Type: Package
Title: Web Application to Run Meta-Analyses
Version: 0.3.2
Date: 2024-06-23
Maintainer: Theodore Lytras <thlytras@gmail.com>
Imports: shiny, shinyjs, shinyWidgets, colourpicker, rhandsontable, metafor, markdown, WriteXLS, readxl, jsonlite, grDevices, methods, stats
Depends: meta (≥ 7.0-0), R (≥ 4.0.0)
Description: Shiny web application to run meta-analyses. Essentially a graphical front-end to package 'meta' for R. Can be useful as an educational tool, and for quickly analyzing and sharing meta-analyses. Provides output to quickly fill in GRADE (Grading of Recommendations, Assessment, Development and Evaluations) Summary-of-Findings tables. Importantly, it allows further processing of the results inside R, in case more specific analyses are needed.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
URL: https://github.com/thlytras/miniMeta
BugReports: https://github.com/thlytras/miniMeta/issues
NeedsCompilation: no
Packaged: 2024-06-22 19:55:01 UTC; bones
Author: Theodore Lytras ORCID iD [aut, cre]
Repository: CRAN
Date/Publication: 2024-06-22 23:50:02 UTC

Get analytical options from miniMeta object

Description

This function returns the analytical options stored in a miniMeta object, as a named list of arguments, for further processing.

Usage

analysisOptions(x, data = FALSE)

Arguments

x

An object of class miniMeta

data

If TRUE, the data associated with the meta-analysis are included in the return value. This allows you for example to run: do.call(metabin, analysisOptions(x, data=TRUE)) or do.call(metagen, analysisOptions(x, data=TRUE)) (depending on the contents of x), in order to re-run the meta-analysis.

Value

A named list of arguments corresponding to the arguments of metagen or metabin.

Examples

analysisOptions(example_miniMeta_rct)

Return miniMeta analysis as source code

Description

Returns an entire miniMeta analysis in an R source code format. This provides a basis for further processing the results exported from miniMeta, using R code, in order to perform more elaborate or more specific analyses.

Usage

as.source(x)

Arguments

x

An object of class miniMeta

Value

A character vector of length one, containing R code that loads the data, runs the meta-analysis, and plots a forest plot. You can save this in a text file using writeLines.

Examples

fname <- tempfile("my_analysis", fileext = ".R")
fname
# Writes the miniMeta analysis to an R script
writeLines(as.source(example_miniMeta_rct), fname)

Example miniMeta analyses

Description

These are example miniMeta meta-analyses, with study data taken from Lytras et al, 2014. Object example_miniMeta_rct contains a meta-analysis of Randomized Controlled Trials (RCTs), and example_miniMeta_obs a meta-analysis of observational studies.

Usage

example_miniMeta_obs

example_miniMeta_rct

Format

Objects of class miniMeta

An object of class miniMeta (inherits from list) of length 4.

An object of class miniMeta (inherits from list) of length 4.

References

Lytras T, Nikolopoulos G, Bonovas S. World J Gastroenterol 2014;20(7):1858-70 (PubMed)

Forest plot for miniMeta objects

Description

Draws a forest plot for a miniMeta object using the options stored in the object

Usage

## S3 method for class 'miniMeta'
forest(x, ...)

Arguments

x

An object of class miniMeta

...

Further arguments passed to or from other methods

Examples

forest(example_miniMeta_obs)

Is this a miniMeta object?

Description

This function checks whether this is a valid miniMeta object

Usage

is.miniMeta(x)

Arguments

x

An object of class miniMeta

Value

TRUE if it is a valid miniMeta object, FALSE if it is not.

Examples

is.miniMeta(example_miniMeta_obs) # returns TRUE
is.miniMeta(example_miniMeta_rct) # returns TRUE

Is this a miniMeta object for observational studies?

Description

This function checks whether this is a valid miniMeta object holding a a meta-analysis of observational studies.

Usage

is.miniMeta.obs(x)

Arguments

x

An object of class miniMeta

Value

TRUE if it is a valid miniMeta object holding a meta-analysis of observational studies, FALSE if it is not.

Examples

is.miniMeta.obs(example_miniMeta_obs) # returns TRUE
is.miniMeta.obs(example_miniMeta_rct) # returns FALSE

Is this a miniMeta object for RCTs?

Description

This function checks whether this is a valid miniMeta object holding a a meta-analysis of Randomized Controlled Trials (RCTs).

Usage

is.miniMeta.rct(x)

Arguments

x

An object of class miniMeta

Value

TRUE if it is a valid miniMeta object holding a meta-analysis of Randomized Controlled Trials (RCTs), FALSE if it is not.

Examples

is.miniMeta.rct(example_miniMeta_obs) # returns FALSE
is.miniMeta.rct(example_miniMeta_rct) # returns TRUE

Launch miniMeta in your browser

Description

This function lanuches miniMeta in your browser

Usage

miniMeta()

Examples

## Not run: 
miniMeta()

## End(Not run)

Parse arguments from a comma-separated list

Description

Read a comma-separated list of arguments (as a character string), parse them, and return as a named R list. This function is used in miniMeta to parse arguments for forest.meta() when given as a string.

Usage

parseArguments(x)

Arguments

x

A character vector (of length one) containing the arguments. All should be named.

Value

A named list of arguments, or an object of class "try-error" on failure.

Examples

parseArguments('col.diamond="red", sm="RR", common=FALSE')

Get forest plot options from miniMeta object

Description

This function returns the forest plot options stored in a miniMeta object, as a named list of arguments, for further processing. This allows finer control than directly plotting using the forest.miniMeta method. See the example below.

Usage

plotOptions(x)

Arguments

x

An object of class miniMeta

Value

A named list of arguments corresponding to the arguments of forest.meta.

Examples

## Not run: 
# Extract the plot options from the miniMeta object
plot_opts <- plotOptions(example_miniMeta_obs)
# Call directly the forest.meta method, with all plot options
do.call(forest, c(x=list(example_miniMeta_obs$meta), plot_opts))

# Equivalently, call the forest.miniMeta method directly
forest(example_miniMeta_obs)

## End(Not run)

Sample size calculator for binary outcomes

Description

Calculates sample size for a trial with a binomial outcome, for a given power and false positive rate.

Usage

sampleSizeBin(cer, RRR = 25, ier = NULL, a = 0.05, b = 0.2, K = 1)

Arguments

cer

Control group event rate, a value between 0 and 1. All should be named.

RRR

Relative Risk Reduction (%) in the intervention group.

ier

Intervention group event rate, a value between 0 and 1 If NULL, it is calculated from RRR. If non-NULL, the value of this argument is used and RRR is ignored.

a

False positive rate (alpha). Defaults to 0.05 (5%).

b

False negative rate (beta). Defaults to 0.2. Power is one minus beta; thus the default is 80% power.

K

Ratio of intervention group size to control group size. Defaults to 1, meaning both groups have the same size. Set to infinity (Inf) in order to calculate sample size for a single-group study, see details below.

Value

An integer vector of length 2, with the sample sizes for the control and intervention groups.

If K=Inf, then the sample size calculation is not for a study with two groups, but for a single-group study in which a fixed known population event rate is assumed. In that case, argument cer represents the population event rate, and ier the study event rate that it we anticipate. And the return value is a single value, i.e. the sample size of the study.

Examples

# Sample size for a trial with 40\% control event rate and 1:1 randomization,
# aiming to show a Relative Risk Reduction of 30\% with 80\% power.
sampleSizeBin(0.4, RRR=30)

# Sample size for a single-group study aiming to show an event rate of 20\%
# against a population event rate of 10\%, with 90\% power.
sampleSizeBin(0.1, ier=0.2, b=0.1, K=Inf)

Sample size calculator for continuous outcomes

Description

Calculates sample size for a trial with a continuous outcome, for a given power and false positive rate.

Usage

sampleSizeCont(Dm, SD, a = 0.05, b = 0.2, K = 1)

Arguments

Dm

Anticipated absolute difference in means between the two groups (intervention and control).

SD

Anticipated standard deviation for the outcome.

a

False positive rate (alpha). Defaults to 0.05 (5%).

b

False negative rate (beta). Defaults to 0.2. Power is one minus beta; thus the default is 80% power.

K

Ratio of intervention group size to control group size. Defaults to 1, meaning both groups have the same size. Set to infinity (Inf) in order to calculate sample size for a single-group study, see details below.

Value

An integer vector of length 2, with the sample sizes for the control and intervention groups.

If K=Inf, then the sample size calculation is not for a study with two groups, but for a single-group study in which we try to show a difference from a fixed known population mean. In that case, argument Dm represents the absolute difference between the study mean and population mean, rather than the difference in means between two groups. And the return value is a single value, i.e. the sample size of the study.

Examples

# Sample size for a trial with 2:1 randomization, aiming to show a mean
# difference of 2 for a continuous outcome with a standard deviation of 3, 
# with 90\% power.
sampleSizeCont(2, 3, b=0.1, K=2)

# Similar for a single-group study aiming to show a difference of 2 against
# a known population mean. 
sampleSizeCont(2, 3, b=0.1, K=Inf)