SWOP Regression (By Simulation)

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Statistics without Probability
SWOP Regression (By Simulation)
Code for SWOP in R including Shiny Apps
Advantages of Statistics Without Probability
Point Estimation
Interval Estimation
Hypothesis Testing
Corrected Treatment Effect – To Adjust for multiple measured confounders
Variance of the Corrected Treatment Effect
Influence of Extremities Factor
Interaction terms with SWOP
Modifying disease by changing exposure or confounding.
Corrected Treatment Effect – Nonlinear association equations.
Point Estimation and adjusting using Logistic Regression
Prognostic Risk Modelling to Enable Shared Optimal Decision Making in Medicine
Using Longitudinal methods (via least squares) over Survival analysis in Point Estimation
Prediction modelling and testing without Probability
Sample Size Verification Method using Statistics without Probability
Adequacy of fit using residuals
Results: Verification of Methods
Results 2: Verification of SWOP+CTE to replace Poisson Regression using Doll-Hill Smoking Study
Least Squares Methods of Estimation (Resources)
Further Research for SWOP+CTE
For further Research: Mediation, Confounding and Effect Decomposition using the Corrected Treatment Effect.
A Summary of Public Health Frameworks: By Dr Mithilesh Dronavalli
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