Systematic Review and meta-analysis as described in:
- Milou S.C. Sep, Marijn Vellinga, R. Angela Sarabdjitsingh, Marian Joëls. (2021). Measuring context-dependent memory in rodents: a systematic review and meta-analysis of important variables in the object-in-context task. bioRxiv 2021.03.12.435070; doi: https://doi.org/10.1101/2021.03.12.435070 [preprint]
- Milou S.C. Sep, Marijn Vellinga, R. Angela Sarabdjitsingh, Marian Joëls. (in press). The rodent object-in-context task: a systematic review and meta-analysis of important variables. PLOS ONE.
_ README.md
: an overview of the project
|___ data
: data files used in the project
|___ processed_data
: intermediate files from the analysis
|___ results
: results of the analyses (data, tables, figures)
|___ R
: contains all R-code in the project
- script:
flowchart.R
- input (script contains code that retrieves data from OSF):
- search step:
data/hits.search.thesis.MV.txt
data/hits.new.search.meta.oic.v25.5.20.txt
- screening step:
data/Screening S1 thesis search PMIDs.csv
data/Screening S2 new.in.new.search.PMIDs.csv
- included data:
data/280121_Data_Extraction_RoB.xlsx
- search step:
- actions:
- count numbers screening & inclusions for flow chart
- script:
prepare_data.Rmd
(for Systematic review & meta-analysis) - input:
data/280121_Data_Extraction_RoB.xlsx
(script contains code that retrieves data from OSF) - actions:
- pre-processing data (and save cleaned data for Systematic review table)
- missing values.
- Variables with more than 1/3 missing are excluded from Random forest-based meta-analysis
- Other variables: Missing values are replaced by median value (for numeric) of most prevalent category (for factors)
- create sum scores:
Arousal.Prior
,Context.Difference.Score
,Arousal.Task.Habituation
,Arousal.Total
- output:
processed_data/SR_data.RDS
(for Systematic review table)processed_data/cleaned_data.RDS
(for meta-analysis)
- script:
systematic_review_table.Rmd
- action: create systematic review table
- output:
results/Overview_SR.docx
- script:
QA_RoB_plots.Rmd
- action: create waffel plot with SYRCLE’s risk of bias assessment per study (PMID)
- output:
results/QA_ROB.tiff
- script:
random_effects_meta_analysis.Rmd
- input:
processed_data/cleaned_data.RDS
data/280121_Data_Extraction_RoB.xlsx
- actions:
- random-effects meta-analysis
- calculate required sample size for future studies
- robustness of effects measures
- sensitivity analyses
- output:
processed_data/data_with_effect_size.RDS
results/forest_year.tiff
results/funnel.colours.tiff
results/study.quality.jpeg
- script:
MetaForest.Rmd
- Input:
processed_data/data_with_effect_size.RDS
- Actions:
- tune & run random forest-based meta-analysis (MetaForest)
- identify important moderators based on variable importance in RF
- Output:
- processed data:
processed_data/datForest_for_WS_Plot.RDS
processed_data/fitted.MetaForest.RDS
processed_data/important_variables.RDS
- results:
results/metaforest_convergencePlot.jpeg
results/metaforest_varImportance.jpeg
results/important_variables_metaforest.csv
- processed data:
- script:
MetaForest_PD_WS_plots.Rmd
- input:
processed_data/fitted.MetaForest.RDS
processed_data/datForest_for_WS_Plot.RDS
processed_data/important_variables.RDS
- actions:
- create partial dependance (PD) and weighted scatter plots to follow-up most important variables MetaForest
- output:
results/metaforest_adapted_PD_plots.jpeg
results/metaforest_adapted_WS plots.jpeg