psychmeta (version 0.2.4)

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 = "weight", boot_iter = 1000, boot_conf_level = 0.95,
  boot_ci_type = "bca", ...)
sensitivity_bootstrap(ma_obj, boot_iter = 1000, boot_conf_level = 0.95,
  boot_ci_type = "bca", ...)
sensitivity_cumulative(ma_obj, sort_method = "weight", ...)
sensitivity_leave1out(ma_obj, ...)

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 (see "type" options for boot::boot.ci for possible arguments). Default is "bca".

...

Additional arguments.

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 boostrapped 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.

Examples

# NOT RUN {
## Run a meta-analysis using simulated UVIRR 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, cumulative = TRUE,
boot_ci_type = "norm", sort_method = "inv_var")

## Examine the tables and plots produced for the IC meta-analysis:
ma_obj$follow_up_analyses$bootstrap$barebones$`Analysis ID = 1`
ma_obj$follow_up_analyses$bootstrap$individual_correction$true_score$`Analysis ID = 1`
ma_obj$follow_up_analyses$leave1out$individual_correction$true_score$`Analysis ID = 1`
ma_obj$follow_up_analyses$cumulative$individual_correction$true_score$`Analysis ID = 1`

## Examine the tables and plots produced for the AD meta-analysis:
ma_obj$follow_up_analyses$bootstrap$artifact_distribution$true_score$`Analysis ID = 1`
ma_obj$follow_up_analyses$leave1out$artifact_distribution$true_score$`Analysis ID = 1`
ma_obj$follow_up_analyses$cumulative$artifact_distribution$true_score$`Analysis ID = 1`
# }