meta_any: Estimate any meta effect.

In rcalinjageman/esci: Estimation Statistics with Confidence Intervals

Estimate any meta effect.

Description

meta_any is suitable for synthesizing any effect size across multiple studies. You must provide the effect size for each study and the predicted sampling variance for each study.

Usage

meta_any(
  data,
  yi,
  vi,
  labels = NULL,
  moderator = NULL,
  contrast = NULL,
  effect_label = "My effect",
  effect_size_name = "Effect size",
  moderator_variable_name = "My moderator",
  random_effects = TRUE,
  conf_level = 0.95
)

Arguments

data	A data frame or tibble with columns
yi	Name a column in data containing the effect size for each study
vi	Name of a column in data containing the expected sampling variance for each study
labels	Name of a column in data containing a label for each study
moderator	Optional name of a column in data containing a factor as a categorical moderator
contrast	Optional vector specifying a contrast analysis for the categorical moderator. Only define if a moderator is defined; vector length should match number of levels in the moderator
effect_label	Optional human-friendly name for the effect being synthesized; defaults to 'My effect'
effect_size_name	Optional human-friendly name of the effect size being synthesized; defaults to 'Effect size'
moderator_variable_name	Optional human-friendly name of the moderator, if defined; If not passed but a moderator is defined, will be set to the quoted name of the moderator column or 'My moderator'
random_effects	Use TRUE to obtain a random effect meta-analysis (usually recommended); FALSE for fixed effect.
conf_level	The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95.

Details

#' Once you generate an estimate with this function, you can visualize it with plot_meta().

The meta-analytic effect size, confidence interval and heterogeneity estimates all come from metafor::rma().

The diamond ratio and its confidence interval come from CI_diamond_ratio().

Value

An esci-estimate object; a list of data frames and properties. Returned tables include:

• es_meta - A data frame of meta-analytic effect sizes. If a moderator was defined, there is an additional row for each level of the moderator.

  • effect_label - Study label

  • effect_size - Effect size

  • LL - Lower bound of conf_level% confidence interval

  • UL - Upper bound of conf_level% confidence interval

  • SE - Expected standard error

  • k - Number of studies

  • diamond_ratio - ratio of random to fixed effects meta-analytic effect sizes

  • diamond_ratio_LL - lower bound of conf_level% confidence interval for diamond ratio

  • diamond_ratio_UL - upper bound of conf_level% confidence interval for diamond ratio

  • I2 - I2 measure of heterogeneity

  • I2_LL - Lower bound of conf_level% confidence interval for I2

  • I2_UL - upper bound of conf_level% confidence interval for I2

  • PI_LL - lower bound of conf_level% of prediction interval

  • PI_UL - upper bound of conf_level% of prediction interval

  • p - p value for the meta-analytic effect size, based on null of exactly 0

  • *width - width of the effect-size confidence interval

  • FE_effect_size - effect size of the fixed-effects model (regardless of if fixed effects was selected

  • RE_effect_size - effect size of the random-effects model (regardless of if random effects was selected

  • FE_CI_width - width of the fixed-effects confidence interval, used to calculate diamond ratio

  • RE_CI_width - width of the fixed-effects confidence interval, used to calculate diamond ratio

• es_heterogeneity - A data frame of of heterogeneity values and conf_level% CIs for the meta-analytic effect size. If a moderator was defined also reports heterogeneity estimates for each level of the moderator.

  • effect_label - study label

  • moderator_variable_name - if moderator passed, gives name of the moderator

  • moderator_level - 'Overall' and each level of moderator, if passed

  • measure - Name of the measure of heterogeneity

  • estimate - Value of the heterogeneity estimate

  • LL - lower bound of conf_level% confidence interval

  • UL - upper bound of conf_level% confidence interval

• raw_data - A data from with one row for each study that was passed

  • label - study label

  • effect_size - effect size

  • weight - study weight in the meta analysis

  • sample_variance - expected level of sampling variation

  • SE - expected standard error

  • LL - lower bound of conf_level% confidence interval

  • UL - upper bound of conf_level% confidence interval

  • mean - used to calculate study p value; this is the d value entered for the study

  • sd - use to calculate study p value; set to 1 for each study

  • n - study sample size

  • p - p value for the study, based on null of exactly 0

Examples

#' # Data set -- see Introduction to the New Statistics, 2nd edition
data("data_mccabemichael_brain")

# Fixed effect, 95% CI
esizes <- esci::meta_mean(
  data = esci::data_mccabemichael_brain,
  means = "M Brain",
  sds = "s Brain",
  ns = "n Brain",
  labels = "Study name",
  random_effects = FALSE
)$raw_data

estimate <- esci::meta_any(
  data = esizes,
  yi = effect_size,
  vi = sample_variance,
  labels = label,
  effect_size_name = "Mean",
  random_effects = FALSE
)

myplot_forest <- esci::plot_meta(estimate)

rcalinjageman/esci documentation built on Dec. 23, 2024, 2:37 p.m.