📝 SummarXiv

Abstract

When longitudinal outcomes are evaluated in mortal populations, their non-existence after death complicates the analysis and its causal interpretation. Where popular methods often merge longitudinal outcome and survival into one scale or otherwise try to circumvent the problem of mortality, some highly relevant questions require survival to be acknowledged as a unique condition. "\textit{What are my chances of survival}" and "\textit{What can I expect for my condition while still alive}" reflect the intrinsically two-dimensional outcome of survival and longitudinal outcome while-alive. We define a two-dimensional causal while-alive estimand for a point exposure and compare two methods for estimation in an observational setting. Regression-Standardization models survival and the observed longitudinal outcome before standardizing the latter to a target population weighted by its estimated survival. Alternatively, Inverse Probability of Treatment and Censoring Weighting weights the observed outcomes twice, to account for censoring and differences in baseline-case-mix. Both approaches rely on the same causal identification assumptions, but require different models to be correctly specified. With its potential to extrapolate, Regression-Standardization is more efficient when all assumptions are met. We show finite sample performance in a simulation study and apply the methods to a case study on quality of life in oncology.