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Introduction


The metagear package for R contains tools for facilitating systematic reviews, data extraction, and meta-analyses. It aims to facilitate research synthesis as a whole, by providing a single source for several of the common tasks involved in screening studies, extracting outcomes from studies, and performing statistical analyses on these outcomes using meta-analysis. Below are a few illustrative examples of applications of these functionalities.

Updates to these examples will be posted on our research webpage at USF, and for previous versions of this vignette see v. 0.3, v. 0.2 and v. 0.1.

For the source code of metagear see: http://cran.r-project.org/web/packages/metagear/index.html.


Acknowledgements


Funding for metagear is supported by National Science Foundation (NSF) grants DBI-1262545 and DEB-1451031.

I also thank J. Richardson, J. Zydek, N. Ogburn, B. MacNeill, J. Zloty, and my colleagues in the OpenMEE software team, J. Gurevitch and B. Wallace, for persuading me to develop tools in R.


How 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. article link


Installation and Dependencies

Metagear has several dependencies that need to be installed and loaded prior to use in R. The first is the EBImage R package (Pau et al. 2010) available only from Bioconductor repository. The second is associated with metagear's abstract_screener() function that generates a GUI to help quickly sift bibliographic data from multiple studies.

To properly install metagear, use the following script in R:

# first load Bioconductor resources needed to install the EBImage package 
# and accept/download all of its dependencies
source("https://bioconductor.org/biocLite.R")
biocLite("EBImage")

# then load metagear
library(metagear)

The first loading of metagear using library(metagear) will trigger the download of the gWidgets package and associated toolkits needed to build GUI interfaces. A small window will also prompt you to download GTK+ asking "Need GTK+ ?". From the listed options answer: "Install GTK+" and click "OK". Once installed, these will not be downloaded again.

Sometimes there is an issue with the installation of GTK+, see here or here for advice based on the Rattle R Package. Note that both Rattle and metagear use the same GUI dependencies. Also sometimes the installation will freeze; however, re-starting the R session can fix this issue.


Report a bug? Have comments or suggestions?

Please email me any bugs, comments, or suggestions and I'll try to include them in future releases: lajeunesse@usf.edu. Also try to include metagear in the subject heading of your email. Finally, I'm open to almost anything, but expect a lag before I respond and/or new additions are added.


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Delegating reference screening effort to a team


One of the first tasks of a systematic review is to screen the titles and abstracts of study references to assess their relevance for the synthesis project. For example, after a bibliographic search using Web of Science, there may be thousands of references generated; references from experimental studies, modeling studies, review papers, commentaries, etc. These need to be reviewed individually as a first pass to exclude those that do not fit the synthesis project; such as excluding simulation studies that do not report experimental outcomes useful for estimating an effect size.

However, individually screening thousands of references is time consuming, and large synthesis projects may benefit from delegating this screening effort to a research team. Having multiple people screen references also provides an opportunity to assess the repeatability of these screening decisions.

In this example, we have the following goals:

  1. Initialize a dataframe containing bibliographic data (tile, abstract, journal) from multiple study references.
  2. Distribute these references randomly to two team members.
  3. Merge and summarize the screening efforts of this team.

First, let's start by loading and exploring the contents of a pre-packaged dataset from metagear that contains the bibliographic information of 11 journal articles (example_references_metagear). These data are a subset of references generated from a search in Web of Science for "Genome size", and contain the abstracts, titles, volume, page numbers, and authors of these references.

# load package
library(metagear)
# load a bibliographic dataset with the authors, titles, and abstracts of multiple study references 
data(example_references_metagear)
# display the bibliographic variables in this dataset
names(example_references_metagear)
## [1] "AUTHORS"  "YEAR"     "TITLE"    "JOURNAL"  "VOLUME"   "LPAGES"   "UPAGES"   "DOI"      "ABSTRACT"
# display the various Journals that these references were published in
example_references_metagear["JOURNAL"]
##                                                JOURNAL
## 1  BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS
## 2                        EVOLUTIONARY ECOLOGY RESEARCH
## 3                                  AMERICAN NATURALIST
## 4                                                 GENE
## 5                                          VIRUS GENES
## 6                        JOURNAL OF SHELLFISH RESEARCH
## 7                      JOURNAL OF GENERAL MICROBIOLOGY
## 8                                 APPLIED GEOCHEMISTRY
## 9      JOURNAL OF DRUG DELIVERY SCIENCE AND TECHNOLOGY
## 10                                  BIOLOGIA PLANTARUM
## 11                                            GENOMICS

Our next step is to initialize/prime this dataset for screening tasks. Our goal is to distribute screening efforts to two screeners/reviewers: "Christina" and "Luc". Here each reviewer will screen a separate subset of these references (a forthcoming example will review how to set up a dual screening design where each member screens the same references). The dataset first needs to be initialized as follows:

# prime the study-reference dataset
theRefs <- effort_initialize(example_references_metagear)
# display the new columns added by effort_initialize 
names(theRefs)
##  [1] "STUDY_ID"  "REVIEWERS" "INCLUDE"   "AUTHORS"   "YEAR"      "TITLE"     "JOURNAL"   "VOLUME"    "LPAGES"    "UPAGES"    "DOI"       "ABSTRACT"

Note that the effort_initialize() function added three new columns: "STUDY_ID" which is a unique number for each reference (e.g., from 1 to 11), "REVIEWERS" an empty column with NAs that will be later populated with our reviewers (e.g., Christina and Luc), and finally the "INCLUDE" column, which will later contain the screening efforts by the two reviewers.

Screening efforts are essentially how individual study references get coded for inclusion in the synthesis project; currently the "INCLUDE" column has each reference coded as "not vetted", indicating that each reference has yet to be screened.

Our next task is to delegate screening efforts to our two reviewers Christina and Luc. Our goal is to randomly distribute these references to each reviewer.

# randomly distribute screening effort to a team
theTeam <- c("Christina", "Luc")
theRefs_unscreened <- effort_distribute(theRefs, reviewers = theTeam)
# display screening tasks
theRefs_unscreened[c("STUDY_ID", "REVIEWERS")]
##    STUDY_ID REVIEWERS
## 1         1       Luc
## 2         2       Luc
## 3         3 Christina
## 4         4 Christina
## 5         5 Christina
## 6         6 Christina
## 7         7 Christina
## 8         8       Luc
## 9         9       Luc
## 10       10       Luc
## 11       11 Christina

The screening efforts can also be delegated unevenly, such as below where Luc will take on 80% of the screening effort:

# randomly distribute screening effort to a team, but with Luc handeling 80% of the work
theRefs_unscreened <- effort_distribute(theRefs, reviewers = theTeam, effort = c(20, 80))
theRefs_unscreened[c("STUDY_ID", "REVIEWERS")]
##    STUDY_ID REVIEWERS
## 1         1 Christina
## 2         2 Christina
## 3         3       Luc
## 4         4       Luc
## 5         5       Luc
## 6         6       Luc
## 7         7       Luc
## 8         8       Luc
## 9         9       Luc
## 10       10       Luc
## 11       11       Luc

The effort can also be redistributed with the effort_redistribute() function. In the above example we assigned Luc 80% of the work. Now let's redistribute half of Luc's work to a new team member "Patsy".

theRefs_Patsy <- effort_redistribute(theRefs_unscreened, 
                                     reviewer = "Luc",
                                     remove_effort = 50, # move 50% of Luc's work to Patsy
                                     reviewers = c("Luc", "Patsy")) # team members loosing and picking up work
theRefs_Patsy[c("STUDY_ID", "REVIEWERS")]
##    STUDY_ID REVIEWERS
## 1         1 Christina
## 2         2 Christina
## 3         3       Luc
## 4         4     Patsy
## 5         5     Patsy
## 6         6       Luc
## 7         7       Luc
## 8         8     Patsy
## 9         9     Patsy
## 10       10       Luc
## 11       11       Luc

The references have now been randomly assigned to either Christina or Luc. The whole initialization of the reference dataset with effort_initialize() can be abbreviated with effort_distribute(example_references_metagear, reviewers = c("Christina", "Luc"), initialize = TRUE).

Now that screening tasks have been distributed, the next stage is for reviewers to start the manual screening of each assigned reference. This is perhaps best done by providing a separate file of these references to Christina and Luc. They can then work on screening these references separately and remotely. Once the screening is complete, we can then merge these files into a complete dataset (we'll get to this later).

The effort_distribute() function can also save to file each reference subset; these can be given to Christina and Luc to start their work. This is done by setting the 'save_split' parameter to TRUE.

# randomly distribute screening effort to a team, but with Luc handling 80% of the work, 
# but also saving these screening tasks to separate files for each team member
theRefs_unscreened <- effort_distribute(theRefs, reviewers = theTeam, effort = c(20, 80), save_split = TRUE)
## 2 files saved in: C:/Users/lajeunesse/Desktop/R_projects/metagear_0.3.2/metagear/vignettes
theRefs_unscreened[c("STUDY_ID", "REVIEWERS")]
##    STUDY_ID REVIEWERS
## 1         1       Luc
## 2         2 Christina
## 3         3       Luc
## 4         4       Luc
## 5         5 Christina
## 6         6       Luc
## 7         7       Luc
## 8         8       Luc
## 9         9       Luc
## 10       10       Luc
## 11       11       Luc
list.files(pattern = "effort")
## [1] "effort_Christina.csv" "effort_Luc.csv"

These two effort_*.csv files contain the assigned references for Christina and Luc. These can be passed on to each team member so that they can begin screening/coding each reference for inclusion in the synthesis project.

References should be coded as "YES" or "NO" for inclusion, but can also be coded as "MAYBE" if bibliographic information is missing or there is inadequate information to make a proper assessment of the study.

The abstract_screener() function can be used to facilitate this screening process (an example is forthcoming), but for the sake of introducing how screening efforts can be merged and summarized, I manually coded all the references in both of Christina's and Luc's effort_*.csv files. Essentially, I randomly coded each references as either "YES", "NO", or "MAYBE". These files now contain the completed screening efforts.

We can merge these two files with the completed screening efforts using the effort_merge() function, as well as summarize the outcome of screening tasks using the effort_summary() function.

# merge the effort_Luc.csv and effort_Christina.csv 
# WARNING: will merge all files named "effort_*" in directory
theRefs_screened <- effort_merge()
theRefs_screened[c("STUDY_ID", "REVIEWERS", "INCLUDE")]
##    STUDY_ID REVIEWERS INCLUDE
## 1         2 Christina     YES
## 2         5 Christina     YES
## 3         1       Luc      NO
## 4         3       Luc      NO
## 5         4       Luc   MAYBE
## 6         6       Luc     YES
## 7         7       Luc      NO
## 8         8       Luc      NO
## 9         9       Luc     YES
## 10       10       Luc     YES
## 11       11       Luc   MAYBE
theSummary <- effort_summary(theRefs_screened)
## === SCREENING EFFORT SUMMARY ===
## 
##    4 candidate studies identified
##    2 studies excluded
##    5 challenging studies needing additional screening
##   ----
##    11 TOTAL SCREENED
## 
## === SCREENING DESIGN SUMMARY ===
## 
##           YES MAYBE NO TOTAL         %
## Christina   2     0  0     2  18.18182
## Luc         3     2  4     9  81.81818
## TOTAL       5     2  4    11 100.00000

The summary of screening tasks describes the outcomes of which references had studies appropriate for the synthesis project, while also outlining which need to be re-assessed. The team should discuss these challenging references and decide if they are appropriate for inclusion or track down any additional/missing information needed to make proper assessment of their inclusion.