Extracting Study Characteristics

Learning Objectives

By the end of this comprehensive tutorial, you will be able to:

  • Understand the critical role of extracting study characteristics in systematic reviews.
  • Identify key study characteristics to extract for different types of research.
  • Implement best practices and methodologies for accurate and consistent data extraction.
  • Utilize advanced techniques to handle complex study designs and data types.
  • Enhance the overall quality and reliability of your systematic reviews through meticulous data extraction.

Introduction

Extracting study characteristics is a fundamental step in conducting a systematic review. It involves systematically collecting detailed information about each included study to facilitate critical appraisal, data synthesis, and interpretation of results.

In this tutorial, we will delve deep into the methodologies, best practices, and strategies for effectively extracting study characteristics. Whether you are a novice or an experienced reviewer, mastering these skills is essential for ensuring the accuracy and credibility of your systematic reviews.

Extracting study characteristics serves several crucial purposes:

  • Facilitates Comparison: Allows for the comparison of studies based on their methodologies, populations, interventions, and outcomes.
  • Enables Quality Assessment: Provides necessary information for assessing the risk of bias and quality of each study.
  • Supports Data Synthesis: Ensures that data is appropriately categorized and ready for qualitative or quantitative synthesis.
  • Enhances Transparency: Detailed extraction promotes transparency and reproducibility of the review process.

Without meticulous extraction, valuable insights may be missed, and the validity of the review's conclusions can be compromised.

Key Study Characteristics to Extract

When extracting study characteristics, it's essential to collect comprehensive information that covers all aspects of each study. This includes, but is not limited to:

  • Study Identification: Author(s), year of publication, journal, DOI or PMID.
  • Study Design: Type of study (e.g., randomized controlled trial, cohort study), methodological details.
  • Population Characteristics: Inclusion and exclusion criteria, sample size, demographic information.
  • Intervention and Comparison: Details of interventions, comparisons, duration, and delivery methods.
  • Outcomes Measured: Primary and secondary outcomes, measurement tools used, time points.
  • Results: Numerical data, effect sizes, statistical significance, confidence intervals.
  • Funding Sources and Conflicts of Interest: Any potential biases related to funding or author affiliations.

Essential Elements:
  • Authors: Full names of all authors.
  • Title: Full title of the study.
  • Journal: Name of the journal or source of publication.
  • Year of Publication: The year in which the study was published.
  • Identifiers: DOI, PMID, or other relevant identifiers.
Best Practices:
  • Ensure accurate spelling of authors' names to facilitate reference management.
  • Record full bibliographic details for proper citation.
  • Include hyperlinks to the study when possible for easy access.
Example:

Smith, J.A., & Doe, A.B. (2021). The effects of X on Y: A randomized controlled trial. Journal of Clinical Research, 35(4), 123-130. DOI:10.1000/jcr.2021.12345

Types of Study Designs:
  • Randomized Controlled Trials (RCTs): Participants are randomly assigned to intervention or control groups.
  • Cohort Studies: Follow a group over time to assess outcomes.
  • Case-Control Studies: Compare participants with a condition to those without.
  • Cross-Sectional Studies: Analyze data from a population at a single point in time.
  • Qualitative Studies: Explore phenomena through non-numeric data, such as interviews.
Key Details to Extract:
  • Randomization methods and allocation concealment (for RCTs).
  • Blinding of participants and personnel.
  • Follow-up duration and any losses to follow-up.
  • Ethical considerations and approvals.
Example Data Points:
  • Study Type: Randomized Controlled Trial
  • Randomization: Computer-generated allocation
  • Blinding: Double-blind (participants and assessors)
  • Follow-up Period: 12 months

Key Information to Collect:
  • Sample Size: Total number of participants and numbers in each group.
  • Inclusion Criteria: Criteria used to select participants for the study.
  • Exclusion Criteria: Criteria that disqualified potential participants.
  • Demographics: Age, gender, ethnicity, socioeconomic status.
  • Baseline Characteristics: Health status, comorbidities, prior treatments.
Best Practices:
  • When possible, extract data in a tabular format for clarity.
  • Note any significant differences between groups at baseline.
  • Record how participants were recruited (e.g., hospital patients, community volunteers).
Example Table:
Characteristic Intervention Group (n=50) Control Group (n=50)
Mean Age (years) 45.2 ± 10.1 46.5 ± 9.8
Gender (% female) 52% 55%
Baseline Disease Severity Mild: 20%, Moderate: 60%, Severe: 20% Mild: 18%, Moderate: 62%, Severe: 20%

Details to Extract for Interventions:
  • Description of the Intervention: Precise details of what the intervention entailed.
  • Dosage and Frequency: If applicable (e.g., medication dosage).
  • Mode of Delivery: How the intervention was administered (e.g., oral, intravenous, face-to-face counseling).
  • Duration: Length of the intervention period.
  • Compliance Measures: How adherence was assessed and reported.
Details for Comparators:
  • Control Conditions: Placebo, standard care, no intervention, alternative treatments.
  • Equivalence of Treatment Setting: Ensuring comparable settings between groups.
Best Practices:
  • Use clear, specific language to describe interventions.
  • Include any co-interventions or additional treatments provided.
  • Note any deviations from the planned interventions.
Example:

Intervention Group: Participants received Drug X at a dosage of 10 mg orally once daily for 12 weeks, along with standard care.

Control Group: Participants received a matched placebo orally once daily for 12 weeks, along with standard care.

Key Information:
  • Primary Outcomes: The main outcomes the study was designed to assess.
  • Secondary Outcomes: Additional outcomes of interest.
  • Outcome Definitions: Clear definitions, including measurement scales and units.
  • Time Points: When outcomes were measured (e.g., baseline, 6 weeks, 12 weeks).
  • Measurement Tools: Instruments or methods used to assess outcomes (e.g., questionnaires, laboratory tests).
Best Practices:
  • Record outcome measures exactly as reported in the study.
  • Note any validation of measurement tools.
  • Include information on any non-standardized or novel outcome measures.
Example:
  • Primary Outcome: Change in systolic blood pressure from baseline to 12 weeks, measured using an automated cuff.
  • Secondary Outcomes:
    • Total cholesterol levels at 12 weeks.
    • Incidence of adverse events during the study period.

Data to Extract:
  • Numerical Data: Means, medians, proportions, rates, etc.
  • Effect Sizes: Risk ratios, odds ratios, hazard ratios, mean differences.
  • Statistical Significance: p-values, confidence intervals.
  • Adjustments: Any adjustments made for confounders.
  • Subgroup Analyses: Results for specific subgroups if reported.
Best Practices:
  • Extract data exactly as reported, noting any calculations performed.
  • Be cautious of units and scales; convert if necessary but document conversions.
  • Include notes on any data imputation or handling of missing data by the study authors.
Example Table:
Outcome Intervention Group Control Group Effect Size p-value
Change in Systolic BP (mmHg) -15.2 ± 5.1 -5.3 ± 4.8 Mean Difference: -9.9 (95% CI: -11.5 to -8.3) <0.001
Total Cholesterol (mg/dL) 180.5 ± 30.2 190.7 ± 28.9 Mean Difference: -10.2 (95% CI: -18.5 to -2.0) 0.015

Why It's Important:

Understanding the funding sources and potential conflicts of interest helps assess the risk of bias due to external influences on the study outcomes.

Information to Extract:
  • Funding Sources: Details of any financial support received.
  • Author Conflicts of Interest: Any declared personal or financial interests related to the study.
  • Industry Involvement: Role of any sponsoring organizations in study design, data collection, analysis, or publication.
Best Practices:
  • Record statements exactly as reported in the publication.
  • Note if no conflicts of interest were declared.
  • If information is missing, consider contacting authors or noting it in limitations.
Example:

The study was funded by Grant XYZ from the National Institutes of Health. The authors declare no conflicts of interest.

Best Practices for Data Extraction

To ensure accuracy and consistency in your data extraction process, consider the following best practices:

Creating and using standardized extraction forms helps maintain consistency across all studies and reduces the risk of errors.

Tips:
  • Develop a template that includes all necessary fields based on your research question and protocol.
  • Use validation rules to ensure data is entered in the correct format.
  • Consider utilizing software tools that support standardized data extraction.

Standardization facilitates easier synthesis and comparison of data during later stages of your review.

Ensuring that all team members are trained and calibrated enhances the reliability of the data extraction process.

Steps to Implement:
  • Conduct training sessions to familiarize reviewers with the extraction form and protocols.
  • Perform a pilot test where all reviewers extract data from the same studies and compare results.
  • Discuss discrepancies to align understanding and approach.

Regular calibration meetings help maintain consistency, especially in reviews involving complex or subjective data.

Having two reviewers independently extract data from each study reduces the risk of errors and biases.

Advantages:
  • Increases accuracy through cross-verification of data.
  • Identifies and resolves discrepancies promptly.
  • Enhances the credibility of the review findings.

Ensure that disagreements are documented and resolved through discussion or consultation with a third reviewer.

Transparent documentation is essential for the reproducibility and transparency of your systematic review.

Recommendations:
  • Record the reasons for any judgments or assumptions made during extraction.
  • Keep a log of any issues encountered and how they were resolved.
  • Include notes on any deviations from the protocol.

This documentation is valuable for both the review team and external stakeholders who may assess the quality of your review.

Implementing quality control measures ensures the ongoing accuracy and reliability of the data extraction process.

Strategies:
  • Randomly select extracted data to verify against the original studies.
  • Use data validation tools and software features to detect inconsistencies.
  • Regularly review extraction practices and update protocols as needed.

Quality control helps identify systemic issues early, allowing for timely corrections.

Conclusion

Accurate and thorough extraction of study characteristics is indispensable for the success of a systematic review. By meticulously collecting and documenting detailed information, you lay a solid foundation for data synthesis, critical appraisal, and the drawing of meaningful conclusions.

Implementing best practices and leveraging appropriate tools can significantly enhance the efficiency and reliability of your data extraction process.

EviSynth offers customizable data extraction tools and collaborative features to streamline your systematic review workflow. Explore EviSynth's features to enhance the quality and efficiency of your research.

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