The original paper used logistic regression using backwards selection on a number of binary variables to attempt to predict the risk of childhood mortality. It created a simple binary risk score to aid in calculating a child's probability of death.

My work attempts to extend this analysis by using machine learning analyses to validate this approach and compare it to other analytical methods. In particular, I aim to produce some decision trees to aid in decision-making.

01_clean_data.do:

• Formats all variables the same way
• Decide what to do with “9” values
• Exclude variables that don’t matter
• Create variable lists: signs, symptoms, treatment, diagnoses, test results, outcomes
• Turn all variables into binary indicators
  • May need Herbie’s guidance on indicators not included in the original analysis

02_prep_data.R

• Apply different methods of imputation or observation-dropping
• Output table of missing variables for all variables of interest
• Descriptive table of primary patient characteristics
• Any other descriptive stats

analysis_functions.R

• Specify one function for each method — assume same data structure, and take in arguments for formula etc.
• Logistic regression with backwards selection
  • Define variable importance cutoffs for selection (or default)
• Decision trees
  • Define ideal tree breakdown — pruning characteristics etc.
• Random forests
  • Source up-sampling/down-sampling methods
• Define sampling parameters, number of cv runs, etc.
• Output predictions, graphical representations, ROC analyses.
• Save graphs, predictions, and ROC analyses to flat files (how to toggle by source?)

03_apply_analysis.R

• Source all analysis functions
• Separate data into train and test data
• Apply analysis functions and get/plot results