dianashams

ensemble-methods-for-survival-analysis

Mixing classical survival analysis model, Cox Proportionate Hazards model, with tree-based machine learning algorithms (survival decision trees, survival random forest) to achieve better predictive performance while producing a transparent algorithm

Ensemble_methods_Shiny_app

Private _ code in progress

missing-data-in-healthcare

consolidation page for missing data research

NLP_sample_size_simulation_study

predicting_type2_diabetes_in_severe_mental_illness

simulating_survival_data_python

survcompare

Internally validates and compares Cox Proportionate Hazards model and Survival Random Forest

dianashams/ensemble-methods-for-survival-analysis

Mixing classical survival analysis model, Cox Proportionate Hazards model, with tree-based machine learning algorithms (survival decision trees, survival random forest) to achieve better predictive performance while producing a transparent algorithm

dianashams/survcompare

Internally validates and compares Cox Proportionate Hazards model and Survival Random Forest

dianashams/Ensemble_methods_Shiny_app

Private _ code in progress

dianashams/missing-data-in-healthcare

consolidation page for missing data research

dianashams/NLP_sample_size_simulation_study

dianashams/predicting_type2_diabetes_in_severe_mental_illness

dianashams/simulating_survival_data_python