README.md

In chengs94/BioPETsurv: Biomarker Prognostic Enrichment Tool for clinical trials with survival outcomes

BioPETsurv: Biomarker Prognostic Enrichment Tool for clinical trials with survival outcomes

Prognostic Enrichment is a clinical trial strategy of evaluating an intervention in a patient population with a higher rate of the unwanted event than the broader patient population (R. Temple (2010) ). A higher event rate translates to a lower sample size for the clinical trial, which can have both practical and ethical advantages. The package BioPET-Surv provides tools to evaluate biomarkers for prognostic enrichment of clinical trials with survival or time-to-event outcomes. An associated R shiny webtool (with simplified functionality) can be found here.

Key functions of this package are:

• sim_data: Simulate a dataset containing biomarker and survival observations
• surv_enrichment: Estimate trial characteristics at different levels of enrichment, given a biomarker (which can be a single biomarker or a composite)
• surv_plot_enrichment: Visualize trial characteristics returned by surv_enrichment

Update history

v4, 9/19/2019

Added the functionality of simulating datasets containing biomarker and survival observations. The R function currently allows for constant baseline hazard.

v3, 5/21/2019:

Incorporated an alternative method for calculating event rates. The method comes from Heagerty et al (2000), and uses a kernel smoothed version of Kaplan-Meier survival estimators. This method allows the censoring process to be dependent on the biomarker, and guarantees monotone ROC curves (if the user is interested in the prognostic capacity of a biomarker represented by time-dependent ROC curves).

chengs94/BioPETsurv documentation built on Nov. 25, 2019, 9:45 a.m.