Collection of functions and scripts to investigate how clinical features can be used for prediction.
Files:
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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).
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1.0: Detailed explanation of steps taken in the selection of patients for our cohorts.
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2.0: Collection of plots demonstrating specific details/quirks of the dataset.
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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.
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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.
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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.
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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.
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7.0: Simulations.
- .1: