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Transportability of causal inference under random dynamic treatment regimes for kidney–pancreas transplantation

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Listed:
  • Grace R. Lyden
  • David M. Vock
  • Erika S. Helgeson
  • Erik B. Finger
  • Arthur J. Matas
  • Jon J. Snyder
Abstract
A difficult decision for patients in need of kidney–pancreas transplant is whether to seek a living kidney donor or wait to receive both organs from one deceased donor. 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., wait times, organ qualities). Existing DTR methods average over the distribution of treatment versions in the data, estimating survival under a “representative intervention.” This is undesirable if 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 among strategy compliers in the target population (e.g., patients today). We describe an inverse‐probability‐weighted product‐limit estimator of survival under a GRI that performs well in simulations and can be implemented in standard statistical software. For continuous treatments (e.g., organ quality), weights are reformulated to depend on probabilities only, not densities. We apply our method to a national database of kidney–pancreas transplant candidates from 2001–2020 to illustrate that variability in transplant rate across years and centers results in qualitative differences in the optimal strategy for patient survival.

Suggested Citation

  • Grace R. Lyden & David M. Vock & Erika S. Helgeson & Erik B. Finger & Arthur J. Matas & Jon J. Snyder, 2023. "Transportability of causal inference under random dynamic treatment regimes for kidney–pancreas transplantation," Biometrics, The International Biometric Society, vol. 79(4), pages 3165-3178, December.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:4:p:3165-3178
    DOI: 10.1111/biom.13899
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    References listed on IDEAS

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    1. Cain Lauren E. & Robins James M. & Lanoy Emilie & Logan Roger & Costagliola Dominique & Hernán Miguel A., 2010. "When to Start Treatment? A Systematic Approach to the Comparison of Dynamic Regimes Using Observational Data," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-26, April.
    2. Jessica G. Young & Roger W. Logan & James M. Robins & Miguel A. Hernán, 2019. "Inverse Probability Weighted Estimation of Risk Under Representative Interventions in Observational Studies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 938-947, April.
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    4. repec:nas:journl:v:115:y:2018:p:12441-12446 is not listed on IDEAS
    5. Jeffrey A. Boatman & David M. Vock, 2018. "Estimating the causal effect of treatment regimes for organ transplantation," Biometrics, The International Biometric Society, vol. 74(4), pages 1407-1416, December.
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