MAIVE (version 0.2.4)

waive: WAIVE: More Aggressive Correction for P-Hacking and Spurious Precision

Description

WAIVE (Weighted Adjusted Instrumental Variable Estimator) provides a more aggressive correction for p-hacking and spurious precision by extending MAIVE with exponential-decay weights that downweight both spuriously precise estimates and extreme outliers.

Usage

waive(
  dat,
  method,
  weight,
  instrument,
  studylevel,
  SE,
  AR,
  first_stage = 0L,
  estimate = NULL,
  se = NULL,
  n = NULL,
  study_id = NULL,
  seed = 123
)

Value

List with the same structure as maive(). See ?maive for details.

Arguments

dat

Data frame with columns bs, sebs, Ns, study_id (optional).

method

1 FAT-PET, 2 PEESE, 3 PET-PEESE, 4 EK.

weight

0 no weights, 1 standard weights, 2 MAIVE adjusted weights, 3 study weights.

instrument

1 yes, 0 no.

studylevel

Correlation at study level: 0 none, 1 fixed effects, 2 cluster.

SE

SE estimator: 0 CR0 (Huber-White), 1 CR1 (Standard empirical correction), 2 CR2 (Bias-reduced estimator), 3 wild bootstrap.

AR

Anderson Rubin corrected CI for weak instruments (available for unweighted and MAIVE-adjusted weight versions of PET, PEESE, PET-PEESE, not available for fixed effects): 0 no, 1 yes.

first_stage

First-stage specification for the variance model: 0 levels, 1 log.

estimate

Optional column name to use instead of 'bs'

se

Optional column name to use instead of 'sebs'

n

Optional column name to use instead of 'Ns'

study_id

Optional column name for study identifiers

seed

Seed for the wild bootstrap when SE = 3. Use NULL to avoid setting a seed (results depend on the current RNG state). Default is 123 for historical reproducibility.

Details

Guided, interactive workflow available at https://www.easymeta.org.

For technical details on WAIVE methodology, see: https://meta-analysis.cz/waive_ottawa.pdf

WAIVE combines variance instrumentation (as in MAIVE) with robust downweighting based on first-stage residuals. Studies with negative residuals (spurious precision) or extreme residuals (outliers) receive exponentially reduced influence in the meta-analytic estimate. This makes WAIVE more aggressive than standard MAIVE at correcting for p-hacking and handling outliers.

Examples

dat <- data.frame(
  bs = c(0.5, 0.45, 0.55, 0.6),
  sebs = c(0.25, 0.2, 0.22, 0.27),
  Ns = c(50, 80, 65, 90)
)

result <- waive(dat,
  method = 3, weight = 0, instrument = 1,
  studylevel = 0, SE = 0, AR = 0, first_stage = 0
)