psychmeta (version 2.6.4)

sensitivity: Sensitivity analyses for meta-analyses

Description

Wrapper function to compute bootstrap analyses, leave-one-out analyses, and cumulative meta-analyses. This function helps researchers to examine the stability/fragility of their meta-analytic results with bootstrapping and leave-one-out analyses, as well as detect initial evidence of publication bias with cumulative meta-analyses.

Usage

sensitivity(
  ma_obj,
  leave1out = TRUE,
  bootstrap = TRUE,
  cumulative = TRUE,
  sort_method = c("weight", "n", "inv_var"),
  boot_iter = 1000,
  boot_conf_level = 0.95,
  boot_ci_type = c("bca", "norm", "basic", "stud", "perc"),
  ...
)
sensitivity_bootstrap(
  ma_obj,
  boot_iter = 1000,
  boot_conf_level = 0.95,
  boot_ci_type = c("bca", "norm", "basic", "stud", "perc"),
  ...
)
sensitivity_cumulative(ma_obj, sort_method = c("weight", "n", "inv_var"), ...)
sensitivity_leave1out(ma_obj, ...)

Value

An updated meta-analysis object with sensitivity analyses added.

• When bootstrapping is performed, the bootstrap section of the follow_up_analyses section of the updated ma_obj returned by this function will contain both a matrix summarizing the mean, variance, and confidence intervals of the bootstrapped samples and a table of meta-analytic results from all bootstrapped samples.

• When leave-one-out analyses are performed, the ma_obj will acquire a list of leave-one-out results in its follow_up_analyses section that contains a table of all leave-one-out meta-analyses along with plots of the mean and residual variance of the effect sizes in the meta-analyses.

• When cumulative meta-analysis is performed, the ma_obj will acquire a list of cumulative meta-analysis results in its follow_up_analyses section that contains a table of all meta-analyses computed along with plots of the mean and residual variance of the effect sizes in the meta-analyses, sorted by the order in which studies were added to the meta-analysis.

Arguments

ma_obj

Meta-analysis object.

leave1out

Logical scalar determining whether to compute leave-one-out analyses (TRUE) or not (FALSE).

bootstrap

Logical scalar determining whether bootstrapping is to be performed (TRUE) or not (FALSE).

cumulative

Logical scalar determining whether a cumulative meta-analysis is to be computed (TRUE) or not (FALSE).

sort_method

Method to sort samples in the cumulative meta-analysis. Options are "weight" to sort by weight (default), "n" to sort by sample size, and "inv_var" to sort by inverse variance.

boot_iter

Number of bootstrap iterations to be computed.

boot_conf_level

Width of confidence intervals to be constructed for all bootstrapped statistics.

boot_ci_type

Type of bootstrapped confidence interval. Options are "bca", "norm", "basic", "stud", and "perc" (these are "type" options from the boot::boot.ci function). Default is "bca". Note: If you have too few iterations, the "bca" method will not work and you will need to either increase the iterations or choose a different method.

...

Additional arguments.

Examples

if (FALSE) {
## Run a meta-analysis using simulated correlation data:
ma_obj <- ma_r_ic(rxyi = rxyi, n = n, rxx = rxxi, ryy = ryyi, ux = ux,
                  correct_rr_y = FALSE, data = data_r_uvirr)
ma_obj <- ma_r_ad(ma_obj, correct_rr_y = FALSE)

## Pass the meta-analysis object to the sensitivity() function:
ma_obj <- sensitivity(ma_obj = ma_obj, boot_iter = 10,
                      boot_ci_type = "norm", sort_method = "inv_var")

## Examine the tables and plots produced for the IC meta-analysis:
ma_obj$bootstrap[[1]]$barebones
ma_obj$bootstrap[[1]]$individual_correction$true_score
ma_obj$leave1out[[1]]$individual_correction$true_score
ma_obj$cumulative[[1]]$individual_correction$true_score

## Examine the tables and plots produced for the AD meta-analysis:
ma_obj$bootstrap[[1]]$artifact_distribution$true_score
ma_obj$leave1out[[1]]$artifact_distribution$true_score
ma_obj$cumulative[[1]]$artifact_distribution$true_score


## Run a meta-analysis using simulated d-value data:
ma_obj <- ma_d_ic(d = d, n1 = n1, n2 = n2, ryy = ryyi,
                  data = filter(data_d_meas_multi, construct == "Y"))
ma_obj <- ma_d_ad(ma_obj)
                  
## Pass the meta-analysis object to the sensitivity() function:
ma_obj <- sensitivity(ma_obj = ma_obj, boot_iter = 10,
                      boot_ci_type = "norm", sort_method = "inv_var")

## Examine the tables and plots produced for the IC meta-analysis:
ma_obj$bootstrap[[1]]$barebones
ma_obj$bootstrap[[1]]$individual_correction$latentGroup_latentY
ma_obj$leave1out[[1]]$individual_correction$latentGroup_latentY
ma_obj$cumulative[[1]]$individual_correction$latentGroup_latentY

## Examine the tables and plots produced for the AD meta-analysis:
ma_obj$bootstrap[[1]]$artifact_distribution$latentGroup_latentY
ma_obj$leave1out[[1]]$artifact_distribution$latentGroup_latentY
ma_obj$cumulative[[1]]$artifact_distribution$latentGroup_latentY
}