Shrinkage Based Forest Plots
Average Hazard Ratio Estimation
Average Hazard Estimation based on Kaplan-Meier Estimates
bonsaiforest: Shrinkage Based Forest Plots
Compare Treatment Estimate Methods
Helper for Cutting into Normal Quantiles
Elastic Net Penalization Model Estimation
Generation of Stacked Data by Subgroups
Bayesian Shrinkage Model Estimation
Helper Function to get Kaplan-Meier Estimate
Estimation of Log-Odds Ratio
Naive Model Estimation
Naive Overall Population Model Estimation
Compare Forest Plots
Forest plot Summary Elastic Net
Forest plot Summary Horseshoe
Forest plot Summary Naive
Data Preprocessing
Print Function for Elastic Net Summary
Print Function for Horseshoe Summary
Print Function for Naive Summary
Print Function for Naivepop Summary
Generation of a Design Matrix for Simulations
Simulate Covariates and Progression Free Survival Data
Simulation of Progression Free Survival Times
Subgroup Treatment Effect
Summary Elastic Net Function
Summary Horseshoe Function
Summary Naive
Summary Naivepop Function
Average Survival Curves
Subgroup Treatment Effect Horseshoe
Subgroup analyses are routinely performed in clinical trial analyses. From a methodological perspective, two key issues of subgroup analyses are multiplicity (even if only predefined subgroups are investigated) and the low sample sizes of subgroups which lead to highly variable estimates, see e.g. Yusuf et al (1991) <doi:10.1001/jama.1991.03470010097038>. This package implements subgroup estimates based on Bayesian shrinkage priors, see Carvalho et al (2019) <https://proceedings.mlr.press/v5/carvalho09a.html>. In addition, estimates based on penalized likelihood inference are available, based on Simon et al (2011) <doi:10.18637/jss.v039.i05>. The corresponding shrinkage based forest plots address the aforementioned issues and can complement standard forest plots in practical clinical trial analyses.
Useful links