method.MAIVE: MAIVE: Meta-Analysis Instrumental Variable Estimator

In PublicationBiasBenchmark: Benchmark for Publication Bias Correction Methods

MAIVE: Meta-Analysis Instrumental Variable Estimator

Description

Implements the MAIVE method for publication bias correction using instrumental variable estimation with variance instrumentation. MAIVE addresses spurious precision in meta-analysis by instrumenting standard errors with inverse sample sizes, providing consistent estimates even when precision is manipulated through p-hacking \insertCiteirsova2025spuriousPublicationBiasBenchmark.

The method implements several estimators:

• PET (Precision-Effect Test): Linear precision-effect model

• PEESE (Precision-Effect Estimate with Standard Error): Quadratic model

• PET-PEESE: Conditional selection based on PET significance

• EK (Endogenous Kink): Flexible bias function with kink point

• WAIVE: Robust variant with outlier downweighting

Usage

## S3 method for class 'MAIVE'
method(method_name, data, settings)

Arguments

method_name	Method identifier (automatically passed by framework)
data	Data frame with yi (effect sizes), sei (standard errors), ni (sample sizes), and optionally study_id for clustering
settings	List of method settings from method_settings.MAIVE()

Details

MAIVE uses inverse sample sizes (1/N) as instruments for variances (SE^2) in the first stage, then uses the instrumented variances in second-stage PET/PEESE models. This approach provides consistent estimation when precision is endogenous due to p-hacking or selective reporting.

The Anderson-Rubin confidence interval is robust to weak instruments and is automatically computed for unweighted IV estimators when feasible (n < 5000). For weighted estimators or large samples, standard CIs are used. PublicationBiasBenchmark targets MAIVE 0.2.4 and persists upstream MAIVE warnings in the standardized note column.

WAIVE extends MAIVE by downweighting: (1) negative residuals (spurious precision) using exponential decay, and (2) extreme residuals (|z| > 2) as potential outliers. This provides additional robustness against publication bias and outliers. When IV is used and the first-stage F-statistic is numeric and below 10, PublicationBiasBenchmark blanks the standardized inferential outputs (estimate, standard_error, ci_lower, ci_upper, p_value) to NA while keeping convergence = TRUE. These rows remain available as diagnostics and are treated as missing estimates in downstream performance summaries rather than as convergence failures.

Available settings (see method_settings.MAIVE()):

default

PET-PEESE with IV, unweighted, levels first-stage

PET

PET with IV, unweighted

PEESE

PEESE with IV, unweighted

EK

Endogenous Kink model with IV

weighted

PET-PEESE with MAIVE-adjusted weighting

WAIVE

Robust WAIVE variant with outlier downweighting

log_first_stage

PET-PEESE with log-linear first stage

no_IV

Standard PET-PEESE without instrumentation (baseline)

Value

Single-row data frame with standardized output columns:

method

Method identifier

estimate

Meta-analytic effect size estimate

standard_error

Standard error of estimate

ci_lower, ci_upper

95% confidence interval bounds (Anderson-Rubin if available)

p_value

Two-tailed p-value

BF

Bayes factor (NA for MAIVE)

convergence

Logical convergence indicator

note

Error messages if any

first_stage_f

First-stage F-statistic for instrument strength

hausman_stat

Hausman test statistic comparing IV vs OLS

bias_p_value

P-value for publication bias test

used_ar_ci

Whether Anderson-Rubin CI was used

ar_ci_available

Whether AR CI was computed successfully

instrument_strength

Instrument strength classification returned by MAIVE or derived from the first-stage F-statistic

Author(s)

Petr Cala [e-mail]

References

\insertAllCited

See Also

run_method(), method_settings(), method_extra_columns()

Examples

## Not run: 
# Generate test data
data <- simulate_dgm("Stanley2017", condition_id = 1)

# Apply default MAIVE (PET-PEESE with IV)
result <- run_method("MAIVE", data, "default")

# Apply WAIVE variant
result_waive <- run_method("MAIVE", data, "WAIVE")

# Apply weighted MAIVE
result_weighted <- run_method("MAIVE", data, "weighted")

# View available configurations
method_settings("MAIVE")

## End(Not run)

PublicationBiasBenchmark documentation built on March 16, 2026, 5:07 p.m.