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SGLT2-GLP1

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

Files:

  • 0.0: Functions

    • .1: Functions for plotting and some calculations.
    • .2: Functions for bartMachine tree analysis. Slightly modified bartMan R package (not kept up to date) (package requirements were modified).

  • 1.0: Detailed explanation of steps taken in the selection of patients for our cohorts.

  • 2.0: Collection of plots demonstrating specific details/quirks of the dataset.

  • 3.0: R packages to model causal treatment effect.

    • .1: Fitting of a causal model using grf R package. This includes an evaluation of model fit.
    • .2: Fitting of a causal model using bcf R package. This includes an evaluation of model fit.

  • 4.0: bartMachine models for treatment heterogeneity.

    • .1: Fitting a BART model with variable selection for propensity score and outcome model. This includes an evaluation of model fit.
    • .2: Fitting a BART model with routine variables in propensity score model and biomarkers in outcome model. This includes an evaluation of model fit.
    • .3: Fitting a BART model with variable selection for propensity score and variable selection using BART + grf for the outcome model. This includes an evaluation of model fit.
    • .4: Fitting a BART model with variable selection using BART + grf for the outcome model. This includes an evaluation of model fit.

  • 5.0: bartMachine models using no methodical procedure.

    • .1: Fitting a collection of naive Bart models for HbA1c outcome using routine clinical variables / all variables / propensity scores, alternating between them.

  • 6.0: Comparing models.

    • .1: Collection of plots comparing naive BART models in 5.0.
    • .2: Collection of plots comparing bcf and bartMachine with the same variables. Head-to-head comparisons of treatment effect for 3.2. vs 5.1. model 1 (Complete/Routine)
    • .3: Collection of plots comparing 4.1-4.4 models.

  • 7.0: Simulations.

    • .1: