Introduction
Systematic reviews are a type of literature review that uses a rigorous and transparent methodology to identify, appraise, and synthesize all relevant studies on a specific topic. They are an essential tool for evidence-based practice, as they provide a comprehensive and reliable overview of the available evidence. Key characteristics of a systematic review include a clearly defined question with inclusion and exclusion criteria, a rigorous and systematic search of the literature, critical appraisal of included studies, data extraction and management, analysis and interpretation of results, and a report for publication. One of the key steps in conducting a systematic review is extracting study characteristics from the included studies. This involves systematically collecting data from each study on a range of factors, such as the study design, participants, interventions, and outcomes. This information is then used to synthesize the findings of the review and draw conclusions about the effectiveness of interventions or the prevalence of a condition.
A systematic and extensive search is conducted to identify all relevant published and unpublished literature. It is crucial to document the search strategy, including the databases searched, the search terms used, and the time frame of the search. This documentation ensures transparency and allows others to replicate the search.
Study Characteristic Description Study design This includes the type of study (e.g., randomized controlled trial, cohort study, case-control study), the methods used to collect data, and the study setting. It also includes information related to the design and methods of the research study, such as the use of randomization and allocation concealment in RCTs. Risk of bias This refers to the potential for systematic errors in the study design or conduct that could lead to misleading results. Participant characteristics This includes information about the participants in the study, such as their age, sex, ethnicity, and any relevant medical conditions. Interventions This includes information about the interventions being studied, such as the type of intervention, the dosage, and the duration. Outcomes This includes information about the outcomes that were measured in the study, such as the primary and secondary outcomes, and the time points at which they were measured.
In addition to these core characteristics, systematic reviews may also extract data on other factors, such as the funding source of the study, the country in which the study was conducted, and the publication date.
| Study Characteristic | Description |
|---|---|
| Study design | This includes the type of study (e.g., randomized controlled trial, cohort study, case-control study), the methods used to collect data, and the study setting. It also includes information related to the design and methods of the research study, such as the use of randomization and allocation concealment in RCTs. |
| Risk of bias | This refers to the potential for systematic errors in the study design or conduct that could lead to misleading results. |
| Participant characteristics | This includes information about the participants in the study, such as their age, sex, ethnicity, and any relevant medical conditions. |
| Interventions | This includes information about the interventions being studied, such as the type of intervention, the dosage, and the duration. |
| Outcomes | This includes information about the outcomes that were measured in the study, such as the primary and secondary outcomes, and the time points at which they were measured. |
In addition to these core characteristics, systematic reviews may also extract data on other factors, such as the funding source of the study, the country in which the study was conducted, and the publication date 3.
Data extraction forms are used to ensure that data is collected systematically and consistently across all studies included in a systematic review. These forms typically include a list of the study characteristics to be extracted, as well as clear instructions on how to extract the data. There are a number of different data extraction forms available, and the choice of form will depend on the specific needs of the review. Some commonly used forms include:
- Cochrane data collection form for interventions: This form is designed for use in systematic reviews of interventions, and it includes sections for extracting data on the study design, participants, interventions, outcomes, and risk of bias.
- Data Extraction Template (Cochrane Working Group): This is a comprehensive template for systematic reviews developed by the Cochrane Haematological Malignancies Group.
- JBI Manual for Evidence Synthesis: This manual provides guidance on conducting different types of systematic reviews, and it includes a number of different data extraction forms that can be used.
In addition to these established frameworks, supplementary resources for data extraction forms have been collected by the George Washington University Libraries. Many researchers also prefer to use Excel to collect data.
Developing a data extraction form is a crucial step in conducting a systematic review. The form should be tailored to the specific research question and the type of studies being included in the review. Here are some steps to guide the development of a data extraction form:
- Collect the full text of included articles: Ensure you have access to the full text of all articles that meet the inclusion criteria for the systematic review.
- Choose the information to collect: Based on the research question and the type of systematic review, determine the specific study characteristics that need to be extracted from each article. This may include details about the study design, participants, interventions, outcomes, and any other relevant factors.
- Choose a method for collecting the data: Decide on the format for collecting the data. This could be a paper-based form, an electronic spreadsheet (e.g., Excel), or a dedicated software program designed for systematic reviews. When using Excel, consider the PIECES approach, a structured method designed by Margaret Foster at Texas A&M for managing the screening and data extraction stages.
- Create the data extraction table: Develop a structured table or form with clear headings and fields for each piece of information to be extracted. Ensure the form is user-friendly and allows for easy data entry and organization.
- Test the data collection table (optional): Before starting the full data extraction process, it is advisable to pilot test the form on a small sample of included studies. This helps identify any ambiguities or areas for improvement in the form.
It's important to remember that developing a data extraction form can be an iterative process. Pilot testing and revisions may be necessary to refine the form and ensure it captures the necessary information effectively.
While developing a data extraction form is crucial, various tools and software programs can assist with data extraction for systematic reviews. These tools can help streamline the process, improve accuracy, and facilitate collaboration among reviewers. Some popular options include:
- Covidence: This online software is designed to improve the efficiency and experience of conducting systematic reviews. It offers features for screening articles, extracting data, and assessing the risk of bias.
- DistillerSR: This is another systematic review software that helps manage, track, and streamline the screening, data extraction, and reporting processes. It offers features such as automatic generation of the PRISMA flowchart.
- EPPI-Reviewer: This online tool is designed for research synthesis and provides support for data extraction and management.
- AHRQ's SRDR tool: This free web-based tool helps with data extraction and management for systematic reviews and meta-analyses. It also serves as an open and searchable archive of systematic reviews and their data.
- RevMan: This free software is used to manage Cochrane reviews and provides tools for creating data extraction forms and conducting meta-analyses.
- Systematic Review Data Repository (SRDR): This web-based tool is specifically designed for extracting and managing data for systematic reviews and meta-analyses. It also serves as an open and searchable archive of systematic reviews and their data.
- Online survey forms: Tools like Qualtrics, RedCAP, or Survey Monkey can also be used to create customized data extraction forms.
Ensuring the accuracy and reliability of data extraction is critical for the validity of a systematic review. Here are some strategies to enhance the quality of data extraction:
- Independent extraction by multiple reviewers: Having two or more reviewers independently extract data from each study helps minimize errors and reduce bias. Discrepancies between reviewers can be resolved through discussion and consensus.
- Use of standardized data extraction forms: Using a structured form with clear instructions ensures consistency in data extraction across all studies.
- Pilot testing of the data extraction form: Pilot testing the form on a small sample of studies helps identify any ambiguities or areas for improvement.
- Training of reviewers: Providing adequate training to reviewers on the data extraction process and the use of the form helps ensure consistency and accuracy.
- Regular meetings and discussions: Regular meetings among reviewers allow for discussion of any challenges or uncertainties encountered during data extraction and help ensure consistency in decision-making.
- Documentation of any changes to the process: Any modifications made to the data extraction form or process should be clearly documented to maintain transparency and reproducibility.
- Focus on key information: When extracting data, it's essential to focus on the key information relevant to the research question or objective of the systematic review. This helps ensure that the findings and conclusions drawn from the review are accurate and directly related to the research question, minimizing the risk of bias.
- Quality assessment: In addition to data extraction, systematic reviews often involve assessing the quality of the included studies. For quantitative studies, the GRADE system can be used to evaluate the quality of evidence. For qualitative studies, rigor is assessed, although this can be more subjective.
It's important to acknowledge that data extraction can be challenging. Researchers may encounter issues such as missing data, inconsistent reporting in the primary studies, or the need to contact study authors for clarification. Developing strategies to address these challenges is crucial for ensuring the accuracy and completeness of the extracted data.
Extracting study characteristics is a fundamental step in conducting a systematic review. By following a rigorous and transparent process, using appropriate tools and software, and ensuring the accuracy and reliability of data extraction, researchers can contribute to the production of high-quality systematic reviews that inform evidence-based practice and decision-making. The extracted data enables researchers to compare and analyze studies, leading to the synthesis of findings and the drawing of conclusions. Systematic reviews have a significant impact on various fields, including the development of public health policies, resource allocation decisions, clinical practices, and the implementation of evidence-based interventions for diseases and illnesses. When planning a statistical analysis or meta-analysis, consulting with a biostatistician is essential to ensure the appropriate data is collected and analyzed correctly. By adhering to these principles, researchers can ensure the quality and reliability of systematic reviews, ultimately contributing to a more robust and reliable evidence base for decision-making in various fields.