Calculating the Expected Net Benefit of Sampling for Survival Data: A Tutorial and Case Study.

The net value of reducing decision uncertainty by collecting additional data is quantified by the expected net benefit of sampling (ENBS). This tutorial presents a general-purpose algorithm for computing the ENBS for collecting survival data along with a step-by-step implementation in R.The algorithm is based on recently published methods for simulating survival data and computing expected value of sample information that do not rely on the survival data to follow any particular parametric distribution and that can take into account any arbitrary censoring process.

A conserved complex lipid signature marks human muscle aging and responds to short-term exercise.

Studies in preclinical models suggest that complex lipids, such as phospholipids, play a role in the regulation of longevity. However, identification of universally conserved complex lipid changes that occur during aging, and how these respond to interventions, is lacking. Here, to comprehensively map how complex lipids change during aging, we profiled ten tissues in young versus aged mice using a lipidomics platform.

General-Purpose Methods for Simulating Survival Data for Expected Value of Sample Information Calculations.

Background: Expected value of sample information (EVSI) quantifies the expected value to a decision maker of reducing uncertainty by collecting additional data. EVSI calculations require simulating plausible data sets, typically achieved by evaluating quantile functions at random uniform numbers using standard inverse transform sampling (ITS). This is straightforward when closed-form expressions for the quantile function are available, such as for standard parametric survival models, but these are often unavailable when assuming treatment effect waning and for flexible survival models.

An Efficient Method for Computing Expected Value of Sample Information for Survival Data from an Ongoing Trial.

Background: Decisions about new health technologies are increasingly being made while trials are still in an early stage, which may result in substantial uncertainty around key decision drivers such as estimates of life expectancy and time to disease progression. Additional data collection can reduce uncertainty, and its value can be quantified by computing the expected value of sample information (EVSI), which has typically been described in the context of designing a future trial. In this article, we develop new methods for computing the EVSI of extending an existing trial's follow-up, first for an assumed survival model and then extending to capture uncertainty about the true survival model.