Open AccessEstimating and evaluating personalized treatment recommendations from randomized trials with ptr
Author(s) -
Matthias Pierce,
Richard Emsley
Publication year - 2021
Publication title -
the stata journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.637
H-Index - 76
eISSN - 1536-8734
pISSN - 1536-867X
DOI - 10.1177/1536867x211025799
Subject(s) - categorical variable , randomized controlled trial , personalized medicine , resampling , confidence interval , outcome (game theory) , computer science , medicine , data mining , medical physics , machine learning , artificial intelligence , bioinformatics , mathematics , mathematical economics , biology
One of the targets of personalized medicine is to provide treatment recommendations using patient characteristics. We present the command ptr, which both predicts a personalized treatment recommendation algorithm and evaluates its effectiveness versus an alternative regime, using randomized trial data. The command allows for multiple (continuous or categorical) biomarkers and a binary or continuous outcome. Confidence intervals for the evaluation parameter are provided using bootstrap resampling.