Version:	0.7
Date:	2021-02-15
Title:	Comprehensive Research Synthesis Tools for Systematic Reviews and Meta-Analysis
Description:	Functionalities for facilitating systematic reviews, data extractions, and meta-analyses. It includes a GUI (graphical user interface) to help screen the abstracts and titles of bibliographic data; tools to assign screening effort across multiple collaborators/reviewers and to assess inter- reviewer reliability; tools to help automate the download and retrieval of journal PDF articles from online databases; figure and image extractions from PDFs; web scraping of citations; automated and manual data extraction from scatter-plot and bar-plot images; PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagrams; simple imputation tools to fill gaps in incomplete or missing study parameters; generation of random effects sizes for Hedges' d, log response ratio, odds ratio, and correlation coefficients for Monte Carlo experiments; covariance equations for modelling dependencies among multiple effect sizes (e.g., effect sizes with a common control); and finally summaries that replicate analyses and outputs from widely used but no longer updated meta-analysis software (i.e., metawin). Funding for this package was supported by National Science Foundation (NSF) grants DBI-1262545 and DEB-1451031. CITE: Lajeunesse, M.J. (2016) Facilitating systematic reviews, data extraction and meta-analysis with the metagear package for R. Methods in Ecology and Evolution 7, 323-330 <doi:10.1111/2041-210X.12472>.
Type:	Package
Depends:	R (≥ 3.3.2)
Imports:	Matrix, MASS, metafor (≥ 1.9-9), stringr
SystemRequirements:	Tcl/Tk toolkit (X11 Quarts for Mac)
VignetteBuilder:	R.rsp
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	http://lajeunesse.myweb.usf.edu/ https://github.com/mjlajeunesse/ https://www.youtube.com/c/LajeunesseLab/
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.1.1
Suggests:	EBImage, ape, hexView, RCurl, testthat, R.rsp
NeedsCompilation:	no
Packaged:	2021-02-15 14:55:42 UTC; lajeunesse
Author:	Marc J. Lajeunesse [aut, cre]
Maintainer:	Marc J. Lajeunesse <lajeunesse@usf.edu>
Repository:	CRAN
Date/Publication:	2021-02-15 15:40:02 UTC

Research synthesis tools to facilitate systematic reviews, data extraction, and meta-analysis.

Description

metagear is a comprehensive, multifunctional toolbox with capabilities aimed to cover much of the research synthesis taxonomy: from applying a systematic review approach to objectively assemble and screen the literature, to extracting data from studies, and to finally summarize and analyze these data with the statistics of meta-analysis. More information about metagear can be found at http://lajeunesse.myweb.usf.edu/.

Details

What to cite?

Lajeunesse, M.J. (2016) Facilitating systematic reviews, data extraction and meta-analysis with the metagear package for R. Methods in Ecology and Evolution 7: 323-330. [ download here ]

Installation and Dependencies.

metagear has one external dependency that need to be installed and loaded prior to use in R. This is the EBImage R package (Pau et al. 2010) available only from the Bioconductor repository: https://www.bioconductor.org/.

To properly install metagear, start with the following R script that loads the Bioconductor resources needed to install the EBImage (also accept all of its dependencies):

install.packages("BiocManager");
BiocManager::install("EBImage"))
library(metagear)

Finally for Mac OS users, installation is sometimes not straighforward as the abstract_screener() requires the Tcl/Tk GUI toolkit to be installed. You can get this toolkit by making sure the latest X11 application (xQuartz) is installed from here: https://www.xquartz.org/.

Author(s)

Marc J. Lajeunesse (University of South Florida, Tampa USA)

References

Pau, G., Fuchs, F., Sklyar, O., Boutros, M. and Huber, W. (2010) EBImage: an R package for image processing with applications to cellular phenotypes. Bioinformatics 26: 979-981.

An example image of a scatterplot figure

Description

A jpg image of a scatterplot from Figure 2 of Kam, M., Cohen-Gross, S., Khokhlova, I.S., Degen, A.A. and Geffen, E. 2003. Average daily metabolic rate, reproduction and energy allocation during lactation in the Sundevall Jird Meriones crassus. Functional Ecology 17:496-503.

Format

A raw jpg-formated image

Note

How to use

readImage(system.file("images", "Kam_et_al_2003_Fig2.jpg", package = "metagear"))

An example image of a bar plot figure

Description

A jpg image of a bar plot from Figure 4 of Kortum, P., and Acymyan, C.Z. 2013. How low can you go? Is the System Usability Scale range restricted? Journal of Usability Studies 9:14-24.

Format

A raw jpg-formated image

Note

How to use

readImage(system.file("images", "Kortum_and_Acymyan_2013_Fig4.jpg", package = "metagear"))

Generate an ANOVA-like effects table for a meta-analysis.

Description

Generates an ANOVA-like effects table that summarizes the within and between-study homogeneity tests (Q-tests), as well as moderator level Q-tests as originally described by Hedges and Olkin (1985; p. 156).

Usage

MA_effectsTable(model, weights, data, effects_model = "random")

Arguments

Value

An lm object of main effects.

References

DerSimonian, R., and N. Laird. 1986. Meta-analysis in clinical trials. Controlled Clinical Trials, 7, 177-188.

Hedges, L.V., and I. Olkin. 1985. Statistical methods for meta-analysis. Academic Press, New York, USA.

Attempts to download a PDF using a DOI link.

Description

Tries to download a PDF file using the digital objected identifier (DOI) link. Uses ad hoc searches of journal HTML pages to detect candidate PDFs for download. Downloads all candidate pdfs. If running downloader in Windows, having "WindowsProxy = TRUE" will significantly improve download success.

Usage

PDF_download(
  DOI,
  directory = getwd(),
  theFileName = "temp",
  validatePDF = TRUE,
  quiet = FALSE,
  WindowsProxy = FALSE
)

Arguments

Value

A string describing the download success. If unsuccessful, returns the type of error during the download attempt.

See Also

PDFs_collect

Attempts to extract all images from a PDF

Description

Tries to extract images within a PDF file. Currently does not support decoding of images in CCITT compression formats. However, will still save these images to file; as a record of the number of images detected in the PDF.

Usage

PDF_extractImages(file = file.choose())

Arguments

Value

A vector of file names saved as images.

Attempts to download PDFs from multiple DOI links.

Description

Tries to download a collection of PDF files using multiple digital object identifier (DOI) links. Updates a data frame with the success of these downloads. The function is a wrapper for PDF_download. NOTE: A single DOI may generate multiple PDF files. If running downloader in Windows, having "WindowsProxy = TRUE" will significantly improve download success.

Usage

PDFs_collect(
  aDataFrame,
  DOIcolumn,
  FileNamecolumn,
  directory = getwd(),
  randomize = FALSE,
  seed = NULL,
  buffer = FALSE,
  validatePDF = TRUE,
  quiet = FALSE,
  showSummary = TRUE,
  WindowsProxy = FALSE
)

Arguments

Value

The data frame with new column containing download-outcome successes.

See Also

PDF_download

Examples

## Not run: 

data(example_references_metagear)
someRefs <- effort_initialize(example_references_metagear)  
dir.create("metagear_downloads")      
PDFs_collect(aDataFrame = someRefs, DOIcolumn = "DOI", 
             FileNamecolumn = "STUDY_ID", directory = "metagear_downloads",
			WindowsProxy = TRUE)

## End(Not run)

A GUI screener to quickly code candidate studies for inclusion/exclusion into a systematic review or meta-analysis.

Description

A GUI screener to help scan and evaluate the title and abstract of studies to be included in a systematic review or meta-analysis.

Usage

abstract_screener(
  file = file.choose(),
  aReviewer = NULL,
  reviewerColumnName = "REVIEWERS",
  unscreenedColumnName = "INCLUDE",
  unscreenedValue = "not vetted",
  abstractColumnName = "ABSTRACT",
  titleColumnName = "TITLE",
  browserSearch = "https://www.google.com/search?q=",
  fontSize = 13,
  windowWidth = 70,
  windowHeight = 16,
  theButtons = c("YES", "maybe", "NO"),
  keyBindingToButtons = c("y", "m", "n"),
  buttonSize = 10,
  highlightColor = "powderblue",
  highlightKeywords = NA
)

Arguments

Note

Installation and troubleshooting

For Mac OS users, installation is sometimes not straighforward as this screener requires the Tcl/Tk GUI toolkit to be installed. You can get this toolkit by making sure the latest X11 application (xQuartz) is installed, see here: https://www.xquartz.org/. More information on installation is found in metagear's vignette.

How to use the screener

The GUI itself will appear as a single window with the first title/abstract listed in the .csv file. If abstracts have already been screened/coded, it will begin at the nearest reference labeled as "not vetted". The SEARCH WEB button opens the default browser and searches Google with the title of the reference. The YES, MAYBE, NO buttons, which also have keyboard shortcuts y and n, are used to code the inclusion/exclusion of the reference. Once clicked/coded the next reference is loaded. The SAVE button is used to save the coding progress of screening tasks. It will save coding progress directly to the loaded .csv file. Closing the GUI, and not saving, will result in the loss of screening efforts relative to last save.

There is also an ISSUE FIXES menu bar with quick corrections to screening errors. These include ISSUE FIXES: REFRESH TITLE AND ABSTRACT TEXT which reloads the text of the current abstract in case portions were deleted when copying and pasting sections, ISSUE FIXES: STATUS OF CURRENT ABSTRACT which provides information on whether or not the abstract was previously screened, and ISSUE FIXES: RETURN TO PREVIOUS ABSTRACT that backtracks to the previous abstract if a selection error occurred (note a warning will appear of there is a change to its inclusion/exclusion coding).

Examples

## Not run: 

data(example_references_metagear)
effort_distribute(example_references_metagear,
                  initialize = TRUE,
                  reviewers = "marc",
                  save_split = TRUE)
abstract_screener("effort_marc.csv",
                  aReviewer = "marc",
                  highlightKeywords = "and")

## End(Not run)

Opens a web page associated with a DOI (digital object identifier).

Description

Uses the DOI name of a study reference to locate the e-journal website, or reference/citation website in Web of Science, Google Scholar, or CrossRef. Opens in default web-browser.

Usage

browse_DOI(theDOI, host = "DOI")

Arguments

Examples

## Not run: 

browse_DOI("10.1086/603628")        

## End(Not run)

A small tribute to Chachi.

Description

Rest easy little bud, 200?-2016.

Usage

chachi()

Generates a sampling variance-covariance matrix for modeling dependencies among effect sizes due to sharing a common control.

Description

Generates K by K sampling variance-covariance (VCV) matrix that models the dependencies that arise due to using the same control group study parameters when estimating multiple effect sizes. This VCV matrix can then be used in meta-analysis. Currently only supports VCV calculation for log response ratios (see Lajeunesse 2011).

Usage

covariance_commonControl(
  aDataFrame,
  control_ID,
  X_t,
  SD_t,
  N_t,
  X_c,
  SD_c,
  N_c,
  metric = "RR"
)

Arguments

Value

A K by K sampling variance-covariance matrix and a data frame aligned with the block diagonal design of the sampling matrix.

Note

Response Ratio's (\mathit{RR}) with a comon control group

Following Lajeunesse (2011), when two (or more) reponse ratio (\mathit{RR}) effect sizes share a common control mean (\bar{X}_C), such as \mathit{RR}_{A,C}=ln(\bar{X}_A/\bar{X}_C) and \mathit{RR}_{B,C}=ln(\bar{X}_B/\bar{X}_C), then they share a sampling covariance of:

cov(\mathit{\mathit{RR}}_{A,C},~\mathit{RR}_{B,C})=\frac{(\mathit{SD}_C)^2}{N_C\bar{X}_C^2},

where the \mathit{SD} and N are the standard deviation and sample size of \bar{X}_C, respectively.

References

Lajeunesse, M.J. 2011. On the meta-analysis of response ratios for studies with correlated and multi-group designs. Ecology 92: 2049-2055.

Assigns title/abstract screening efforts to a team.

Description

Randomly distributes screening tasks evenly or unevenly across multiple team members. It populates this effort in a data frame column that includes this screening work (e.g., ABSTRACTS and TITLES).

Usage

effort_distribute(
  aDataFrame = NULL,
  dual = FALSE,
  reviewers = NULL,
  column_name = "REVIEWERS",
  effort = NULL,
  initialize = FALSE,
  save_split = FALSE,
  directory = getwd()
)

Arguments

Value

A data.frame with title/abstract screening efforts randomly distributed across a team.

See Also

effort_initialize, effort_merge, effort_summary

Examples

## Not run: 

data(example_references_metagear)
theTeam <- c("Christina", "Luc")
effort_distribute(example_references_metagear, initialize = TRUE, reviewers = theTeam)

## End(Not run)

Formats a reference dataset for title/abstract screening efforts.

Description

Adds columns with standardized labels to a data framw with bibliographic data on journal articles. These columns will be used to assign reviewers, implementation of dual screening design, and the coding of inclusion/exclusions screening decisions.

Usage

effort_initialize(
  aDataFrame,
  study_ID = TRUE,
  unscreenedValue = "not vetted",
  dual = FALSE,
  front = TRUE
)

Arguments

Value

A data.frame formatted for title/abstract screening efforts.

See Also

effort_distribute, effort_merge, effort_summary

Examples

data(example_references_metagear)
effort_initialize(example_references_metagear)        

Merges multiple files that had title/abstract screening efforts distributed across a team.

Description

Combines (merges) multiple effort_*.csv files within the same directory that represent the completed screening efforts of multiple team members. These files were originally generated with effort_distribute.

Usage

effort_merge(directory = getwd(), reviewers = NULL, dual = FALSE)

Arguments

Value

A single data.frame merged from multiple files.

See Also

effort_initialize, effort_distribute, effort_summary

Examples

## Not run: 

data(example_references_metagear)
theTeam <- c("Christina", "Luc")
# warning effort_distribute below, will save two files to working 
# directory: effort_Christina.csv and effort_Luc.csv
effort_distribute(example_references_metagear, initialize = TRUE, 
                  reviewers = theTeam, save_split = TRUE)
effort_merge()

## End(Not run)

Redistributes title/abstract screening efforts among a review team.

Description

Randomly re-distributes screening tasks from one reviewers to the rest of the reviewing team. Used when screening effort needs to be re-allocated among reviewing team members.

Usage

effort_redistribute(
  aDataFrame,
  column_name = "REVIEWERS",
  reviewer = NULL,
  remove_effort = 100,
  reviewers = NULL,
  effort = NULL,
  save_split = FALSE,
  directory = getwd()
)

Arguments

Value

A single data.frame with effort re-allocated among team members.

Provides a text summary of screening efforts among the reviewing team.

Description

Summarizes the number of studies screened, which were identified to be included/excluded from the project, as well as those with conflicting agreement on their inclusion/exclusion. If a dual (paired) design was implemented to screen references, then it also provides inter-reviewer agreement estimate following Cohen (1960) that describes the agreement (or repeatability) of screening/coding decisions. The magnitudes of inter-reviewer agreement estimates are then interpreted following Landis & Koch (1977).

Usage

effort_summary(
  aDataFrame,
  column_reviewers = "REVIEWERS",
  column_effort = "INCLUDE",
  dual = FALSE,
  quiet = FALSE
)

Arguments

Value

A data frame with summary information on the screening tasks of a reviewing team.

References

Cohen, J. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20: 37-46.

Landis, J.R., and Koch, G.G. 1977. The measurement of observer agreement for categorical data. Biometrics 33: 159-174.

See Also

effort_initialize, effort_distribute, effort_merge

A collection of bibliographic references

Description

An example dataset containing 11 journal references. The variables are described below.

Usage

data(example_references_metagear)

Format

A data frame with 12 rows and 9 variables.

Details

• AUTHORS. Authors of the journal article

• YEAR. Publication year

• TITLE. Article title

• JOURNAL. Journal name

• VOLUME. Journal volume number

• LPAGES. Lower page number

• UPAGES. Upper page number

• DOI. Digital object identifier (DOI) of journal article

• ABSTRACT. Full text of the journal article abstract

Manually add/detect points to a scatter plot figure.

Description

Allows for the user to manually add an unlimited number of points to a figure image, by left-clicking over a figure's point. Click on the red upper-right box called "EXIT" to end recording the position of manually detected points.

Allows for the user to manually add an unlimited number of points to a figure image, by left-clicking over a figure/image point. Click on the red upper-right box called "EXIT" to end recording the position of manually detected points.

Usage

figure_add(file = file.choose(), color = "#009900", size = 0.03)

figure_add(file = file.choose(), color = "#009900", size = 0.03)

Arguments

Value

A data frame with detected points.

A data frame with detected points.

See Also

figure_detectAllPoints

figure_detectAllPoints

Detect and display all bar plot objects.

Description

Automated detection of grouped data displayed in a bar-plot/chart figure image. The default returns these detected objects as an EBImage raster image, and as a vector of all the estimated lengths that are proportional to the values presented on each bar (and their error bars, if they are present). Note that the extracted points will be sorted by their positioning on the X-axis (or Y if the plot is a horizontal bar plot). For example, if there were error bars in the figure these will be grouped with the detected bar column. However, within these X-axis positioning they will not be sorted. See vignette for worked several illustrations.

Usage

figure_barPlot(
  file = file.choose(),
  horizontal = FALSE,
  binary_threshold = 0.6,
  axis_thickness = 3,
  axis_sensitivity = 0.2,
  axis_length = 0.75,
  axis_X_color = "#00ABAB",
  axis_Y_color = "#B0D36A",
  Y_min = 0,
  Y_max = 100,
  bar_width = 9,
  bar_sensitivity = 0.1,
  point_color = "#0098B2",
  point_size = 9,
  ignore = FALSE
)

Arguments

Value

A vector of scaled lengths for detected column and error bars.

Automated detection of plotted points from a scatter-plot figure image.

Description

Attempts to detect all points of a certain shape and size from a scatter-plot figure image (even those lying outside of the axis range).

Usage

figure_detectAllPoints(
  aBinaryPlot,
  sensitivity = 0.2,
  point_shape = "circle",
  point_size = 5
)

Arguments

Value

An EBImage object with detected scatter-plot points.

See Also

figure_detectAxis

Detect an axis from a figure image.

Description

Attempts to detect either the X (horizontal) or Y (vertical) axis from a plotted figure.

Attempts to detect either the X (horizontal) or Y (vertical) axis from a plotted figure.

Usage

figure_detectAxis(
  aBinaryPlot,
  axis_type = "X",
  axis_thickness = 5,
  sensitivity = 0.2
)

figure_detectAxis(
  aBinaryPlot,
  axis_type = "X",
  axis_thickness = 5,
  sensitivity = 0.2
)

Arguments

Value

An EBImage object with detected points.

An EBImage object with detected points.

See Also

figure_detectAllPoints

figure_detectAllPoints

Displays an image plot.

Description

Displays a .jpg, .jpeg, .png, or .tiff image file containing a plotted figure, or plots an EBImage object.

Usage

figure_display(file = file.choose(), browser = FALSE)

Arguments

Value

An EBImage object figure.

See Also

figure_read

Displays detected points on figure.

Description

Generates a raster image of a figure with the detected points painted on a background/reference figure.

Usage

figure_displayDetectedPoints(
  aDetectedPlot,
  background = NULL,
  color = "red",
  size = 2,
  ignore = FALSE
)

Arguments

Value

A RGB EBImage painted with detected points.

See Also

figure_displayDetections

Displays the detected figure objects.

Description

Generates a raster image of a figure with the detected objects painted on a background/reference figure.

Usage

figure_displayDetections(
  aDetectedPlot,
  background = NULL,
  color = "red",
  ignore = FALSE
)

Arguments

Value

A RGB EBImage painted with detected figure objects.

Extracts data points from a detected image.

Description

Extracts raw X and Y data from the points detected in a scatter-plot figure.

Usage

figure_extractDetectedPoints(
  aDetectedPlot,
  xAxis = NULL,
  yAxis = NULL,
  X_min = NULL,
  X_max = NULL,
  Y_min = NULL,
  Y_max = NULL,
  summarize = TRUE
)

Arguments

Value

A data frame with the extracted X and Y values.

Reads/loads a figure image from file.

Description

Reads a .jpg, .jpeg, .png, or .tiff image file containing a plotted figure.

Usage

figure_read(file = file.choose(), display = FALSE)

Arguments

Value

An EBImage object figure.

See Also

figure_write

Remove outlier points from a figure.

Description

Removes all detected points outside of axis range. Requires three detected images: one based on figure_detectAllPoints, and two others based on detected X- and Y-axes (i.e. figure_detectAxis)

Usage

figure_removeOutlyingPoints(aDetectedPlot, xAxis = NULL, yAxis = NULL)

Arguments

Value

An EBImage object with detected points within the specified X- and Y-axis ranges.

Detect and display all scatter plot objects.

Description

Automated detection of the X-axis, Y-axis, and points on a scatter-plot figure image. The default returns these detected objects as an EBImage raster image, as well as the estimated effect size (correlation coefficient or r) of the data within the scatter-plot.

Usage

figure_scatterPlot(
  file = file.choose(),
  binary_threshold = 0.6,
  binary_point_fill = FALSE,
  binary_point_tolerance = 2,
  axis_thickness = 5,
  axis_sensitivity = 0.2,
  axis_X_color = "#00ABAB",
  X_min = 40,
  X_max = 140,
  axis_Y_color = "#B0D36A",
  Y_min = 40,
  Y_max = 140,
  point_sensitivity = 0.2,
  point_shape = "circle",
  point_size = 3,
  point_color = "#0098B2",
  ignore = FALSE
)

Arguments

Value

A data frame with detected points.

Splits a composite figure that contains multiple plots.

Description

Automatically detects divisions among multiple plots found within a single figure image file. It then uses these divisions to split the image into multiple image files; each containing only a single X-Y plot. Currently only works on composite figures that have a matrix-style presentation where each sub-plot has the same size.

Usage

figure_splitPlot(
  file = file.choose(),
  binary_threshold = 0.6,
  space_sensitivity_X = 0.4,
  space_sensitivity_Y = 0.6,
  border_buffer = 5,
  guess_limit = 10,
  ignoreX = FALSE,
  ignoreY = FALSE,
  quiet = FALSE
)

Arguments

Value

The number of sub-plots saved to file.

Transforms RBG figure into list of binary images.

Description

Generates a list of binary images relative to the number of colors in an RBG figure. Useful to do when there are multiple objects in a figure presented with different colors.

Usage

figure_transformByColors(aFigure, colorsToSplit = 2)

Arguments

Value

A colorsToSplit + 1 list of EBImage black and white objects. The final item in this list will be an inverse binary of the original figure.

See Also

figure_transformToBinary

Transforms figure to binary image.

Description

Transforms a figure into a black and white image. This pre-processing of the image is necessary to help identify objects within the figure (e.g., axes, plotted points).

Usage

figure_transformToBinary(
  aFigure,
  threshold = 0.6,
  point_fill = FALSE,
  point_tolerance = 2
)

Arguments

Value

An EBImage black and white object ready for object detection.

Saves/writes a figure image.

Description

Writes a figure image to file and returns the file name.

Usage

figure_write(aFigure, file = NULL)

Arguments

Value

Vector of file names.

See Also

figure_read

Imputes missing standard deviations in a dataset.

Description

Imputes (fills gaps) of missing standard deviations (SD) using simple imputation methods following Bracken (1992) and Rubin and Schenker's (1991) "hot deck" approach.

Usage

impute_SD(
  aDataFrame,
  columnSDnames,
  columnXnames,
  method = "Bracken1992",
  range = 3,
  M = 1
)

Arguments

Value

An imputed (complete) dataset.

References

Bracken, M.B. 1992. Statistical methods for analysis of effects of treatment in overviews of randomized trials. Effective care of the newborn infant (eds J.C. Sinclair and M.B. Bracken), pp. 13-20. Oxford University Press, Oxford.

Rubin, D.B. and Schenker, N. 1991. Multiple imputation in health-care databases: an overview and some applications. Statistics in Medicine 10: 585-598.

Provides a summary of missingness in a dataset.

Description

Generates a summary of the percentage of missing data in a dataset. Provides insight on the appropriateness of imputation methods. For example, if 30% of data is missing, then perhaps this is too much to impute.

Usage

impute_missingness(aDataFrame)

Arguments

Value

A data frame that summarizes percent missingness for each column of a dataset.

Examples

data(example_references_metagear)
impute_missingness(example_references_metagear)

Evaluates whether a file is a PDF document.

Description

Checks if provided file is in Portable Document Format (PDF).

Usage

isPDF(aFileName, verbose = TRUE)

Arguments

Value

A logical value indicating whether the file is a PDF document.

Plots and creates a PRISMA flow diagram.

Description

Creates a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram depicting the 'flow' of study inclusions and exclusions during various systematic review phases. It is meant to describe the number of studies identified, included, excluded, reasons for inclusion/exclusions, and final number of studies used in the meta-analysis. NOTE: currently only supports two start phases, and final phase must not have an exclude branch.

Usage

plot_PRISMA(
  aPhaseVector,
  colWidth = 30,
  excludeDistance = 0.8,
  design = "classic",
  hide = FALSE
)

Arguments

Value

a grid object (grob) list

Note

Using canned or custom PRISMA design layouts

There are several color schemes and design layouts (e.g. curved or flat) available. These designs include: cinnamonMint, sunSplash, pomegranate, vintage, grey, and greyMono. Custom schemes can also be developed by modifying each aspect of the design. These are:

S

color of start phases (default: white)

P

color of the main phases (default: white)

E

color of the exclusion phases (default: white)

F

color of the final phase (default: white)

fontSize

the size of the font (default: 12)

fontColor

the font color (default: black)

fontFace

either plain, bold, italic, or bold.italic (default: plain)

flatArrow

arrows curved when FALSE (default); arrows square when TRUE

flatBox

boxes curved when FALSE (default); Boxes square when TRUE

For example, changing the defaults to have red rather than white exclusion phases, and square boxes, would be: design = c(E = "red", flatBox = TRUE).

References

Moher, D., Liberati, A., Tetzlaff, J. and Altman, D.G., PRISMA Group. (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ 339, b2535.

Examples

phases <- c("START_PHASE: # of studies identified through database searching",
            "START_PHASE: # of additional studies identified through other sources",
            "# of studies after duplicates removed",
            "# of studies with title and abstract screened",
            "EXCLUDE_PHASE: # of studies excluded",
            "# of full-text articles assessed for eligibility",
            "EXCLUDE_PHASE: # of full-text excluded, not fitting eligibility criteria",
            "# of studies included in qualitative synthesis",
            "EXCLUDE_PHASE: # studies excluded, incomplete data reported",
            "final # of studies included in quantitative synthesis (meta-analysis)")
plot_PRISMA(phases, design = "cinnamonMint")

Random generation of sample sizes (N) for study outcomes.

Description

Generates random sample sizes (N) by either sampling from a Negative Binomial or Poisson distribution.

Usage

random_N(K, method = "NegativeBinomial", mean = 15, min = 3, NB_size = 15)

Arguments

Value

A vector of random sample sizes (N).

Random generation of odds ratio (OR) effect sizes.

Description

Generates random odds ratios, logged odds ratios, and their variances (Cornfield 1951).

Usage

random_OR(K, p_A, N_A, p_B, N_B, continuity = 0.5, logged = TRUE)

Arguments

Value

A data table with columns of random effect sizes (OR) and their variances.

References

Cornfield, J. 1951. A method for estimating comparative rates from Clinical Data. Applications to cancer of the lung, breast, and cervix. Journal of the National Cancer Institute 11: 1269-1275.

Cox, D.R. 1970. The continuity correction. Biometrika 57: 217-219.

Examples

   random_OR(K = 5, p_A = 0.3, N_A = 100, p_B = 0.1, N_B = 60)

Random generation of log response ratio (RR) effect sizes.

Description

Generates random log response ratios and their variances (Hedges et al. 1999). NOTE: samples from a log-normal distribution to generate non-negative control and treatment means (following Lajeunesse 2015).

Usage

random_RR(K, X_t, var_t, N_t, X_c, var_c, N_c)

Arguments

Value

A data table with columns of random effect sizes (RR) and their variances.

References

Hedges, L.V., J. Gurevitch, and P.S. Curtis. 1999. The meta-analysis of response ratios in experimental ecology. Ecology 80: 1150-1156.

Lajeunesse, M.J. 2015. Bias and correction for the log response ratio used in ecological meta-analysis. Ecology.

Examples

   random_RR(K = 5, X_t = 25, var_t = 1, N_t = 15, X_c = 10, var_c = 1, N_c = 15)

Random generation of Hedges' d effect sizes.

Description

Generates random Hedges' d (1981, 1982) effect sizes and their variances.

Usage

random_d(K, X_t, var_t, N_t, X_c, var_c, N_c, bias_correction = TRUE)

Arguments

Value

A data table with columns of random effect sizes (d) and their variances (var_d).

References

Hedges, L.V. 1981. Distribution theory for Glass's estimator of effect size and related estimators. Journal of Educational Statistics 6: 107-128.

Hedges, L.V. 1982. Estimation of effect size from a series of independent experiments. Psychological Bulletin 92: 490-499.

Examples

   random_d(K = 5, X_t = 25, var_t = 1, N_t = 15, X_c = 10, var_c = 1, N_c = 15)

Random generation of missingness in a data frame.

Description

Generates random NA's in in a column or groups of columns of a data frame. Used in imputation simulations based on complete datasets.

Usage

random_missingness(aDataFrame, columnNames, percentMissing = 10)

Arguments

Value

A data table with columns of missing data (specified as NA's).

Random generation of paired sample sizes (N) for study outcomes.

Description

Generates random paired sample sizes (N). For example, sample sizes for a treatment group and samples sizes for a control group. These paired N are often correlated within studies.

Usage

random_pairedN(K, mean = 15, min = 3, correlation = 0.95)

Arguments

Value

A data table of paired random sample sizes (N).

Random generation of correlation coefficients.

Description

Generates random correlation coefficients (r or Pearson product-moment correlation coefficients) and their variances (Pearson 1895). Also provides Fisher z-transformed correlation coefficients (Fisher 1915).

Usage

random_r(K = 100, correlation = 0.5, N = 10, Fisher_Z = FALSE)

Arguments

Value

A data table with columns of random effect sizes (r), their variances and sample sizes.

References

Pearson, K. 1895. Notes on regression and inheritance in the case of two parents. Proceedings of the Royal Society of London 58: 240-242.

Fisher, R.A. 1915. Frequency distribution of the values of the correlation coefficient in samples of an indefinitely large population. Biometrika 10: 507-521.

Examples

   random_r(K = 5, correlation = 0.5, N = 50)

Replicate meta-analysis results and summaries from MetaWin 2.0.

Description

Replicate meta-analysis results and summaries from Rosenberg's et al. (2000) software 'MetaWin' 2.0. Currently only replicates moderator analyses and not meta-regressions.

Usage

replicate_MetaWin2.0(
  model,
  weights,
  effects_model = "random",
  data,
  bootstraps = 999
)

Arguments

References

Rosenberg, M.S., Adams, D.C., and Gurevitch, J. 2000. MetaWin: Statistical Software for Meta-Analysis. Sinauer Associates Sunderland, Massachusetts.

Replicate phylogeneic meta-analysis results and summaries from phyloMeta 1.3.

Description

Replicate phylogenetic meta-analysis results and summaries from Lajeunesse (2011) software 'phyloMeta' 1.3. Currently does not fully replicate all functionality.

Usage

replicate_phyloMeta1.3(model, weights, data, phylogenyFile)

Arguments

References

Lajeunesse, M.J. (2011) phyloMeta: a program for phylogenetic comparative analyses with meta-analysis. Bioinformatics 27, 2603-2604.

Attempts to scrape/extract bibliographic data from Web of Science.

Description

A not so elegant way to extract bibliographic data of a research article by scraping the contents of Web of Science (WOS). Requires the DOI (digital object identifier) of an article, as well as web access with an institutional subscription to WOS. Note: This function is not suited to extract data for book chapters available on WOS. Current extractions include: a vector of authors (author), publication year (year), article title (title), journal title (journal), journal volume (volume), page numbers (pages), abstract (abstract), number of references (N_references), number of citations (N_citations), journal impact factor (journal_IF), and the year the journal impact factor was released (journal_IF_year). Finally the date of the scrape is also provided (date_scraped). Bulleted abstracts or those with subheadings or subparagraphs will not be extracted properly.

Usage

scrape_bibliography(DOI, quiet = FALSE)

Arguments

Value

A list of bibliographic extractions and a timestamp of the scrape.

Examples

## Not run: 

# use DOI to scrape number of WOS citations of a research article
data(example_references_metagear)
someRefs <- effort_initialize(example_references_metagear)
theWOSRef <- scrape_bibliography(someRefs$DOI[1])
print(paste("citations = ", theWOSRef$N_citations))


## End(Not run)