Package {ProMetaR}

Type: Package
Title: Meta-Analysis of Proportions and Prevalence
Version: 1.0.0
Description: Tools for meta-analysis of proportions and prevalence from studies reporting event counts and sample sizes. Provides transformed and untransformed inverse-variance models, random-effects estimation, heterogeneity statistics, prediction intervals, subgroup analysis, meta-regression, leave-one-out sensitivity analysis, influence diagnostics, forest plots, funnel plots, and an optional binomial generalized linear mixed model interface. The package is designed for epidemiological, veterinary, medical, and One Health applications, including antimicrobial resistance prevalence studies.
License: MIT + file LICENSE
Encoding: UTF-8
Language: en-US
Depends: R (≥ 4.1.0)
Imports: stats, graphics
Suggests: testthat (≥ 3.0.0), metafor, knitr, rmarkdown
Config/testthat/edition: 3
Config/roxygen2/version: 8.1.0
URL: https://github.com/vinodhpmd/ProMetaR
BugReports: https://github.com/vinodhpmd/ProMetaR/issues
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-09-03 09:26:33 UTC; m
Author: Vinodhkumar Obli Rajendran [aut, cre], Keerthi Aaradhana [aut]
Maintainer: Vinodhkumar Obli Rajendran <vinodhkumar.rajendran@gmail.com>
Repository: CRAN
Date/Publication: 2026-09-12 14:10:33 UTC

ProMetaR: Meta-Analysis of Proportions

Description

Tools for meta-analysis of proportions and prevalence, including transformation-based random-effects models, heterogeneity assessment, prediction intervals, subgroup analysis, meta-regression, sensitivity analysis, influence diagnostics, and graphical methods.

Author(s)

Maintainer: Vinodhkumar Obli Rajendran vinodhkumar.rajendran@gmail.com

Authors:

See Also

Useful links:

Small-study effect diagnostic

Description

Performs an Egger-type regression diagnostic for possible small-study effects.

Usage

bias_prop(x)

Arguments

x

A prometar object returned by meta_prop().

Value

An object of class prometar_bias containing the regression intercept, its standard error, test statistic, p-value, and fitted regression model.

Forest plot for a proportion meta-analysis

Description

Forest plot for a proportion meta-analysis

Usage

forest_prop(x, xlab = "Proportion", xlim = c(0, 1), ...)

Arguments

x

A prometar object.

xlab

X-axis label.

xlim

Axis limits.

...

Graphical arguments.

Value

Invisibly returns x.

Funnel plot for a proportion meta-analysis

Description

Funnel plot for a proportion meta-analysis

Usage

funnel_prop(x, ...)

Arguments

x

A prometar object.

...

Graphical arguments.

Value

Invisibly returns x.

Influence diagnostics

Description

Calculates simple influence measures based on leave-one-out meta-analysis results.

Usage

influence_prop(x)

Arguments

x

A prometar object returned by meta_prop().

Value

A data frame containing the leave-one-out estimate, its change from the full analysis, and the corresponding I-squared statistic.

Leave-one-out sensitivity analysis

Description

Repeats the meta-analysis after removing each study in turn.

Usage

loo_prop(x)

Arguments

x

A prometar object returned by meta_prop().

Value

A data frame containing the omitted study, pooled estimate, confidence limits, and I-squared statistic for each leave-one-out analysis.

Meta-analysis of proportions

Description

Performs a meta-analysis of proportions using a transformation-based random-effects model.

Usage

meta_prop(
  events,
  n,
  studlab = NULL,
  data = NULL,
  method = c("REML", "DL", "PM"),
  transform = c("logit", "arcsine", "raw", "pft"),
  correction = 0.5,
  level = 0.95,
  prediction = TRUE
)

Arguments

events

Number of events in each study.

n

Sample size in each study.

studlab

Optional study labels.

data

Optional data frame containing the variables.

method

Random-effects estimator: "REML", "DL", or "PM".

transform

Transformation: "logit", "arcsine", "raw", or "pft".

correction

Continuity correction for extreme logit proportions.

level

Confidence level.

prediction

Logical; calculate a prediction interval.

Value

An object of class prometar.

Optional binomial GLMM via metafor

Description

Fits a binomial generalized linear mixed model for proportions using metafor.

Usage

meta_prop_glmm(events, n, studlab = NULL, ...)

Arguments

events

Number of events in each study.

n

Sample size for each study.

studlab

Optional study labels.

...

Additional arguments passed to metafor::rma.glmm().

Value

A fitted GLMM object returned by metafor::rma.glmm().

Meta-regression for proportions

Description

Fits a weighted linear meta-regression model using transformed study-level proportions as the response.

Usage

metareg_prop(x, moderators, ...)

Arguments

x

A prometar object returned by meta_prop().

moderators

A data frame containing moderator variables, with one row per study.

...

Additional arguments reserved for future extensions.

Value

An object of class prometar_metareg, containing regression coefficients, standard errors, test statistics, p-values, fitted values, residuals, moderator data, and the transformation used in the original meta-analysis.

Prediction interval

Description

Prediction interval

Usage

predict_prop(x)

Arguments

x

A prometar object.

Value

A two-element vector.

Heterogeneity statistics

Description

Heterogeneity statistics

Usage

prop_heterogeneity(x)

Arguments

x

A prometar object.

Value

A named list of heterogeneity statistics.

Transform proportions

Description

Transform proportions

Usage

prop_transform(
  events,
  n,
  method = c("logit", "arcsine", "raw", "pft"),
  correction = 0.5
)

Arguments

events

Number of events.

n

Sample size.

method

Transformation method.

correction

Continuity correction.

Value

A data frame of transformed study-level values.

Subgroup meta-analysis

Description

Performs separate meta-analyses of proportions within levels of a categorical subgroup variable.

Usage

subgroup_prop(x, subgroup, ...)

Arguments

x

A prometar object returned by meta_prop().

subgroup

A categorical vector identifying the subgroup for each study. Its length must equal the number of studies in x.

...

Additional arguments passed to meta_prop().

Value

An object of class prometar_subgroup, containing a fitted prometar object for each subgroup.

Summarize a ProMetaR meta-analysis

Description

Produces a compact summary of a meta-analysis of proportions.

Usage

summary_prop(x, digits = 4)

Arguments

x

A prometar object.

digits

Number of decimal places.

Value

A data frame containing the main meta-analysis results.