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How Randomized Controlled Trials are Used in
Meta-Analysis
An Academic presentation by Dr. Nancy Agnes,
Head, Technical Operations, Pubrica Group
www.pubrica.com Email sales_at_pubrica.com
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Introduction
Randomized controlled trialsfor efficacy
research

• Randomized controlled trials (RCTs) are
  prospective studies that assess the efficacy of a
  novel intervention or treatment.
• Although no research is likely to show causation,
  randomization eliminates bias and provides a
  rigorous technique for examining cause-effect
  correlations between an intervention and result.
• This is because the process of randomization
  balances participant characteristics (both
  observable and unobserved) between groups,
  allowing any variations in result to be
  attributed to the research intervention. This is
  not achievable with any other research design.

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RCTs are Used in Meta-Analysis

• Randomized Controlled Trials (RCTs) are a
  commonly used research design in medical and
  scientific studies to assess the effectiveness of
  interventions or treatments. Meta-analysis, on
  the other hand, is a statistical technique used
  to combine and analyze the results of multiple
  studies on a particular topic to draw more robust
  conclusions.

• To know more about meta-analysis services, check
  our study guide on How to do a meta-analysis for
  a manuscript.

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Here's how RCTs are used in the context of
meta-analysis
Inclusion Criteria

• Researchers establish specific inclusion and
  exclusion criteria to determine which RCTs are
  eligible for inclusion in the meta-analysis.
  Common criteria might include the type of
  intervention, the patient population, the outcome
  measures, and the study design.

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Identifying Relevant RCTs

• The first step in a meta-analysis is to identify
  all relevant RCTs that have been conducted on a
  specific topic. This is typically done through a
  systematic literature review, where researchers
  search various databases and sources to find all
  available RCTs.

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Effect Size Calculation

• One of the key steps in meta-analysis is
  calculating the effect size for each RCT. The
  effect size is a measure of the magnitude of the
  treatment effect, and it allows for the
  comparison of results across different studies.
  Common effect size measures include odds ratios,
  risk ratios, or mean differences, depending on
  the type of data and outcome being studied.

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Data Extraction

• For each eligible RCT, relevant data is
  extracted. This includes information about the
  study design, sample size, treatment group,
  control group, and the results in terms of the
  outcome of interest. This data collection is
  typically recorded in a standardized format.

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Statistical Analysis

• Meta-analysis employs statistical techniques to
  combine the effect sizes from individual RCTs. A
  common method is to use a weighted average of the
  effect sizes, where the weights are determined by
  the study's sample size and quality.

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Weighting Studies

• In a meta-analysis, not all studies are given
  equal importance. Larger studies or those with a
  lower risk of bias may be given more weight in
  the analysis. This helps ensure that the results
  of higher-quality studies have a greater
  influence on the overall findings.

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Publication Bias Assessment

• Publication bias occurs when studies with
  positive results are more likely to be published,
  leading to an overestimation of the treatment
  effect. Meta-analysts often assess for
  publication bias and attempt to correct it.

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Heterogeneity Assessment

• Researchers assess the heterogeneity of the
  included RCTs to determine how much variability
  exists between the studies. This is important
  because high heterogeneity may impact the
  reliability of the meta-analysis results.

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Reporting and Publication
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• The findings of the meta-analysis are typically
  reported in a scientific paper or a report, and
  they may be subject to peer review and
  publication in academic journals.

Interpretation of Results

• The meta-analysis results are interpreted to
  provide an overall estimate of the treatment
  effect, along with a measure of uncertainty
  (e.g., a confidence interval). This synthesis of
  evidence from multiple RCTs allows for a more
  comprehensive and reliable assessment of the
  intervention's efficacy.

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• Check our Examples to get an understanding of our
  adaptability across meta-analysis topics and
  subject domains.

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About Pubrica

• Pubrica has extensive experience in conducting
  meta-analysis, a quantitative, formal,
  epidemiological study design used to
  systematically assess the results of previous
  research to derive conclusions about that body of
   research.
• Pubrica's team of researchers and authors develop
  Scientific and medical research papers that can
  act as an indispensable tool to the
  practitioner/authors.
• Pubrica medical writers help you to write and
  edit the introduction by introducing the reader
  to the shortcomings or empty spaces in the
  identified research field.
• Our experts are aware of the structure that
  follows the broad topic, the problem, and the
  background and advance to a narrow topic to state
  the hypothesis. 

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