Systematic Review Guide

Example (PICO):

• P: Adults with Type 2 diabetes
• I: Telehealth-based self-management programs
• C: Standard in-person care
• O: HbA1c levels, quality of life

Step 2: Register the Protocol

Register your protocol before conducting the search to reduce publication bias and selective reporting:

• PROSPERO (crd.york.ac.uk/prospero): Free registration for health-related systematic reviews
• OSF Registries (osf.io/registries): Open to all disciplines
• Protocol paper: Publish in BMJ Open, Systematic Reviews, or JMIR Research Protocols

Protocol should include:

• Research question and objectives
• Eligibility criteria
• Search strategy (databases, search terms)
• Screening process
• Data extraction plan
• Risk of bias assessment tool
• Synthesis method (meta-analysis or narrative)

Step 3: Conduct the Search

Recommended minimum databases (health sciences):
1. PubMed / MEDLINE
2. Embase
3. Cochrane Central Register of Controlled Trials (CENTRAL)
4. At least one subject-specific database

Recommended minimum databases (social sciences):
1. Web of Science
2. Scopus
3. PsycINFO or ERIC (field-specific)
4. ProQuest Dissertations (for grey literature)

Additional sources:
- Reference lists of included studies (backward citation chaining)
- Forward citation searches
- Grey literature: conference proceedings, theses, reports
- Trial registries: ClinicalTrials.gov, WHO ICTRP
- Preprint servers: medRxiv, SSRN

Document each search with: database name, date, exact search string, and number of results.

Step 4: Screen Studies

Two-stage screening, each conducted by at least two independent reviewers:

Stage 1: Title and Abstract Screening
- Apply inclusion/exclusion criteria based on title + abstract only
- Resolve disagreements by discussion or third reviewer
- Calculate inter-rater reliability (Cohen's kappa >= 0.60)

Stage 2: Full-Text Screening
- Retrieve full texts of all studies passing Stage 1
- Apply full eligibility criteria
- Document reasons for exclusion at this stage
- Calculate inter-rater reliability

Screening tools: Covidence (covidence.org), Rayyan (rayyan.ai), ASReview (AI-assisted)

Step 5: Extract Data

Create a standardized data extraction form:

| Field | Description |
|-------|-------------|
| Study ID | First author + year |
| Country | Where study was conducted |
| Study design | RCT, cohort, cross-sectional, etc. |
| Sample size | N in each group |
| Population | Demographics, inclusion criteria used |
| Intervention | Description, duration, intensity |
| Comparator | Description of control condition |
| Outcomes | Primary and secondary, measurement tools |
| Results | Effect sizes, confidence intervals, p-values |
| Funding | Source of funding |
| Conflicts of interest | Declared COIs |

Pilot the extraction form on 3-5 studies, then extract independently by two reviewers.

Step 6: Assess Risk of Bias

Select the appropriate tool based on study design:

Step 7: Synthesize Evidence

Narrative Synthesis

When meta-analysis is not appropriate (due to heterogeneity in study designs, populations, or outcomes):

1. Group studies by outcome, population, or intervention type
2. Describe patterns and consistencies across studies
3. Use vote counting only with direction of effect (not p-values)
4. Present findings in summary tables

Meta-Analysis

When studies are sufficiently similar to pool quantitatively:

# Meta-analysis using Python (PythonMeta or custom)
# Example: Random-effects meta-analysis of standardized mean differences

import numpy as np
from scipy.stats import norm

def random_effects_meta(effects, variances):
    """DerSimonian-Laird random effects meta-analysis."""
    weights_fe = 1 / np.array(variances)
    theta_fe = np.sum(weights_fe * effects) / np.sum(weights_fe)

    # Estimate tau-squared (between-study variance)
    Q = np.sum(weights_fe * (effects - theta_fe)**2)
    df = len(effects) - 1
    C = np.sum(weights_fe) - np.sum(weights_fe**2) / np.sum(weights_fe)
    tau2 = max(0, (Q - df) / C)

    # Random effects weights
    weights_re = 1 / (np.array(variances) + tau2)
    theta_re = np.sum(weights_re * effects) / np.sum(weights_re)
    se_re = np.sqrt(1 / np.sum(weights_re))

    ci_lower = theta_re - 1.96 * se_re
    ci_upper = theta_re + 1.96 * se_re

    # Heterogeneity statistics
    I2 = max(0, (Q - df) / Q * 100) if Q > 0 else 0

    return {
        "pooled_effect": theta_re,
        "ci_lower": ci_lower,
        "ci_upper": ci_upper,
        "tau2": tau2,
        "I2": I2,
        "Q": Q,
        "p_heterogeneity": 1 - chi2.cdf(Q, df)
    }

# Meta-analysis in R using metafor
library(metafor)

# Random-effects model (REML estimator)
res <- rma(yi = effect_sizes, vi = variances, method = "REML", data = dat)
summary(res)

# Forest plot
forest(res, slab = dat$study_label, header = TRUE)

# Funnel plot (publication bias assessment)
funnel(res)

# Egger's test for funnel plot asymmetry
regtest(res)

Step 8: Report Using PRISMA 2020

The PRISMA 2020 flow diagram documents the study selection process:

Records identified from databases (n = X)
Records identified from other sources (n = X)
          |
Records after duplicates removed (n = X)
          |
Records screened (title/abstract) (n = X)
    -> Records excluded (n = X)
          |
Reports sought for retrieval (n = X)
    -> Reports not retrieved (n = X)
          |
Reports assessed for eligibility (n = X)
    -> Reports excluded with reasons (n = X)
       - Reason 1 (n = X)
       - Reason 2 (n = X)
       - Reason 3 (n = X)
          |
Studies included in review (n = X)
Studies included in meta-analysis (n = X)

Common Pitfalls