A method for estimating treatment effects in clinical data associated with personalized medicine: a replication study

In this text, the methodology developed by Tian et al. is verified by the author via a number of numerical simulations: an arbitrary collection of random variables are generated to represent baseline covariates, and “true” treatment effect for each subject is calculated using a preset formula. By coding the treatment variable as ±1 and fitting the products of the treatment variable and baseline covariates (which essentially are treatment/covariate interaction terms) in a LASSO regression model,...
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