Master information synthesis, literature review, meta-analysis, and knowledge integration across sources
Use this skill when you need to:
Purpose: Qualitative integration of findings using words and text
Process:
1. Define scope and research questions
2. Search and select studies systematically
3. Extract key findings and themes
4. Organize by theoretical framework or chronology
5. Identify patterns, contradictions, gaps
6. Synthesize into coherent narrativeExample Structure:
## Research Question
How does X influence Y in Z contexts?
## Synthesis Findings
### Theme 1: Direct Effects
- Study A (2023): Found positive correlation (r=0.65, p<0.01)
- Study B (2022): Confirmed effect in different population
- Study C (2021): Mixed results, moderated by factor Q
### Theme 2: Mediating Mechanisms
- Study D identifies pathway through M
- Study E challenges this, proposes alternative
### Theme 3: Contextual Factors
- Effect stronger in setting X
- No effect observed in setting Y
## Synthesis Conclusion
Evidence suggests X→Y relationship is robust but context-dependent...Purpose: Statistical integration of quantitative results
Python Implementation:
import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
class MetaAnalysis:
"""Fixed and random effects meta-analysis"""
def __init__(self, effect_sizes, standard_errors, study_labels):
self.effects = np.array(effect_sizes)
self.se = np.array(standard_errors)
self.labels = study_labels
self.weights = None
self.pooled_effect = None
def fixed_effects(self):
"""Fixed effects meta-analysis"""
# Inverse variance weighting
self.weights = 1 / (self.se ** 2)
self.pooled_effect = np.sum(self.weights * self.effects) / np.sum(self.weights)
pooled_se = np.sqrt(1 / np.sum(self.weights))
# 95% CI
ci_lower = self.pooled_effect - 1.96 * pooled_se
ci_upper = self.pooled_effect + 1.96 * pooled_se
# Heterogeneity statistics
Q = np.sum(self.weights * (self.effects - self.pooled_effect) ** 2)
df = len(self.effects) - 1
I2 = max(0, ((Q - df) / Q) * 100)
return {
'pooled_effect': self.pooled_effect,
'se': pooled_se,
'ci_lower': ci_lower,
'ci_upper': ci_upper,
'Q': Q,
'I2': I2,
'p_heterogeneity': 1 - stats.chi2.cdf(Q, df)
}
def random_effects(self, tau2_method='DL'):
"""Random effects meta-analysis (DerSimonian-Laird)"""
# First get fixed effects for Q statistic
fixed = self.fixed_effects()
Q = fixed['Q']
df = len(self.effects) - 1
# Estimate between-study variance (tau^2)
C = np.sum(self.weights) - np.sum(self.weights ** 2) / np.sum(self.weights)
tau2 = max(0, (Q - df) / C)
# Random effects weights
re_weights = 1 / (self.se ** 2 + tau2)
pooled_effect = np.sum(re_weights * self.effects) / np.sum(re_weights)
pooled_se = np.sqrt(1 / np.sum(re_weights))
ci_lower = pooled_effect - 1.96 * pooled_se
ci_upper = pooled_effect + 1.96 * pooled_se
return {
'pooled_effect': pooled_effect,
'se': pooled_se,
'ci_lower': ci_lower,
'ci_upper': ci_upper,
'tau2': tau2,
'I2': fixed['I2']
}
def forest_plot(self, results, title='Meta-Analysis Forest Plot'):
"""Create forest plot visualization"""
fig, ax = plt.subplots(figsize=(10, len(self.effects) + 2))
# Calculate CIs for individual studies
ci_lower = self.effects - 1.96 * self.se
ci_upper = self.effects + 1.96 * self.se
# Plot individual studies
y_pos = np.arange(len(self.effects))
ax.errorbar(self.effects, y_pos,
xerr=[self.effects - ci_lower, ci_upper - self.effects],
fmt='s', markersize=8, capsize=5, label='Studies')
# Plot pooled effect
pooled_y = len(self.effects) + 0.5
ax.errorbar([results['pooled_effect']], [pooled_y],
xerr=[[results['pooled_effect'] - results['ci_lower']],
[results['ci_upper'] - results['pooled_effect']]],
fmt='D', markersize=12, capsize=8,
color='red', label='Pooled Effect')
# Styling
ax.axvline(x=0, color='black', linestyle='--', linewidth=1)
ax.set_yticks(list(y_pos) + [pooled_y])
ax.set_yticklabels(list(self.labels) + ['Pooled'])
ax.set_xlabel('Effect Size')
ax.set_title(title)
ax.legend()
ax.grid(axis='x', alpha=0.3)
return fig
# Example usage
studies = [
('Study A', 0.45, 0.12),
('Study B', 0.62, 0.15),
('Study C', 0.38, 0.10),
('Study D', 0.51, 0.14),
('Study E', 0.55, 0.11)
]
labels = [s[0] for s in studies]
effects = [s[1] for s in studies]
ses = [s[2] for s in studies]
meta = MetaAnalysis(effects, ses, labels)
fixed_results = meta.fixed_effects()
random_results = meta.random_effects()
print(f"Fixed Effects: {fixed_results['pooled_effect']:.3f} "
f"[{fixed_results['ci_lower']:.3f}, {fixed_results['ci_upper']:.3f}]")
print(f"I² = {fixed_results['I2']:.1f}%")
print(f"\nRandom Effects: {random_results['pooled_effect']:.3f} "
f"[{random_results['ci_lower']:.3f}, {random_results['ci_upper']:.3f}]")
# Create forest plot
fig = meta.forest_plot(random_results)
plt.savefig('forest_plot.png', dpi=300, bbox_inches='tight')Purpose: Integrate qualitative findings across studies
Process:
from collections import defaultdict
import pandas as pd
class ThematicSynthesis:
"""Synthesize themes across qualitative studies"""
def __init__(self):
self.studies = []
self.themes = defaultdict(list)
def add_study(self, study_id, findings):
"""Add study findings
Args:
study_id: Study identifier
findings: List of (finding, theme) tuples
"""
self.studies.append(study_id)
for finding, theme in findings:
self.themes[theme].append({
'study': study_id,
'finding': finding
})
def get_theme_matrix(self):
"""Create study x theme presence matrix"""
themes = list(self.themes.keys())
matrix = []
for study in self.studies:
row = []
for theme in themes:
has_theme = any(f['study'] == study for f in self.themes[theme])
row.append(1 if has_theme else 0)
matrix.append(row)
return pd.DataFrame(matrix,
index=self.studies,
columns=themes)
def synthesize_theme(self, theme_name):
"""Synthesize findings for a specific theme"""
findings = self.themes[theme_name]
synthesis = {
'theme': theme_name,
'n_studies': len(set(f['study'] for f in findings)),
'n_findings': len(findings),
'findings_by_study': defaultdict(list)
}
for finding in findings:
synthesis['findings_by_study'][finding['study']].append(
finding['finding']
)
return synthesis
def generate_report(self):
"""Generate synthesis report"""
report = []
report.append("# Thematic Synthesis Report\n")
report.append(f"Total Studies: {len(self.studies)}\n")
report.append(f"Total Themes: {len(self.themes)}\n\n")
# Theme frequency
report.append("## Theme Prevalence\n")
for theme, findings in sorted(self.themes.items(),
key=lambda x: len(x[1]),
reverse=True):
n_studies = len(set(f['study'] for f in findings))
report.append(f"- **{theme}**: {n_studies} studies, "
f"{len(findings)} findings\n")
report.append("\n## Detailed Synthesis\n")
for theme in sorted(self.themes.keys()):
synthesis = self.synthesize_theme(theme)
report.append(f"\n### {theme}\n")
report.append(f"Present in {synthesis['n_studies']} studies\n\n")
for study, findings in synthesis['findings_by_study'].items():
report.append(f"**{study}**:\n")
for finding in findings:
report.append(f"- {finding}\n")
report.append("\n")
return ''.join(report)
# Example usage
synth = ThematicSynthesis()
synth.add_study('Smith2023', [
('Participants valued flexibility', 'Flexibility'),
('Trust was essential for engagement', 'Trust'),
('Time constraints were barrier', 'Barriers')
])
synth.add_study('Jones2022', [
('Flexibility enabled participation', 'Flexibility'),
('Communication breakdowns reduced trust', 'Trust'),
('Technology issues created frustration', 'Barriers')
])
synth.add_study('Lee2023', [
('Schedule flexibility was key benefit', 'Flexibility'),
('Clear expectations built trust', 'Trust')
])
# Generate outputs
matrix = synth.get_theme_matrix()
print(matrix)
print("\n" + synth.generate_report())Purpose: Visualize research landscape and gaps
Approach:
import networkx as nx
import matplotlib.pyplot as plt
class EvidenceMap:
"""Create evidence map of research domain"""
def __init__(self):
self.graph = nx.DiGraph()
def add_study(self, study_id, population, intervention,
outcome, quality='medium'):
"""Add study to evidence map"""
# Create nodes
pop_node = f"POP: {population}"
int_node = f"INT: {intervention}"
out_node = f"OUT: {outcome}"
self.graph.add_node(pop_node, type='population')
self.graph.add_node(int_node, type='intervention')
self.graph.add_node(out_node, type='outcome')
# Create edges
self.graph.add_edge(pop_node, int_node,
study=study_id, quality=quality)
self.graph.add_edge(int_node, out_node,
study=study_id, quality=quality)
def identify_gaps(self):
"""Identify under-researched areas"""
gaps = {
'populations': [],
'interventions': [],
'outcomes': [],
'combinations': []
}
# Find nodes with few connections
for node in self.graph.nodes():
degree = self.graph.degree(node)
node_type = self.graph.nodes[node]['type']
if degree < 2:
gaps[f"{node_type}s"].append(node)
return gaps
def visualize(self):
"""Create evidence map visualization"""
pos = nx.spring_layout(self.graph, k=2, iterations=50)
# Color by node type
colors = []
for node in self.graph.nodes():
node_type = self.graph.nodes[node]['type']
if node_type == 'population':
colors.append('lightblue')
elif node_type == 'intervention':
colors.append('lightgreen')
else:
colors.append('lightyellow')
plt.figure(figsize=(12, 8))
nx.draw(self.graph, pos, node_color=colors,
with_labels=True, node_size=3000,
font_size=8, arrows=True)
plt.title('Evidence Map')
return plt.gcf()Criteria for Evidence Quality:
Rating System:
High: Further research unlikely to change confidence
Moderate: Further research likely important to confidence
Low: Further research very likely important
Very Low: Very uncertain about the estimate✓ Systematic search strategy documented
✓ Inclusion/exclusion criteria clear
✓ Quality assessment of each study
✓ Appropriate synthesis method for data type
✓ Heterogeneity acknowledged and explored
✓ Limitations discussed
✓ Practical implications stated
✓ Research gaps identified✗ Cherry-picking favorable studies
✗ Mixing apples and oranges without justification
✗ Ignoring study quality differences
✗ Over-generalizing from limited evidence
✗ Failing to report null findings
✗ Missing recent relevant studies
✗ Synthesis method unclear or inappropriateRecords identified through database searching (n=)
↓
Records after duplicates removed (n=)
↓
Records screened (n=) → Records excluded (n=)
↓
Full-text assessed (n=) → Full-text excluded, with reasons (n=)
↓
Studies included in synthesis (n=)
↓
Studies included in meta-analysis (n=)| Study | Year | Design | N | Population | Outcome | Effect Size | Quality |
|-------|------|--------|---|------------|---------|-------------|---------|
| A | 2023 | RCT |100| Adults | Anxiety | d=-0.45 | High |
| B | 2022 | Cohort |250| Teens | Anxiety | d=-0.32 | Medium |