Output from results used for the following studies:

1. Trends and relevance in the bladder and bowel dysfunction literature: PlumX metrics contrasted with fragility indicators
2. A second-look at reported statistics: Challenges in replicating reported p-values in pediatric urology literature

This code will calculate the 1) p-value fragility (for significant studies), 2) reverse p-value fragility (for non-significant studies), and 3) errors in reported p-value calculations.

To replicate the results:

1. Install conda environment: conda install --file conda.env
2. Run the python scripts in order: source pipeline.sh

The pipeline shell is as follows:

1. 1_dataprep.py: This script loads in the hard-coded excel files that have the list of studies with the different group counts and study covariates (study year, impact factor, study design). It will output ~/output/{df_FI,df_inf}.csv, the former having the group 1/2 counts, and the latter havng the study annotations.
2. 2_fragility.py: Calculates the different FI index measures for all studies and saves results in df_res.csv.
3. 3_results_FIFQ.py: Calculates statistical associations on different journal factors and FI/FQ measures.
4. 4_results_insig.py: Explores the papers that had baseline insignificant results and whether these could be accounted for.