Properties and Cases of Application. Rozhkova YA, Burin DA, Galkin SV, Yang H. Rozhkova YA, et al. Gels. 2022 Feb 11;8(2):112. doi: 10.3390/gels8020112. Gels. 2022. PMID: 35200492 Free PMC article. Review.
• Search in MeSH

Related information

• Cited in Books

LinkOut - more resources

Full text sources.

• Elsevier Science
• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

Introduction to Systematic Reviews

• Reference work entry
• First Online: 20 July 2022
• pp 2159–2177
• Cite this reference work entry

• Tianjing Li 3 ,
• Ian J. Saldanha 4 &
• Karen A. Robinson 5  

271 Accesses

A systematic review identifies and synthesizes all relevant studies that fit prespecified criteria to answer a research question. Systematic review methods can be used to answer many types of research questions. The type of question most relevant to trialists is the effects of treatments and is thus the focus of this chapter. We discuss the motivation for and importance of performing systematic reviews and their relevance to trialists. We introduce the key steps in completing a systematic review, including framing the question, searching for and selecting studies, collecting data, assessing risk of bias in included studies, conducting a qualitative synthesis and a quantitative synthesis (i.e., meta-analysis), grading the certainty of evidence, and writing the systematic review report. We also describe how to identify systematic reviews and how to assess their methodological rigor. We discuss the challenges and criticisms of systematic reviews, and how technology and innovations, combined with a closer partnership between trialists and systematic reviewers, can help identify effective and safe evidence-based practices more quickly.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Get 10 units per month
• Download Article/Chapter or Ebook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Durable hardcover edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

What Is the Difference Between a Systematic Review and a Meta-analysis?

Systematic Reviewing

AHRQ (2015) Methods guide for effectiveness and comparative effectiveness reviews. Available from https://effectivehealthcare.ahrq.gov/products/cer-methods-guide/overview . Accessed on 27 Oct 2019

Andersen MZ, Gülen S, Fonnes S, Andresen K, Rosenberg J (2020) Half of Cochrane reviews were published more than two years after the protocol. J Clin Epidemiol 124:85–93. https://doi.org/10.1016/j.jclinepi.2020.05.011

Article   Google Scholar  

Berkman ND, Lohr KN, Ansari MT, Balk EM, Kane R, McDonagh M, Morton SC, Viswanathan M, Bass EB, Butler M, Gartlehner G, Hartling L, McPheeters M, Morgan LC, Reston J, Sista P, Whitlock E, Chang S (2015) Grading the strength of a body of evidence when assessing health care interventions: an EPC update. J Clin Epidemiol 68(11):1312–1324

Borah R, Brown AW, Capers PL, Kaiser KA (2017) Analysis of the time and workers needed to conduct systematic reviews of medical interventions using data from the PROSPERO registry. BMJ Open 7(2):e012545. https://doi.org/10.1136/bmjopen-2016-012545

Chalmers I, Bracken MB, Djulbegovic B, Garattini S, Grant J, Gülmezoglu AM, Howells DW, Ioannidis JP, Oliver S (2014) How to increase value and reduce waste when research priorities are set. Lancet 383(9912):156–165. https://doi.org/10.1016/S0140-6736(13)62229-1

Clarke M, Chalmers I (1998) Discussion sections in reports of controlled trials published in general medical journals: islands in search of continents? JAMA 280(3):280–282

Cooper NJ, Jones DR, Sutton AJ (2005) The use of systematic reviews when designing studies. Clin Trials 2(3):260–264

Djulbegovic B, Kumar A, Magazin A, Schroen AT, Soares H, Hozo I, Clarke M, Sargent D, Schell MJ (2011) Optimism bias leads to inconclusive results-an empirical study. J Clin Epidemiol 64(6):583–593. https://doi.org/10.1016/j.jclinepi.2010.09.007

Elliott JH, Synnot A, Turner T, Simmonds M, Akl EA, McDonald S, Salanti G, Meerpohl J, MacLehose H, Hilton J, Tovey D, Shemilt I, Thomas J (2017) Living systematic review network. Living systematic review: 1. Introduction-the why, what, when, and how. J Clin Epidemiol 91:23–30

Equator Network. Reporting guidelines for systematic reviews. Available from https://www.equator-network.org/?post_type=eq_guidelines&eq_guidelines_study_design=systematic-reviews-and-meta-analyses&eq_guidelines_clinical_specialty=0&eq_guidelines_report_section=0&s=+ . Accessed 9 Mar 2020

Garner P, Hopewell S, Chandler J, MacLehose H, Schünemann HJ, Akl EA, Beyene J, Chang S, Churchill R, Dearness K, Guyatt G, Lefebvre C, Liles B, Marshall R, Martínez García L, Mavergames C, Nasser M, Qaseem A, Sampson M, Soares-Weiser K, Takwoingi Y, Thabane L, Trivella M, Tugwell P, Welsh E, Wilson EC, Schünemann HJ (2016) Panel for updating guidance for systematic reviews (PUGs). When and how to update systematic reviews: consensus and checklist. BMJ 354:i3507. https://doi.org/10.1136/bmj.i3507 . Erratum in: BMJ 2016 Sep 06 354:i4853

Guyatt G, Oxman AD, Akl EA, Kunz R, Vist G, Brozek J, Norris S, Falck-Ytter Y, Glasziou P, DeBeer H, Jaeschke R, Rind D, Meerpohl J, Dahm P, Schünemann HJ (2011) GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables. J Clin Epidemiol 64(4):383–394

Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (eds) (2019a) Cochrane handbook for systematic reviews of interventions, 2nd edn. Wiley, Chichester

Google Scholar  

Higgins JPT, Lasserson T, Chandler J, Tovey D, Thomas J, Flemyng E, Churchill R (2019b) Standards for the conduct of new Cochrane intervention reviews. In: JPT H, Lasserson T, Chandler J, Tovey D, Thomas J, Flemyng E, Churchill R (eds) Methodological expectations of Cochrane intervention reviews. Cochrane, London

IOM (2011) Committee on standards for systematic reviews of comparative effectiveness research, board on health care services. In: Eden J, Levit L, Berg A, Morton S (eds) Finding what works in health care: standards for systematic reviews. National Academies Press, Washington, DC

Jonnalagadda SR, Goyal P, Huffman MD (2015) Automating data extraction in systematic reviews: a systematic review. Syst Rev 4:78

Krnic Martinic M, Pieper D, Glatt A, Puljak L (2019) Definition of a systematic review used in overviews of systematic reviews, meta-epidemiological studies and textbooks. BMC Med Res Methodol 19(1):203. Published 4 Nov 2019. https://doi.org/10.1186/s12874-019-0855-0

Lasserson TJ, Thomas J, Higgins JPT (2019) Chapter 1: Starting a review. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (eds) Cochrane handbook for systematic reviews of interventions version 6.0 (updated July 2019). Cochrane. Available from www.training.cochrane.org/handbook

Lau J, Antman EM, Jimenez-Silva J, Kupelnick B, Mosteller F, Chalmers TC (1992) Cumulative meta-analysis of therapeutic trials for myocardial infarction. N Engl J Med 327(4):248–254

Lau J (2019) Editorial: systematic review automation thematic series. Syst Rev 8(1):70. Published 11 Mar 2019. https://doi.org/10.1186/s13643-019-0974-z

Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, Clarke M, Devereaux PJ, Kleijnen J, Moher D (2009) The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. PLoS Med 6(7):e1000100. https://doi.org/10.1371/journal.pmed.1000100

Lund H, Brunnhuber K, Juhl C, Robinson K, Leenaars M, Dorch BF, Jamtvedt G, Nortvedt MW, Christensen R, Chalmers I (2016) Towards evidence based research. BMJ 355:i5440. https://doi.org/10.1136/bmj.i5440

Marshall IJ, Noel-Storr A, Kuiper J, Thomas J, Wallace BC (2018) Machine learning for identifying randomized controlled trials: an evaluation and practitioner’s guide. Res Synth Methods 9(4):602–614. https://doi.org/10.1002/jrsm.1287

Michelson M, Reuter K (2019) The significant cost of systematic reviews and meta-analyses: a call for greater involvement of machine learning to assess the promise of clinical trials. Contemp Clin Trials Commun 16:100443. https://doi.org/10.1016/j.conctc.2019.100443 . Erratum in: Contemp Clin Trials Commun 2019 16:100450

Moher D, Liberati A, Tetzlaff J (2009) Altman DG; PRISMA group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med 151(4):264–269. W64

Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, Shekelle P, Stewart LA, PRISMA-P Group (2015) Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev 4(1):1. https://doi.org/10.1186/2046-4053-4-1

NIHR HTA Stage 1 guidance notes. Available from https://www.nihr.ac.uk/documents/hta-stage-1-guidance-notes/11743 ; Accessed 10 Mar 2020

Page MJ, Shamseer L, Altman DG, Tetzlaff J, Sampson M, Tricco AC, Catalá-López F, Li L, Reid EK, Sarkis-Onofre R, Moher D (2016) Epidemiology and reporting characteristics of systematic reviews of biomedical research: a cross-sectional study. PLoS Med 13(5):e1002028. https://doi.org/10.1371/journal.pmed.1002028

Page MJ, Higgins JPT, Sterne JAC (2019) Chapter 13: assessing risk of bias due to missing results in a synthesis. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ et al (eds) Cochrane handbook for systematic reviews of interventions, 2nd edn. Wiley, Chichester, pp 349–374

Chapter   Google Scholar  

Robinson KA (2009) Use of prior research in the justification and interpretation of clinical trials. Johns Hopkins University

Robinson KA, Goodman SN (2011) A systematic examination of the citation of prior research in reports of randomized, controlled trials. Ann Intern Med 154(1):50–55. https://doi.org/10.7326/0003-4819-154-1-201101040-00007

Rouse B, Cipriani A, Shi Q, Coleman AL, Dickersin K, Li T (2016) Network meta-analysis for clinical practice guidelines – a case study on first-line medical therapies for primary open-angle glaucoma. Ann Intern Med 164(10):674–682. https://doi.org/10.7326/M15-2367

Saldanha IJ, Lindsley K, Do DV et al (2017) Comparison of clinical trial and systematic review outcomes for the 4 most prevalent eye diseases. JAMA Ophthalmol 135(9):933–940. https://doi.org/10.1001/jamaophthalmol.2017.2583

Shea BJ, Grimshaw JM, Wells GA, Boers M, Andersson N, Hamel C, Porter AC, Tugwell P, Moher D, Bouter LM (2007) Development of AMSTAR: a measurement tool to assess the methodological quality of systematic reviews. BMC Med Res Methodol 7:10

Shea BJ, Reeves BC, Wells G, Thuku M, Hamel C, Moran J, Moher D, Tugwell P, Welch V, Kristjansson E, Henry DA (2017) AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ 358:j4008. https://doi.org/10.1136/bmj.j4008

Shojania KG, Sampson M, Ansari MT, Ji J, Doucette S, Moher D (2007) How quickly do systematic reviews go out of date? A survival analysis. Ann Intern Med 147(4):224–233

Sterne JA, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, Henry D, Altman DG, Ansari MT, Boutron I, Carpenter JR, Chan AW, Churchill R, Deeks JJ, Hróbjartsson A, Kirkham J, Jüni P, Loke YK, Pigott TD, Ramsay CR, Regidor D, Rothstein HR, Sandhu L, Santaguida PL, Schünemann HJ, Shea B, Shrier I, Tugwell P, Turner L, Valentine JC, Waddington H, Waters E, Wells GA, Whiting PF, Higgins JP (2016) ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ 355:i4919. https://doi.org/10.1136/bmj.i4919

Sterne JAC, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, Cates CJ, Cheng HY, Corbett MS, Eldridge SM, Emberson JR, Hernán MA, Hopewell S, Hróbjartsson A, Junqueira DR, Jüni P, Kirkham JJ, Lasserson T, Li T, McAleenan A, Reeves BC, Shepperd S, Shrier I, Stewart LA, Tilling K, White IR, Whiting PF, Higgins JPT (2019) RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ 366:l4898. https://doi.org/10.1136/bmj.l4898

Thomas J, Kneale D, McKenzie JE, Brennan SE, Bhaumik S (2019) Chapter 2: determining the scope of the review and the questions it will address. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (eds) Cochrane handbook for systematic reviews of interventions version 6.0 (updated July 2019). Cochrane. Available from www.training.cochrane.org/handbook

USPSTF U.S. Preventive Services Task Force Procedure Manual (2017). Available from: https://www.uspreventiveservicestaskforce.org/uspstf/sites/default/files/inline-files/procedure-manual2017_update.pdf . Accessed 21 May 2020

Whitaker (2015) UCSF guides: systematic review: when will i be finished? https://guides.ucsf.edu/c.php?g=375744&p=3041343 , Accessed 13 May 2020

Whiting P, Savović J, Higgins JP, Caldwell DM, Reeves BC, Shea B, Davies P, Kleijnen J (2016) Churchill R; ROBIS group. ROBIS: a new tool to assess risk of bias in systematic reviews was developed. J Clin Epidemiol 69:225–234. https://doi.org/10.1016/j.jclinepi.2015.06.005

Download references

Author information

Authors and affiliations.

Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

Tianjing Li

Department of Health Services, Policy, and Practice and Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA

Ian J. Saldanha

Department of Medicine, Johns Hopkins University, Baltimore, MD, USA

Karen A. Robinson

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Tianjing Li .

Editor information

Editors and affiliations.

Department of Surgery, Division of Surgical Oncology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

Steven Piantadosi

Department of Epidemiology, School of Public Health, Johns Hopkins University, Baltimore, MD, USA

Curtis L. Meinert

Section Editor information

Department of Epidemiology, University of Colorado Denver Anschutz Medical Campus, Aurora, CO, USA

The Johns Hopkins Center for Clinical Trials and Evidence Synthesis, Johns Hopkins University, Baltimore, MD, USA

Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this entry

Cite this entry.

Li, T., Saldanha, I.J., Robinson, K.A. (2022). Introduction to Systematic Reviews. In: Piantadosi, S., Meinert, C.L. (eds) Principles and Practice of Clinical Trials. Springer, Cham. https://doi.org/10.1007/978-3-319-52636-2_194

Download citation

DOI : https://doi.org/10.1007/978-3-319-52636-2_194

Published : 20 July 2022

Publisher Name : Springer, Cham

Print ISBN : 978-3-319-52635-5

Online ISBN : 978-3-319-52636-2

eBook Packages : Mathematics and Statistics Reference Module Computer Science and Engineering

Share this entry

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Literature Review: Systematic literature reviews

• Traditional or narrative literature reviews
• Scoping Reviews
• Systematic literature reviews
• Annotated bibliography
• Keeping up to date with literature
• Finding a thesis
• Evaluating sources and critical appraisal of literature
• Managing and analysing your literature
• Further reading and resources

Systematic reviews

Systematic and systematic-like reviews

Charles Sturt University library has produced a comprehensive guide for Systematic and systematic-like literature reviews. A comprehensive systematic literature review can often take a team of people up to a year to complete. This guide provides an overview of the steps required for systematic reviews:

• Identify your research question
• Develop your protocol
• Conduct systematic searches (including the search strategy, text mining, choosing databases, documenting and reviewing
• Critical appraisal
• Data extraction and synthesis
• Writing and publishing .
• Systematic and systematic-like reviews Library Resource Guide

Systematic literature review

A systematic literature review (SLR) identifies, selects and critically appraises research in order to answer a clearly formulated question (Dewey, A. & Drahota, A. 2016). The systematic review should follow a clearly defined protocol or plan where the criteria is clearly stated before the review is conducted. It is a comprehensive, transparent search conducted over multiple databases and grey literature that can be replicated and reproduced by other researchers. It involves planning a well thought out search strategy which has a specific focus or answers a defined question. The review identifies the type of information searched, critiqued and reported within known timeframes. The search terms, search strategies (including database names, platforms, dates of search) and limits all need to be included in the review.

Pittway (2008) outlines seven key principles behind systematic literature reviews

• Transparency
• Integration
• Accessibility

Systematic literature reviews originated in medicine and are linked to evidence based practice. According to Grant & Booth (p 91, 2009) "the expansion in evidence-based practice has lead to an increasing variety of review types". They compare and contrast 14 review types, listing the strengths and weaknesses of each review. 

Tranfield et al (2003) discusses the origins of the evidence-based approach to undertaking a literature review and its application to other disciplines including management and science.

References and additional resources

Dewey, A. & Drahota, A. (2016) Introduction to systematic reviews: online learning module Cochrane Training   https://training.cochrane.org/interactivelearning/module-1-introduction-conducting-systematic-reviews

Gough, David A., David Gough, Sandy Oliver, and James Thomas. An Introduction to Systematic Reviews. Systematic Reviews. London: SAGE, 2012.

Grant, M. J. & Booth, A. (2009) A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information & Libraries Journal 26(2), 91-108

Munn, Z., Peters, M. D. J., Stern, C., Tufanaru, C., McArthur, A., & Aromataris, E. (2018). Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol, 18(1), 143. https://doi.org/10.1186/s12874-018-0611-x 

Pittway, L. (2008) Systematic literature reviews. In Thorpe, R. & Holt, R. The SAGE dictionary of qualitative management research. SAGE Publications Ltd doi:10.4135/9780857020109

Tranfield, D., Denyer, D & Smart, P. (2003) Towards a methodology for developing evidence-informed management knowledge by means of systematic review . British Journal of Management 14 (3), 207-222

Evidence based practice - an introduction : Literature reviews/systematic reviews

Evidence based practice - an introduction is a library guide produced at CSU Library for undergraduates. The information contained in the guide is also relevant for post graduate study and will help you to understand the types of research and levels of evidence required to conduct evidence based research.

• Evidence based practice an introduction
• << Previous: Scoping Reviews
• Next: Annotated bibliography >>
• Last Updated: Jun 3, 2024 9:25 AM
• URL: https://libguides.csu.edu.au/review

Charles Sturt University is an Australian University, TEQSA Provider Identification: PRV12018. CRICOS Provider: 00005F.