Transportability across policy periods of random dynamic treatment regimes for multi-organ transplant

More than 90,000 people are waiting for a kidney transplant in the United States. For those who need both a kidney and another organ, a difficult decision is whether to seek out a living kidney donor or wait to receive both organs from one deceased donor, which can have immunological benefits. The framework of dynamic treatment regimes (DTRs) can inform this choice, but a patient-relevant strategy such as "wait for deceased-donor transplant" is ill-defined because there are multiple versions of treatment (i.e., multiple possible wait times and organ qualities). Existing DTR methods implicitly average over the distribution of versions of treatment in the study data, estimating survival under a so-called "representative intervention." This is undesirable when transporting inferences to a target population such as patients today, who experience shorter wait times thanks to evolutions in allocation policy. We, therefore, propose the concept of a generalized representative intervention (GRI): a random DTR that assigns treatment version by drawing from the distribution of treatments received by strategy compliers in the target population (e.g., patients who joined the waitlist under current allocation policy). We propose a class of inverse-probability-weighted product-limit estimators of survival under a GRI and a robust variance estimator, which perform well in simulations. For ease of estimation with continuous aspects of treatment (e.g., organ quality), weights are reformulated using Bayes' theorem to depend on probabilities only, not densities. We apply our method to a national database of adult kidney-pancreas transplant candidates from 2001-2020 to illustrate that variability in deceased-donor transplant rate and organ quality across years and centers results in qualitative differences in the optimal strategy for patient survival. Specifically, when transplant rate and organ quality are set to their observed distributions under today's allocation policies, survival for the average kidney-pancreas candidate is comparable whether the patient waits for deceased-donor kidney-pancreas transplant or pursues a near-immediate living-donor kidney transplant. This finding implies many potential living donors could be spared the risk of surgery in favor of performing more deceased-donor transplants.

Relevance: Despite a growing literature on statistical decision-making given resource constraints, almost all of the published methods have focused on how to distribute limited resources (as an institution) rather than how to act (as an individual) given the limited resources and distribution policies that are in place. Furthermore, solid-organ transplant research has historically emphasized measures that do not account for waitlist uncertainty and therefore have limited usefulness in patient decision-making, such as post-transplant survival. Our work fills a gap in both fields. We introduce a class of patient-centric estimators that can accommodate evolving organ allocation policies without limiting sample size or follow-up for survival outcomes. The rules for multi-organ allocation in particular have undergone major changes in recent years, and more changes are expected soon with the introduction of heart-kidney and lung-kidney policies, so it is essential to create statistical tools now that can help patients navigate a changing allocation system and make informed decisions about their care.

Background: I am a recently graduated PhD biostatistician now working on the Scientific Registry of Transplant Recipients. In addition to simulating potential allocation policies and evaluating transplant center performance in the U.S., we create patient-facing decision tools to inform treatment planning given policy and resource constraints.

I would be open to presenting a contributed session or a poster.

Causal inference

Adaptive treatment strategies

Generalizability

Inverse probability weights