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Meta-Analysis — Glossary | Creatine.my

3 min read

What is a Meta-Analysis?

A meta-analysis is a statistical technique that combines quantitative results from multiple independent studies addressing the same research question.

It sits at the top of the evidence hierarchy, above individual randomized controlled trials (RCTs), cohort studies, and case reports.

By pooling data from many studies — often involving hundreds or thousands of total participants — a meta-analysis increases statistical power and provides a more precise estimate of effect size than any single study can offer.

How Meta-Analyses Work

The process typically involves:

  1. Systematic search — Researchers identify all relevant studies on the topic using thorough database searches
  2. Inclusion criteria — Studies are screened for quality, design, and relevance
  3. Data extraction — Key results and statistics are extracted from each qualifying study
  4. Statistical pooling — Individual study results are combined using weighted statistical methods
  5. Heterogeneity assessment — Researchers examine whether studies agree or disagree in their findings
  6. Publication bias analysis — Checks are performed to detect whether negative studies may be missing from the literature

The result is a single effect estimate (such as “creatine improves strength by 5-8%”) with a confidence interval reflecting the precision of that estimate.

What Creatine Meta-Analyses Show

Creatine is one of the most meta-analysed supplements in sports nutrition. Key findings across multiple meta-analyses include:

  • Strength: 5-8% improvement in maximal strength (1RM) compared to placebo
  • Power output: 5-15% improvement in peak power during repeated high-intensity efforts
  • Lean mass: Approximately 1-2 kg greater gains over 8-12 weeks of resistance training
  • Body composition: Modest reductions in body fat percentage when combined with training
  • Cognitive function: Benefits observed particularly under conditions of sleep deprivation or cognitive stress

These findings are remarkably consistent across meta-analyses conducted by different research groups in different countries, spanning decades of research.

Why This Matters

When someone asks “Does creatine work?” the answer is not based on a single study or anecdotal experience.

It is based on meta-analyses combining data from hundreds of studies and thousands of participants.

This level of evidence is rare in the supplement industry and is why major scientific bodies (ISSN, ACSM, IOC) endorse creatine supplementation.

For consumers in Malaysia and worldwide, meta-analytic evidence provides the confidence that creatine’s benefits are real, reproducible, and meaningful — not artifacts of individual study design or small sample sizes.

  • Double-Blind Study — The gold-standard studies that feed into meta-analyses
  • Ergogenic Aid — The performance-enhancing category creatine belongs to
  • Phosphocreatine — The mechanism underlying creatine’s meta-analysed benefits

Sources & References

Full citations available in our Research Library.

Frequently Asked Questions

What is a meta-analysis?

A meta-analysis is a statistical method that combines the results of multiple independent studies on the same topic to produce a single, more precise estimate of effect. By pooling data from many studies (often thousands of participants), a meta-analysis provides the highest level of evidence in the scientific hierarchy.

What do meta-analyses say about creatine?

Multiple meta-analyses consistently support creatine's benefits. Key findings include: 5-8% improvement in strength, 5-15% improvement in power output, approximately 1-2 kg greater lean mass gains over 8-12 weeks, and cognitive benefits particularly in sleep-deprived or stressed individuals. These are among the most robust findings in sports nutrition research.

Why are meta-analyses more reliable than individual studies?

Individual studies may have small sample sizes, unique populations, or methodological quirks that skew results. A meta-analysis averages out these variations by combining data across many studies, giving a clearer picture of the true effect. It also has greater statistical power to detect real effects and can identify consistent patterns across diverse research settings.

Reviewed by T. Dinaiz, BSc (Molecular Biology), MSc (Biotechnology)

Reviewed against peer-reviewed research · Our editorial policy