phacking_meta: Right-truncated meta-analysis

In phacking: Sensitivity Analysis for p-Hacking in Meta-Analyses

Right-truncated meta-analysis

Description

Fits right-truncated meta-analysis (RTMA), a bias correction for the joint effects of p-hacking (i.e., manipulation of results within studies to obtain significant, positive estimates) and traditional publication bias (i.e., the selective publication of studies with significant, positive results) in meta-analyses. This method analyzes only nonaffirmative studies (i.e., those with significant, positive estimates). You can pass all studies in the meta-analysis or only the nonaffirmative ones; if the former, the function will still analyze only the nonaffirmative ones.

Usage

phacking_meta(
  yi,
  vi,
  sei,
  favor_positive = TRUE,
  alpha_select = 0.05,
  ci_level = 0.95,
  stan_control = list(adapt_delta = 0.98, max_treedepth = 20),
  parallelize = TRUE
)

Arguments

yi	A vector of point estimates to be meta-analyzed.
vi	A vector of estimated variances (i.e., squared standard errors) for the point estimates.
sei	A vector of estimated standard errors for the point estimates. (Only one of vi or sei needs to be specified).
favor_positive	TRUE if publication bias are assumed to favor significant positive estimates; FALSE if assumed to favor significant negative estimates.
alpha_select	Alpha level at which an estimate's probability of being favored by publication bias is assumed to change (i.e., the threshold at which study investigators, journal editors, etc., consider an estimate to be significant).
ci_level	Confidence interval level (as proportion) for the corrected point estimate. (The alpha level for inference on the corrected point estimate will be calculated from ci_level.)
stan_control	List passed to rstan::sampling() as the control argument.
parallelize	Logical indicating whether to parallelize sampling.

Value

An object of class metabias::metabias(), a list containing:

data

A tibble with one row per study and the columns yi, vi, sei, affirm.

values

A list with the elements favor_positive, alpha_select, ci_level, tcrit, k, k_affirmative, k_nonaffirmative, optim_converged. optim_converged indicates whether the optimization to find the posterior mode converged.

stats

A tibble with two rows and the columns param, mode, median, mean, se, ci_lower, ci_upper, n_eff, r_hat. We recommend reporting the mode for the point estimate; median and mean represent posterior medians and means respectively.

fit

A stanfit object (the result of fitting the RTMA model).

References

\insertRef

mathur2022phackingmetabias

Examples

# passing all studies, though only nonaffirmative ones will be analyzed
money_priming_rtma <- phacking_meta(money_priming_meta$yi, money_priming_meta$vi,
                                    parallelize = FALSE)

phacking documentation built on July 26, 2023, 5:22 p.m.