Truncation by Death and the Survival-Adjusted Median: From Missing Data to Useful Summary Measures

• Activity Number: 263 - Addressing Incomplete Data in Public Health Studies: New Frontiers for Network-Based Studies and Meta-Analyses
• Type: Topic Contributed
• Date/Time: Tuesday, August 4, 2020 : 1:00 PM to 2:50 PM
• Sponsor: ENAR
• Author(s): Judith Lok* and Qingyan Xiang
• Companies: Boston University Department of Mathematics & Statistics and Boston University
• Keywords: Survival-adjusted median; Causal inference; Survival analysis; Always-survivors; Truncation by death; HIV

Abstract

One could argue that if a person dies, their health outcomes are missing. On the other hand, one could argue that if a person dies, their health outcomes are completely obvious. This talk considers the second point of view, and advocates to not always see death as a mechanism through which health outcomes are missing, but rather as part of the outcome measure. This is especially useful when some people’s lives may be saved by a treatment we wish to study. We will show that both the median health score in those alive and the median health score in the always-survivors can lead one to believe that there is a trade-off between survival and good health scores, even in cases where in clinical practice both the probability of survival and the probability of a good health score are better for one treatment arm. To overcome this issue, the survival-adjusted median is proposed as an alternative summary measure of health outcomes in the presence of death. It is the outcome value such that 50% of the population is alive with an outcome above that value. The survival-adjusted median can be interpreted as what happens to the “average” person. We will illustrate our approach with an HIV example.