Hypothetical Treatment Accelerations: Estimating Causal Effects of Kidney Transplants from Observational Data

TL;DR

This paper introduces a continuous-time marginal structural model to estimate how reducing waiting times for kidney transplants could improve patient survival, using observational data and hypothetical treatment acceleration scenarios.

Contribution

It develops a novel continuous-time MSM framework for estimating causal effects of hypothetical treatment timing changes from observational data.

Findings

Model applied to Norwegian kidney failure cohort

Estimated survival improvements with reduced waiting times

Demonstrated feasibility of causal inference in treatment acceleration scenarios

Abstract

Patients with end-stage kidney disease can expect to wait for several years before they receive a transplant, all the while their health deteriorates. How would the survival change if we managed to reduce these waiting times? To provide an answer, we present a continuous-time marginal structural model (MSM) of hypothetical scenarios in which the time until treatment is changed. In these scenarios, the treatment process is defined on a hypothetical time scale where time passes at a different rate compared to the time actually observed. Changing the time of the treatment process corresponds to changing the joint probability distribution, thereby making it possible to identify and estimate hypothetical parameters from observational data using previously developed methodology. We demonstrate this treatment-accelerated MSM using observational data from a Norwegian cohort of elderly patients with kidney failure. This model can potentially be useful to health authorities looking to assess the impacts of reducing the waiting times for organ transplantation in a given patient population.