SGLT2-GLP1

Collection of functions and scripts to investigate how clinical features can be used for prediction.

Final structure for analysis: (Model 05)

1. Fit a BART propensity score model (function bartMachine::bartMachine) with all variable available.
2. Perform BART variable selection (function bartMachine::var_selection_by_permute) to choose variables for the PS model.
3. Re-fit a BART propensity score model (function bartMachine::bartMachine) with the selected variables.
4. Fit a SparseBCF model (function SparseBCF::SparseBCF) on complete data and perform variable selection:
   • Only include variable with ~ 20% missingness (discard higher amounts)
   • Select variables with an inclusion proportion above 1/n (n = number of variables used)
5. Fit a BCF model (function bcf::bcf) on complete data with the selected variables.
   • Fit two versions of the model:
     • With propensity scores included in the "control" or mu(x) (use include_pi = "control")
     • Without propensity scores included in the model (use include_pi = "none")
   • Compare individual predictions from both models in order to decide on the use of propensity scores.
6. Check model fit for the outcomes:
   • Plot standardised results to check for any structure in the residuals.
7. Check model fit for the treatment effects: (model fitted in observational data)
   • Plot predicted CATE vs ATE for several ntiles of predicted treatment effect:
     • Propensity score matching 1:1 (check whether matched individuals are well balanced)
     • Propensity score matching 1:1 whilst adjusting for all variables used in the BCF model.
     • Adjust for all variables used in the BCF model.

Files:

(Developed in CPRD: Aurum download)

• 01: Functions used specifically for this portion.
• 02: Detailed explanation of the selection of cohorts.
• 03: Descriptive analysis of datasets.
• 04: Propensity score model.
• 05: Model heterogeneity.
• 06: Risks/Benefits: hba1c change, eight change, eGFR change, discontinuation, CVD/HF/CKD outcomes, microvascular complications.
• 07: Differential treatment effects.
• 08: Paper plots.
  • .1: Main plots of paper
  • .2: Supplementary plots of paper
  • .3: Plots for DUK.
• 09: Comparison of SGLT2vsGLP1 BCF model to SGLT2vsDPP4 linear model (John Dennis).
• 10: Validation of treatment effects splitting by ethnicity.
• 11: Validation of the excluded individuals that were prescribed semaglutide.
• 12: Validation of treatment effects in those insulin treated.
• 13: Validation of treatment effects in those with/without baseline CVD.