If you're a medical trainee exploring research options, you've probably heard that meta-analyses are a good way to publish without collecting original data. That's partially true—but it doesn't mean they're fast or easy. In fact, most trainees underestimate how long a meta-analysis actually takes, and many projects stall midway through.

This guide breaks down the realistic timeline for completing a meta-analysis, the phases involved, and what to consider before committing to one.

How Long Does a Meta-Analysis Take?

Most meta-analyses take 6–12 months from idea to submission. Some stretch longer, particularly for first-time teams or broad research questions with thousands of potentially relevant studies.

A 2025 study of registered systematic reviews found the median time to completion was 11.5 months, with time to publication stretching to over 16 months. Notably, authors consistently underestimated how long their reviews would take—actual completion times were 69% longer than anticipated.

If you're new to the process, expect to be on the longer end of that range.

The Phases of a Meta-Analysis

Unlike a database study where you're working with a single, pre-cleaned dataset, a meta-analysis requires you to find, screen, extract, and synthesize data from dozens (sometimes hundreds) of published studies. Here's what that process looks like:

Phase 1: Protocol Development and Registration (1–2 Weeks)

Before you begin, you'll need to write a formal protocol outlining your research question, inclusion/exclusion criteria, search strategy, and planned analyses. Most journals now expect meta-analyses to be registered in advance on platforms like PROSPERO or INPLASY.

Registration isn't mandatory, but it's increasingly expected—and it forces you to think through your methodology before you start.

Phase 2: Designing the Search Strategy (1–2 Weeks)

Your search strategy determines which studies you'll find. This involves selecting databases (PubMed, Embase, Cochrane, etc.), developing search terms, and deciding how broad or narrow to cast your net.

A poorly designed search can result in thousands of irrelevant results—or miss key studies entirely. Many trainees work with a medical librarian at this stage, which is highly recommended.

Phase 3: Running Searches and Retrieving Citations (1–2 Weeks)

Once your strategy is finalized, you'll run searches across multiple databases and import citations into reference management software like EndNote, Covidence, or Rayyan. Expect to retrieve hundreds to thousands of citations depending on your topic.

Phase 4: Title and Abstract Screening (2–6 Weeks)

This is where the work becomes tedious. You'll review every title and abstract to determine whether each study might meet your inclusion criteria.

Here's the catch: proper methodology requires at least two independent reviewers screening the same studies, then reconciling disagreements. This isn't optional—it's a core requirement for a credible meta-analysis. If you're working alone, you're not doing it right.

For a review with 2,000 citations, even at 2 minutes per citation, you're looking at 60+ hours of screening—per reviewer.

Phase 5: Full-Text Review (2–4 Weeks)

Studies that pass initial screening move to full-text review. You'll read each paper in detail to confirm eligibility and begin extracting relevant information.

This phase often takes longer than expected because full-text articles aren't always freely available, requiring interlibrary loans or direct requests to authors.

Phase 6: Data Extraction (2–4 Weeks)

For each included study, you'll extract key data points: sample sizes, effect sizes, confidence intervals, outcome definitions, and study characteristics. This requires a standardized extraction form and—again—dual extraction by independent reviewers.

Every study reports things slightly differently, which means you'll spend considerable time interpreting and standardizing data.

Phase 7: Quality and Risk of Bias Assessment (1–2 Weeks)

You'll assess each study's methodological quality using tools like the Cochrane Risk of Bias tool or Newcastle-Ottawa Scale. This step is critical for interpreting your results and is required by most journals.

Phase 8: Statistical Analysis (2–4 Weeks)

Now you're ready to run the actual meta-analysis. This involves calculating pooled effect sizes, generating forest plots, assessing heterogeneity, and potentially running subgroup or sensitivity analyses.

You'll need software like RevMan, R, or Stata—and enough statistical knowledge to use it correctly. Common mistakes at this stage include misinterpreting heterogeneity statistics, using the wrong effect measures, and inappropriate pooling of studies.

If you don't have statistical training, you'll need a collaborator who does.

Phase 9: Writing and PRISMA Reporting (2–4 Weeks)

Meta-analyses follow the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, which include a 27-item checklist and a flow diagram documenting your screening process.

The writing itself isn't dramatically different from other research papers, but ensuring PRISMA compliance adds time.

Phase 10: Co-Author Review and Submission (2–4 Weeks)

As with any manuscript, you'll circulate drafts among co-authors, incorporate feedback, format for your target journal, and submit.

Why Meta-Analyses Take Longer Than Expected

Several factors consistently slow down meta-analysis projects:

You can't do it alone. Dual screening, dual extraction, and dual quality assessment aren't suggestions—they're methodological requirements. Finding collaborators who can commit time over many months is challenging.

The screening volume is massive. A well-designed search often returns thousands of citations. Even with software to help, the manual review burden is substantial.

Finding a mentor is difficult. Meta-analyses require specific methodological expertise that not every faculty member has. Without proper guidance, it's easy to make errors that undermine your entire project.

Statistical complexity is real. Understanding heterogeneity, choosing the right model (fixed vs. random effects), and interpreting results correctly requires training. Many trainees need to learn these skills from scratch.

Do Meta-Analyses Look Better on Your CV?

Meta-analyses sit at the top of the traditional evidence hierarchy, which leads some trainees to assume they're more impressive than original research. The reality is more nuanced.

A well-executed meta-analysis on a clinically important question can be highly impactful. However, the explosion of trainee-produced meta-analyses has led to saturation in the literature—many adding little value. Program directors increasingly recognize when a CV is padded with low-impact reviews versus substantive original work.

The question isn't whether meta-analyses are valuable. It's whether the 6–12 months required is the best use of your limited research time.

How Meta-Analyses Compare to Other Research Types

Here's how the timeline stacks up against other common trainee projects (from idea to submission):

• Case Report: 2–3 months — fastest path to publication, no IRB required
• Database Study: 3–6 months — uses existing data, no manual extraction
• Meta-Analysis: 6–12 months — requires team, statistical expertise
• Chart Review: 8–12 months — IRB approval, manual data collection
• Prospective Study: 18+ months — patient recruitment, long follow-up

For trainees on tight timelines before residency or fellowship applications, case reports and database studies offer faster, more predictable paths to completed work.

When a Meta-Analysis Makes Sense

Meta-analyses are the right choice when:

• A clinically important question has conflicting evidence across multiple studies
• You have access to a mentor with meta-analysis experience
• You can assemble a team of at least 2–3 committed collaborators
• You have 6–12 months before you need results
• You're genuinely interested in evidence synthesis methodology

If those conditions aren't met, consider alternative approaches.

A Faster Alternative: Database Research

If your goal is to publish original research efficiently, database studies using large public datasets like NHANES offer a compelling alternative. These studies don't require IRB approval, manual data extraction, or large teams—and they produce original findings that are generalizable to national populations.

For a detailed breakdown of that process, see our Database Study Timeline: How Long It Takes to Submit an NHANES or Retrospective Data Study.

If you're just getting started with research, our Case Report Timeline outlines the fastest path to your first publication.

And for a deeper look at how meta-analyses compare to original research for building your CV, see Meta-Analysis: What It Is, What It Isn't, and Why Original Research Matters More.

Where Lumono Fits In

Lumono is built for trainees who want to conduct original database research without the barriers of traditional methods. The platform guides you through question generation, variable selection, cohort extraction, and statistical analysis—producing publication-ready methods and results.

If you're weighing your research options and want a faster, more independent path to your first (or next) publication, Lumono can help you get there.

Frequently Asked Questions

How long does a meta-analysis take?

Most meta-analyses take 6–12 months from idea to submission. First-time projects often take longer due to the learning curve with methodology and software.

Can I do a meta-analysis alone?

Not properly. Meta-analysis methodology requires at least two independent reviewers for screening, data extraction, and quality assessment. Working alone undermines the credibility of your findings.

Do I need statistical skills for a meta-analysis?

Yes. You'll need to use software like RevMan, R, or Stata and understand concepts like effect sizes, heterogeneity, and forest plots. If you lack this background, you'll need a statistician collaborator.

Is a meta-analysis easier than original research?

Not necessarily. While you don't collect primary data, the screening and extraction process is labor-intensive. Many trainees find database studies faster and more straightforward.

Do meta-analyses look good on residency applications?

A well-executed meta-analysis can be valuable, but program directors increasingly recognize when CVs are padded with low-impact reviews. Quality and relevance matter more than the study type.

Do I need to register my meta-analysis protocol?

Registration on PROSPERO or similar platforms isn't always mandatory, but it's increasingly expected by journals and strengthens the credibility of your review.