Official Git repository of tracenma R package
Website
The website of tracenma currently includes a structured reference list to facilitate access to the documentation of the functions available in the package.
Description
The tracenma R package contains a database with extracted aggregate study-level characteristics (that may act as effect modifiers) from 217 systematic reviews with network meta-analysis published from 2004 to 2015. tracenma also contains functions to access the catalogue with the available systematic reviews and the datasets with the extracted characteristics, comprising the database.
The package is built upon the R package nmadb (version 1.2.0) to define the eligible connected networks to extract the available characteristics from the corresponding systematic reviews. Currently, tracenma includes a subset of the systematic reviews available in nmadb.
👉 The tracenma R package should only be used to develop and appraise the methodology to assess the transitivity assumption quantitatively.
👎 The tracenma R package should not be used, for instance, to map the characteristics reported in specific healthcare fields of the database.
Getting started
Run the following code to install and load the package from CRAN:
or run the following code to install and load the development version of the package:
Example
Access the catalogue of the database
To access the complete catalogue with all 217 systematic reviews with their characteristics, type index.
head(index[, 1:5])
#> nmadb.ID PMID First.Author Year Journal.Name
#> 1 501330 15147585 Mason 2004 BMC Fam Pract
#> 2 479574 15820294 Biondi-Zoccai 2005 Int J Cardiol
#> 5 501268 15968013 Gafter-Gvili 2005 Ann Intern Med
#> 4 481193 16354303 Moore 2005 BMC Urol
#> 3 479688 16442888 Zhou 2005 Am Heart J
#> 6 501211 16239897 Berner 2006 Int J Impot ResAccess the index of datasets published during a specific year
Use the function get.dataset.index to get one or more systematic reviews and their characteristics. For instance, let us access the available systematic reviews publish in 2007 using the default arguments:
get.dataset.index(pmid = NULL, year = 2007)
#> nmadb.ID PMID First.Author Year Journal.Name Outcome.Type
#> 11 480655 16951908 Vestergaard 2007 Osteoporos Int Objective
#> 14 501355 17478472 Nixon 2007 Rheumatology Subjective
#> 13 501336 17651658 McLeod 2007 Health Technol Assess Subjective
#> 15 501395 17903393 Soares-Weiser 2007 Health Technol Assess Semi-objective
#> 12 501309 17932160 Lam 2007 BMJ Objective
#> Intervention.Comparison.Type Includes.ToC.where Source.ToC
#> 11 pharmacological vs placebo Main text Table 1
#> 14 pharmacological vs placebo Main text Table 1
#> 13 pharmacological vs placebo Main text Table 2 to 4
#> 15 pharmacological vs placebo Both Tables 1 & 2, Appendix Tables 5
#> 12 non-pharmacological vs any Main text Table 1You can access the article titles by adding the argument show.title = TRUE. Then the column Title will appear after Journal.Name. Add the argument show.comment = TRUE to activate the extraction-related comments accompanying the articles. The column Comment will appear after Source.ToC. The default for both arguments is FALSE (do not report).
Access the index of specific datasets
Now, let us access the systematic reviews with PMID numbers 16951908 and 17932160 (again, using the default arguments):
get.dataset.index(pmid = c(16951908, 17932160))
#> nmadb.ID PMID First.Author Year Journal.Name Outcome.Type
#> 11 480655 16951908 Vestergaard 2007 Osteoporos Int Objective
#> 12 501309 17932160 Lam 2007 BMJ Objective
#> Intervention.Comparison.Type Includes.ToC.where Source.ToC
#> 11 pharmacological vs placebo Main text Table 1
#> 12 non-pharmacological vs any Main text Table 1Access a specific dataset
Use the function get.dataset to get the dataset with the extracted characteristics of a specific systematic review. For instance, let us access the dataset of the systematic review with PMID number 16951908 using the default arguments:
get.dataset(pmid = 16951908)
#> # A tibble: 25 × 12
#> `trial (reference)` treat1 treat2 arm1 arm2 sex sample.size h.rPTH calcium vitamin.D duration quality
#> <chr> <dbl> <dbl> <chr> <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Finkelstein 1998 (26) 1 2 control PTH 40 female 43 1-34 NA NA 12 2
#> 2 Lane 1998 (42) 1 3 control PTH 25 female 51 1-34 1500 800 12 3
#> 3 Kurland 2000 (33) 1 4 control PTH 32 male 23 1-34 1500 400 18 3
#> 4 Cosman 2001 (24) 1 3 control PTH 25 female 52 1-34 1500 800 36 3
#> 5 Neer 2001 (5) 1 2 control PTH 40 female 882 1-34 1000 400 21 3
#> 6 Neer 2001 (5) 1 5 control PTH 20 female 892 1-34 1000 400 21 3
#> 7 Neer 2001 (5) 2 5 PTH 40 PTH 20 female 878 1-34 1000 400 21 3
#> 8 Body 2002 (43) 1 2 control PTH 40 female 146 1-34 1000 400 12 4
#> 9 Finkelstein 2003 (28) 1 2 control PTH 40 male 48 1-34 1000 400 30 3
#> 10 Finkelstein 2003 (28) 1 6 control PTH+ALN male 53 1-34 1000 400 30 3
#> # ℹ 15 more rows
#> # ℹ Use `print(n = ...)` to see more rowsAdding the argument show.index = TRUE returns an additional data-frame (following the dataset, which is skipped in the example below) that presents the abbreviation (left column) and full name (right column) of each characteristic:
get.dataset(pmid = 16951908, show.index = TRUE)
#> $Characteristics_index
#> # A tibble: 7 × 2
#> Abbreviation Full.name
#> <chr> <chr>
#> 1 sex sex
#> 2 sample.size sample size
#> 3 h.rPTH human (recombinant) parathyroid hormone
#> 4 calcium calcium in mg/day
#> 5 vitamin.D vitamin D in IU/day
#> 6 duration trial duration in months
#> 7 quality study quality Lastly, adding the argument show.type = TRUE enhances the additional data-frame, $Characteristics_index, with another two columns appearing on the right; the Characteristic.type (Clinical, Demographic, Methodological) and Characteristic.subtype (Participant, Intervention, Outcome, Age, Sex, Ethnicity, Study design, Study setting, Risk of bias, Withdrawals) for each characteristic:
get.dataset(pmid = 16951908, show.index = TRUE, show.type = TRUE)
$Characteristics_index
#> # A tibble: 7 × 4
#> Abbreviation Full.name Characteristic.type Characteristic.subtype
#> <chr> <chr> <chr> <chr>
#> 1 sex sex Demographic Sex
#> 2 sample.size sample size Methodological Study design
#> 3 h.rPTH human (recombinant) parathyroid hormone Clinical Intervention
#> 4 calcium calcium in mg/day Clinical Intervention
#> 5 vitamin.D vitamin D in IU/day Clinical Intervention
#> 6 duration trial duration in months Methodological Study design
#> 7 quality study quality Methodological Risk of bias The default for both arguments is FALSE (do not report the data-frame $Characteristics_index).
That’s it for the moment! 😎 ☕
Acknowledgements
I would like to thank Juan Jose Yepes-Nuñez and Andrés Mauricio García-Sierra for their valuable help during data curation of the extracted datasets for possible extraction errors.
Funding source
The development of thetracenma R package is funded by the German Research Foundation (Deutsche Forschungsgemeinschaft) (grant no. SP 1664/2-1)
