菜单
Use this Skill for psychology meta-analysis: Cohen's d, Hedges' g, random-effects pooling, forest plot, publication bias (Egger, p-curve, PET-PEESE), and sensitivity analysis.
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基于 SOC 职业分类
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| name | meta-analysis-psych |
| description | Use this Skill for psychology meta-analysis: Cohen's d, Hedges' g, random-effects pooling, forest plot, publication bias (Egger, p-curve, PET-PEESE), and sensitivity analysis. |
| tags | ["psychology","meta-analysis","effect-sizes","forest-plot","publication-bias","p-curve"] |
| version | 1.0.0 |
| authors | [{"name":"awesome-rosetta-skills contributors","github":"@xjtulyc"}] |
| license | MIT |
| platforms | ["claude-code","codex","gemini-cli","cursor"] |
| dependencies | {"python":["numpy>=1.23","scipy>=1.9","pandas>=1.5","matplotlib>=3.6"]} |
| last_updated | 2026-03-18 |
| status | stable |
TL;DR — Complete meta-analysis pipeline in pure Python/NumPy: Cohen's d and Hedges' g from raw statistics, DerSimonian-Laird random-effects pooling, I² heterogeneity, forest plot with summary diamond, funnel plot, Egger test, trim-and-fill, PET-PEESE regression correction, and p-curve analysis.
Use this Skill when you need to:
Cohen's d (raw mean difference standardized by pooled SD):
d = (M1 - M2) / SD_pooled
Hedges' g (small-sample bias correction for d):
J = 1 - 3 / (4 * (n1 + n2 - 2) - 1)
g = J × d
Var(g) = (n1 + n2) / (n1 × n2) + g² / (2 × (n1 + n2))
Under the random-effects model, true effect sizes θi vary:
θi = θ + ui + εi where ui ~ N(0, τ²), εi ~ N(0, vi)
Between-study variance τ² is estimated by the DerSimonian-Laird method:
Q = Σ wi(gi - g_FE)² (Cochran's Q statistic)
τ² = max(0, (Q - (k-1)) / (Σwi - Σwi²/Σwi))
wi* = 1 / (vi + τ²) (RE weights)
g_RE = Σ(wi* × gi) / Σwi*
I² = (Q - (k-1)) / Q × 100% (% of total variance due to heterogeneity)
| Method | Description |
|---|---|
| Funnel plot | Asymmetry suggests missing small-n null studies |
| Egger test | Regress std normal deviate on precision; intercept ≠ 0 = bias |
| Trim-and-fill | Impute mirror studies to restore funnel symmetry |
| PET-PEESE | Regress g on SE (PET) or SE² (PEESE); intercept = bias-corrected effect |
| p-curve | Distribution of p-values below .05; right-skew = real effect |
conda create -n meta_env python=3.11 -y
conda activate meta_env
pip install numpy>=1.23 scipy>=1.9 pandas>=1.5 matplotlib>=3.6
# Verify
python -c "import numpy, scipy, pandas, matplotlib; print('All OK')"
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from scipy import stats
from typing import Optional, Dict, List, Tuple
# ── Cohen's d from various input formats ─────────────────────────────────────
def cohens_d_from_means(
m1: float, m2: float,
sd1: float, sd2: float,
n1: int, n2: int,
) -> Dict:
"""
Compute Cohen's d and Hedges' g from group means and SDs.
Args:
m1, m2: Group means.
sd1, sd2: Group standard deviations.
n1, n2: Sample sizes.
Returns:
Dict with d, g, var_g, se_g, 95% CI.
"""
# Pooled SD
sd_pooled = np.sqrt(((n1 - 1) * sd1**2 + (n2 - 1) * sd2**2) / (n1 + n2 - 2))
d = (m1 - m2) / sd_pooled
# Hedges' g correction
df_total = n1 + n2 - 2
J = 1 - 3 / (4 * df_total - 1)
g = J * d
# Variance of g
var_g = (n1 + n2) / (n1 * n2) + g**2 / (2 * (n1 + n2))
se_g = np.sqrt(var_g)
return {
"d": round(d, 4),
"g": round(g, 4),
"J": round(J, 4),
"var_g": round(var_g, 6),
"se_g": round(se_g, 4),
"ci_lo": round(g - 1.96 * se_g, 4),
"ci_hi": round(g + 1.96 * se_g, 4),
"n1": n1, "n2": n2,
}
def cohens_d_from_t(
t: float, n1: int, n2: int,
) -> Dict:
"""
Compute Cohen's d from an independent-samples t-statistic.
Args:
t: t-statistic (signed).
n1, n2: Group sample sizes.
Returns:
Dict with d, g, var_g, se_g, 95% CI.
"""
d = t * np.sqrt((n1 + n2) / (n1 * n2))
df_total = n1 + n2 - 2
J = 1 - 3 / (4 * df_total - 1)
g = J * d
var_g = (n1 + n2) / (n1 * n2) + g**2 / (2 * (n1 + n2))
se_g = np.sqrt(var_g)
return {
"d": round(d, 4), "g": round(g, 4), "J": round(J, 4),
"var_g": round(var_g, 6), "se_g": round(se_g, 4),
"ci_lo": round(g - 1.96 * se_g, 4), "ci_hi": round(g + 1.96 * se_g, 4),
"n1": n1, "n2": n2,
}
def cohens_d_from_F(
F: float, n1: int, n2: int,
) -> Dict:
"""
Compute Cohen's d from a one-df F-ratio (two-group comparison).
Args:
F: F-statistic (F = t²).
n1, n2: Group sample sizes.
Returns:
Dict with d, g, var_g, se_g, 95% CI.
"""
t = np.sqrt(F) # F = t² for one-df tests
return cohens_d_from_t(t, n1, n2)
def run_meta_analysis(
df: pd.DataFrame,
g_col: str = "g",
var_col: str = "var_g",
study_col: str = "study",
) -> Dict:
"""
DerSimonian-Laird random-effects meta-analysis.
Args:
df: DataFrame with one row per study.
g_col: Column of Hedges' g effect sizes.
var_col: Column of effect size variances.
study_col: Column of study labels.
Returns:
Dict with pooled estimate, heterogeneity statistics, and weights.
"""
g = df[g_col].values
v = df[var_col].values
k = len(g)
# Fixed-effects weights and estimate
w_FE = 1 / v
g_FE = np.sum(w_FE * g) / np.sum(w_FE)
# Cochran's Q
Q = np.sum(w_FE * (g - g_FE) ** 2)
# DerSimonian-Laird tau²
c = np.sum(w_FE) - np.sum(w_FE ** 2) / np.sum(w_FE)
tau2 = max(0.0, (Q - (k - 1)) / c)
# I²
I2 = max(0.0, (Q - (k - 1)) / Q * 100) if Q > 0 else 0.0
# Random-effects weights and estimate
w_RE = 1 / (v + tau2)
g_RE = np.sum(w_RE * g) / np.sum(w_RE)
var_RE = 1 / np.sum(w_RE)
se_RE = np.sqrt(var_RE)
z_RE = g_RE / se_RE
p_RE = 2 * stats.norm.sf(abs(z_RE))
ci_lo = g_RE - 1.96 * se_RE
ci_hi = g_RE + 1.96 * se_RE
result = {
"k": k,
"g_RE": round(g_RE, 4),
"se_RE": round(se_RE, 4),
"ci_lo": round(ci_lo, 4),
"ci_hi": round(ci_hi, 4),
"z": round(z_RE, 3),
"p": round(p_RE, 4),
"Q": round(Q, 3),
"Q_df": k - 1,
"Q_p": round(float(stats.chi2.sf(Q, df=k - 1)), 4),
"I2": round(I2, 1),
"tau2": round(tau2, 6),
"tau": round(np.sqrt(tau2), 4),
"w_RE": w_RE,
"g_FE": round(g_FE, 4),
}
print(
f"Random-effects meta-analysis (k={k} studies):\n"
f" g = {g_RE:.3f} [{ci_lo:.3f}, {ci_hi:.3f}], p = {p_RE:.4f}\n"
f" Q({k-1}) = {Q:.2f}, p = {result['Q_p']:.4f}\n"
f" I² = {I2:.1f}%, τ = {result['tau']:.4f}\n"
f" Heterogeneity: "
+ ("low" if I2 < 25 else "moderate" if I2 < 75 else "high")
)
return result
def forest_plot(
df: pd.DataFrame,
meta_result: Dict,
g_col: str = "g",
ci_lo_col: str = "ci_lo",
ci_hi_col: str = "ci_hi",
study_col: str = "study",
sort_by: str = "g",
output_path: Optional[str] = None,
) -> plt.Figure:
"""
Generate a forest plot with study CIs and summary diamond.
Args:
df: Study-level DataFrame (one row per study).
meta_result: Output from run_meta_analysis().
g_col: Column of effect sizes.
ci_lo_col: Column of lower 95% CIs.
ci_hi_col: Column of upper 95% CIs.
study_col: Column of study labels.
sort_by: Sort studies by 'g' (default) or 'year'.
output_path: Optional path to save figure.
Returns:
Matplotlib Figure.
"""
if sort_by == "g":
df = df.sort_values(g_col).reset_index(drop=True)
k = len(df)
g_RE = meta_result["g_RE"]
ci_lo_RE = meta_result["ci_lo"]
ci_hi_RE = meta_result["ci_hi"]
fig, ax = plt.subplots(figsize=(10, max(6, k * 0.45 + 2)))
y_positions = list(range(k, 0, -1))
# Study rows
for i, (_, row) in enumerate(df.iterrows()):
y = y_positions[i]
g = row[g_col]
lo = row[ci_lo_col]
hi = row[ci_hi_col]
ci_width = hi - lo
# Marker size proportional to inverse variance (weight)
w = 1 / (hi - lo) ** 2 if (hi - lo) > 0 else 1
ms = min(max(4, w * 0.5), 16)
ax.plot([lo, hi], [y, y], color="black", linewidth=1)
ax.plot(g, y, "s", color="steelblue", markersize=ms, zorder=5)
ax.text(-0.05 + min(df[ci_lo_col]) - 0.3, y,
row[study_col], ha="right", va="center", fontsize=8)
ax.text(max(df[ci_hi_col]) + 0.3, y,
f"{g:.2f} [{lo:.2f}, {hi:.2f}]",
ha="left", va="center", fontsize=7, color="dimgray")
# Summary diamond
y_summary = 0
diamond_x = [ci_lo_RE, g_RE, ci_hi_RE, g_RE]
diamond_y = [y_summary, y_summary + 0.4, y_summary, y_summary - 0.4]
ax.fill(diamond_x, diamond_y, color="crimson", alpha=0.85, zorder=6)
ax.text(max(df[ci_hi_col]) + 0.3, y_summary,
f"{g_RE:.2f} [{ci_lo_RE:.2f}, {ci_hi_RE:.2f}]",
ha="left", va="center", fontsize=8, fontweight="bold", color="crimson")
# Reference line at 0
ax.axvline(0, color="gray", linestyle="--", linewidth=1)
ax.set_yticks([])
ax.set_xlabel("Hedges' g")
ax.set_title(
f"Forest Plot — {k} Studies\n"
f"g = {g_RE:.3f}, I² = {meta_result['I2']:.1f}%, "
f"τ = {meta_result['tau']:.3f}"
)
ax.set_xlim(min(df[ci_lo_col]) - 1.0, max(df[ci_hi_col]) + 1.5)
fig.tight_layout()
if output_path:
fig.savefig(output_path, dpi=150)
plt.show()
return fig
def egger_test(
df: pd.DataFrame,
g_col: str = "g",
var_col: str = "var_g",
) -> Dict:
"""
Egger's test for funnel plot asymmetry (publication bias).
Regresses standardized effect sizes on precision:
z = g / se (standard normal deviate)
w = 1 / se (precision)
Regression: z = a + b × w
Intercept a ≠ 0 indicates asymmetry.
Args:
df: Study DataFrame.
g_col: Effect size column.
var_col: Variance column.
Returns:
Dict with intercept, SE, t-stat, p-value.
"""
g = df[g_col].values
v = df[var_col].values
se = np.sqrt(v)
z = g / se # standard normal deviate
prec = 1 / se # precision
slope, intercept, r, p_val, se_intercept = stats.linregress(prec, z)
t_stat = intercept / se_intercept
df_resid = len(g) - 2
p_intercept = 2 * stats.t.sf(abs(t_stat), df=df_resid)
result = {
"intercept": round(intercept, 4),
"se_intercept": round(se_intercept, 4),
"t_stat": round(t_stat, 3),
"p_value": round(p_intercept, 4),
"significant": p_intercept < 0.05,
"slope": round(slope, 4),
}
print(
f"Egger test: intercept = {intercept:.3f} (SE = {se_intercept:.3f}), "
f"t = {t_stat:.3f}, p = {p_intercept:.4f} "
+ ("← SIGNIFICANT bias" if p_intercept < 0.05 else "← non-significant")
)
return result
def pet_peese(
df: pd.DataFrame,
g_col: str = "g",
var_col: str = "var_g",
alpha_fat: float = 0.10,
) -> Dict:
"""
PET-PEESE bias correction (Stanley & Doucouliagos, 2014).
FAT-PET: g_i = β0 + β1 × SE_i + ε_i
β0 = bias-corrected effect when SE = 0 (no bias)
PEESE: g_i = β0 + β1 × SE_i² + ε_i
Use if FAT-PET β0 is significant.
Args:
df: Study DataFrame.
g_col: Effect size column.
var_col: Variance column.
alpha_fat: Significance threshold for FAT-PET intercept (default 0.10).
Returns:
Dict with PET estimate, PEESE estimate, and recommendations.
"""
g = df[g_col].values
v = df[var_col].values
se = np.sqrt(v)
w = 1 / v
def wls_intercept(x_vals, y_vals, weights):
"""WLS regression — return intercept and p-value."""
W = np.diag(weights)
X = np.column_stack([np.ones(len(y_vals)), x_vals])
XWX_inv = np.linalg.inv(X.T @ W @ X)
coef = XWX_inv @ X.T @ W @ y_vals
residuals = y_vals - X @ coef
sigma2 = np.sum(weights * residuals**2) / (len(y_vals) - 2)
var_coef = sigma2 * XWX_inv
se_intercept = np.sqrt(var_coef[0, 0])
t_stat = coef[0] / se_intercept
p_val = 2 * stats.t.sf(abs(t_stat), df=len(y_vals) - 2)
return coef[0], se_intercept, t_stat, p_val
# PET (FAT: regress g on SE)
pet_b0, pet_se, pet_t, pet_p = wls_intercept(se, g, w)
# PEESE (regress g on SE²)
peese_b0, peese_se, peese_t, peese_p = wls_intercept(se**2, g, w)
# Decision rule
if pet_p < alpha_fat:
recommended = "PEESE"
recommended_estimate = peese_b0
else:
recommended = "PET (effect non-significant)"
recommended_estimate = pet_b0
result = {
"PET_estimate": round(pet_b0, 4),
"PET_se": round(pet_se, 4),
"PET_p": round(pet_p, 4),
"PEESE_estimate": round(peese_b0, 4),
"PEESE_se": round(peese_se, 4),
"PEESE_p": round(peese_p, 4),
"recommended_method": recommended,
"corrected_estimate": round(recommended_estimate, 4),
}
print(
f"PET-PEESE:\n"
f" PET: β0 = {pet_b0:.4f} (SE = {pet_se:.4f}), p = {pet_p:.4f}\n"
f" PEESE: β0 = {peese_b0:.4f} (SE = {peese_se:.4f}), p = {peese_p:.4f}\n"
f" Recommended: {recommended} → corrected estimate = {recommended_estimate:.4f}"
)
return result
def p_curve_analysis(
p_values: np.ndarray,
alpha: float = 0.05,
) -> Dict:
"""
P-curve analysis for evidential value assessment.
Under H0 (no true effect), p-values are uniform on [0, alpha].
Under H1 (true effect), p-values are right-skewed (more very small p's).
The p-curve plots conditional distribution of p-values < alpha.
Args:
p_values: Array of p-values from studies (all p < alpha are included).
alpha: Significance threshold.
Returns:
Dict with binomial test for right-skew and uniform comparison.
"""
sig_p = p_values[p_values < alpha]
k_sig = len(sig_p)
if k_sig < 3:
print("Warning: fewer than 3 significant p-values — p-curve unreliable.")
return {"warning": "insufficient significant studies", "k_sig": k_sig}
# Expected proportion below alpha/2 under uniform H0 distribution
expected_prop = 0.5 # uniform: 50% below alpha/2 = 0.025
observed_prop = np.mean(sig_p < alpha / 2)
binom_result = stats.binom_test(
int(observed_prop * k_sig), k_sig, p=expected_prop, alternative="greater"
) if hasattr(stats, "binom_test") else stats.binomtest(
int(observed_prop * k_sig), k_sig, p=expected_prop, alternative="greater"
).pvalue
result = {
"k_sig": k_sig,
"observed_prop_below_half_alpha": round(observed_prop, 4),
"expected_prop_H0": expected_prop,
"p_right_skew": round(float(binom_result), 4),
"evidential_value": float(binom_result) < 0.05,
}
print(
f"P-curve (k_sig={k_sig}):\n"
f" Prop p < {alpha/2:.3f}: {observed_prop:.3f} (expected under H0: {expected_prop})\n"
f" Right-skew test p = {float(binom_result):.4f} "
+ ("← evidential value present" if float(binom_result) < 0.05
else "← insufficient evidential value")
)
return result
| Problem | Likely Cause | Solution |
|---|---|---|
| τ² is 0 (homogeneous) | Low heterogeneity; Q < k-1 | Report as-is; fixed-effects model appropriate |
| I² near 100% | Very heterogeneous studies | Report sub-group analyses; reconsider pooling |
| Egger test false positive with large k | Multiple comparisons | Use Egger only as exploratory; also check funnel |
| PET-PEESE gives impossible estimate | Small-study effect inflated d | Report with caution; use sensitivity analysis |
p-curve requires binom_test | Older scipy version | Use scipy.stats.binomtest (scipy >= 1.7) |
| Forest plot labels overlap | Many studies | Reduce fontsize or increase figure height |
| Negative CI lower bound for g | Very small studies | Expected; report accurately |
# Hypothetical studies on working-memory training and fluid intelligence
studies_data = [
{"study": "Smith 2015", "m1": 52.3, "sd1": 9.2, "n1": 30, "m2": 47.1, "sd2": 8.8, "n2": 30},
{"study": "Jones 2016", "m1": 61.2, "sd1": 11.0, "n1": 45, "m2": 57.8, "sd2": 10.5, "n2": 45},
{"study": "Brown 2017", "m1": 55.0, "sd1": 8.5, "n1": 25, "m2": 49.3, "sd2": 9.0, "n2": 25},
{"study": "Garcia 2018", "m1": 70.1, "sd1": 13.2, "n1": 60, "m2": 66.4, "sd2": 12.8, "n2": 60},
{"study": "Patel 2019", "m1": 48.5, "sd1": 7.9, "n1": 20, "m2": 44.2, "sd2": 8.1, "n2": 20},
{"study": "Chen 2020", "m1": 65.3, "sd1": 10.8, "n1": 35, "m2": 60.7, "sd2": 11.2, "n2": 35},
{"study": "Wilson 2021", "m1": 58.9, "sd1": 9.7, "n1": 40, "m2": 53.2, "sd2": 9.3, "n2": 40},
{"study": "Rodriguez 2022", "m1": 53.1, "sd1": 8.3, "n1": 28, "m2": 48.7, "sd2": 8.0, "n2": 28},
]
# Compute effect sizes
rows = []
for s in studies_data:
es = cohens_d_from_means(
s["m1"], s["m2"], s["sd1"], s["sd2"], s["n1"], s["n2"]
)
rows.append({
"study": s["study"],
"n1": s["n1"], "n2": s["n2"],
"g": es["g"], "var_g": es["var_g"],
"ci_lo": es["ci_lo"], "ci_hi": es["ci_hi"],
})
df_meta = pd.DataFrame(rows)
print("Effect sizes:")
print(df_meta[["study", "g", "ci_lo", "ci_hi"]].round(3))
# Random-effects meta-analysis
meta_res = run_meta_analysis(df_meta)
# Forest plot
forest_plot(df_meta, meta_res, output_path="forest_plot.png")
# Publication bias
egger_res = egger_test(df_meta)
pet_res = pet_peese(df_meta)
# Funnel plot
fig, ax = plt.subplots(figsize=(7, 6))
se = np.sqrt(df_meta["var_g"].values)
ax.scatter(df_meta["g"].values, se, alpha=0.7, color="steelblue", s=60)
ax.axvline(meta_res["g_RE"], color="crimson", linestyle="--", linewidth=1.5, label="g_RE")
ax.axvline(0, color="gray", linestyle=":", linewidth=1)
ax.invert_yaxis()
ax.set_xlabel("Hedges' g")
ax.set_ylabel("Standard Error (SE)")
ax.set_title("Funnel Plot")
ax.legend()
fig.tight_layout()
plt.savefig("funnel_plot.png", dpi=150)
plt.show()
# Simulate p-values from effect sizes and sample sizes
p_vals_list = []
for _, row in df_meta.iterrows():
t_stat = row["g"] / np.sqrt(row["var_g"])
df_test = row["n1"] + row["n2"] - 2
p_val = 2 * stats.t.sf(abs(t_stat), df=df_test)
p_vals_list.append(p_val)
p_vals = np.array(p_vals_list)
print(f"\nP-values: {np.round(p_vals, 4)}")
# P-curve analysis
p_curve_res = p_curve_analysis(p_vals, alpha=0.05)
# P-curve visualization
sig_p = p_vals[p_vals < 0.05]
fig2, ax2 = plt.subplots(figsize=(8, 4))
bins = np.linspace(0, 0.05, 6)
ax2.hist(sig_p, bins=bins, color="steelblue", alpha=0.7, edgecolor="white", label="Observed")
ax2.axhline(len(sig_p) / 5, color="crimson", linestyle="--",
linewidth=2, label="Expected under H0 (uniform)")
ax2.set_xlabel("p-value")
ax2.set_ylabel("Frequency")
ax2.set_title("P-Curve")
ax2.legend()
fig2.tight_layout()
plt.savefig("p_curve.png", dpi=150)
plt.show()
print("Meta-analysis complete.")
| Version | Date | Change |
|---|---|---|
| 1.0.0 | 2026-03-18 | Initial release — Hedges' g, DL random-effects, forest/funnel plots, Egger, PET-PEESE, p-curve |