readme.md

PCV Impact Analysis Using Data from Manhica DSS

Basic approach: interrupted time series analysis with Bayesian model averaging

Instructions:

1. Put all data files in [some directory]/data/input
2. Clone this repository to your local computer
3. Set current working directory (in R) to the location of this readme
4. source('impact_analysis.r')
5. impactAnalysis(root='[some directory]', rePrepData=TRUE)
   • If no root is specified, it will assume the data are in ./data/input
   • The default is to not re-prep the data, after running it once with rePrepData=TRUE, you shouldn't need to do that again (unless you want to re-prep the data)

Code structure:

1. impact_analysis.r

   Main script that executes full analysis, has a battery of arguments to control behavior (see script) Outputs:

   • .rdata files in /data/output
   • .pdf graph files in /visualizations

2. prepData.r

   Code that loads/preps data. Inputs:

   • dir - directory where data live

   Outputs:

   • data - all datasets in 'prepped' format (below)

3. its.r

   Function that carries out interrupted time series analysis.
   Inputs:

   • data - data table object in 'prepped' format (see below)
   • outcome - character. name of the outcome variable
   • cutpoint - date object containing the time point or points (up to 2) of intervention
   • slope - logical. TRUE indicates that an interaction term (or terms) should be used to estimate a different slope before/after intervetion

   Outputs (in a list):

   • data - the input data object with six new columns: [outcome]_pred, [outcome]_pred_upper, [outcome]_pred_lower, [outcome]_cf, [outcome]_cf_upper, [outcome]_cf_lower,
   • outcome - character. name of the outcome variable
   • cutpoint - date object containing the time point or points (up to 2) of intervention
   • effect size - a data frame containing the intercept shift associated with intervention, including uncertainty
   • gof - goodness of fit based on BIC

4. bma.r

   Function that carries out Bayesian model averaging over multiple interrupted time series analyses.
   Inputs:

   • itsResults - list of lists. standard output from multiple runs of its.r

   Outputs (in a list):

   • data - the input data object with six new columns: [outcome]_pred, [outcome]_pred_upper, [outcome]_pred_lower, [outcome]_cf, [outcome]_cf_upper, [outcome]_cf_lower,
   • outcome - character. name of the outcome variable
   • cutpoint - date object containing the time point or points (up to 2) of intervention
   • effect size - a data frame containing the intercept shift associated with intervention, including uncertainty

5. cpbma.r

   Function that carries out change-point Bayesian model averaging following Kurum 2016. Inputs:

   • data - data table object in 'prepped' format (see below)
   • outcome - character. name of the outcome variable
   • cutpoint - date vector or matrix (up to 2 columns) containing the time points of intervention to include in BMA
   • slope - logical. TRUE indicates that an interaction term (or terms) should be used to estimate a different slope before/after intervetion

   Outputs (in a list):

   • data - the input data object with six new columns: [outcome]_pred, [outcome]_pred_upper, [outcome]_pred_lower, [outcome]_cf, [outcome]_cf_upper, [outcome]_cf_lower,
   • effect size - a data frame containing the intercept shift associated with intervention, including uncertainty

6. graph.r

   Function that produces a time series graph from the results of its.r, bma.r or cpbma.r. Inputs:

   • itsOutput - list. standard output from its.r

   Outputs:

   • p - a ggplot graph

7. sensitivity_analysis.r

   Script that assembles numerous model outputs and re-graphs them together Output:

   • file at /visualizations/sensitivity_analysis.pdf

Data formats:

1. 'prepped' format FILL IN

2. 'output' format FILL IN