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Trial emulation and survival analysis for disease incidence registers : A case study on the causal effect of pre-emptive kidney transplantation. / Olarte Parra, Camila; Waernbaum, Ingeborg; Schön, Staffan; Goetghebeur, Els.

In: Statistics in medicine, Vol. 41, No. 21, 20.09.2022, p. 4176-4199.

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Olarte Parra, Camila ; Waernbaum, Ingeborg ; Schön, Staffan ; Goetghebeur, Els. / Trial emulation and survival analysis for disease incidence registers : A case study on the causal effect of pre-emptive kidney transplantation. In: Statistics in medicine. 2022 ; Vol. 41, No. 21. pp. 4176-4199.

BibTeX

@article{55fb9a1e3fa34c7a8ffd0e5319c9d25b,
title = "Trial emulation and survival analysis for disease incidence registers: A case study on the causal effect of pre-emptive kidney transplantation",
abstract = "When drawing causal inference from observed data, failure time outcomes present additional challenges of censoring often combined with other missing data patterns. In this article, we follow incident cases of end-stage renal disease to examine the effect on all-cause mortality of starting treatment with transplant, so-called pre-emptive kidney transplantation, vs starting with dialysis possibly followed by delayed transplantation. The question is relatively simple: which start-off treatment is expected to bring the best survival for a target population? To address it, we emulate a target trial drawing on the long term Swedish Renal Registry, where a growing common set of baseline covariates was measured nationwide. Several lessons are learned which pertain to long term disease registers more generally. With characteristics of cases and versions of treatment evolving over time, informative censoring is already introduced in unadjusted Kaplan-Meier curves. This leads to misrepresented survival chances in observed treatment groups. The resulting biased treatment association may be aggravated upon implementing IPW for treatment. Aware of additional challenges, we further recall how similar studies to date have selected patients into treatment groups based on events occurring post treatment initiation. Our study reveals the dramatic impact of resulting immortal time bias combined with other typical features of long-term incident disease registers, including missing covariates during the early phases of the register. We discuss feasible ways of accommodating these features when targeting relevant estimands, and demonstrate how more than one causal question can be answered relying on the no unmeasured baseline confounders assumption.",
keywords = "causal inference, disease registries, kidney transplantation, observational study, survival analysis, target trial emulation",
author = "{Olarte Parra}, Camila and Ingeborg Waernbaum and Staffan Sch{\"o}n and Els Goetghebeur",
note = "Funding Information: This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under the Marie Sklodowska‐Curie Grant Agreement No. 676207. Ingeborg Waernbaum was funded by the Swedish Research Council, Grant No. 2016‐00703. Funding Information: information H2020 Marie Sk{\l}odowska-Curie Actions, Grant/Award Number: 676207; Vetenskapsr{\aa}det, Grant/Award Number: 2016-00703We are grateful to the Steering Group of the Swedish Renal Registry and to Susanne Gabara for assisting us with data transfer. We are thankful to Linda Nyanchoka and Rapha{\"e}l Porcher for their comments and suggestions on an earlier draft and to Dries Reynders who independently repeated the analyses of the data to ensure reproducibility. The computational resources (Stevin Supercomputer Infrastructure) and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by Ghent University, FWO, and the Flemish Government - Department EWI. In particular, we thank Alvaro Garcia and Kenneth Hoste for their help while running the analyses. Funding Information: The computational resources (Stevin Supercomputer Infrastructure) and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by Ghent University, FWO, and the Flemish Government ‐ Department EWI. In particular, we thank Alvaro Garcia and Kenneth Hoste for their help while running the analyses. Publisher Copyright: {\textcopyright} 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.",
year = "2022",
month = sep,
day = "20",
doi = "10.1002/sim.9503",
language = "English",
volume = "41",
pages = "4176--4199",
journal = "Statistics in medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",
number = "21",

}

RIS

TY - JOUR

T1 - Trial emulation and survival analysis for disease incidence registers

T2 - A case study on the causal effect of pre-emptive kidney transplantation

AU - Olarte Parra, Camila

AU - Waernbaum, Ingeborg

AU - Schön, Staffan

AU - Goetghebeur, Els

N1 - Funding Information: This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under the Marie Sklodowska‐Curie Grant Agreement No. 676207. Ingeborg Waernbaum was funded by the Swedish Research Council, Grant No. 2016‐00703. Funding Information: information H2020 Marie Skłodowska-Curie Actions, Grant/Award Number: 676207; Vetenskapsrådet, Grant/Award Number: 2016-00703We are grateful to the Steering Group of the Swedish Renal Registry and to Susanne Gabara for assisting us with data transfer. We are thankful to Linda Nyanchoka and Raphaël Porcher for their comments and suggestions on an earlier draft and to Dries Reynders who independently repeated the analyses of the data to ensure reproducibility. The computational resources (Stevin Supercomputer Infrastructure) and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by Ghent University, FWO, and the Flemish Government - Department EWI. In particular, we thank Alvaro Garcia and Kenneth Hoste for their help while running the analyses. Funding Information: The computational resources (Stevin Supercomputer Infrastructure) and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by Ghent University, FWO, and the Flemish Government ‐ Department EWI. In particular, we thank Alvaro Garcia and Kenneth Hoste for their help while running the analyses. Publisher Copyright: © 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

PY - 2022/9/20

Y1 - 2022/9/20

N2 - When drawing causal inference from observed data, failure time outcomes present additional challenges of censoring often combined with other missing data patterns. In this article, we follow incident cases of end-stage renal disease to examine the effect on all-cause mortality of starting treatment with transplant, so-called pre-emptive kidney transplantation, vs starting with dialysis possibly followed by delayed transplantation. The question is relatively simple: which start-off treatment is expected to bring the best survival for a target population? To address it, we emulate a target trial drawing on the long term Swedish Renal Registry, where a growing common set of baseline covariates was measured nationwide. Several lessons are learned which pertain to long term disease registers more generally. With characteristics of cases and versions of treatment evolving over time, informative censoring is already introduced in unadjusted Kaplan-Meier curves. This leads to misrepresented survival chances in observed treatment groups. The resulting biased treatment association may be aggravated upon implementing IPW for treatment. Aware of additional challenges, we further recall how similar studies to date have selected patients into treatment groups based on events occurring post treatment initiation. Our study reveals the dramatic impact of resulting immortal time bias combined with other typical features of long-term incident disease registers, including missing covariates during the early phases of the register. We discuss feasible ways of accommodating these features when targeting relevant estimands, and demonstrate how more than one causal question can be answered relying on the no unmeasured baseline confounders assumption.

AB - When drawing causal inference from observed data, failure time outcomes present additional challenges of censoring often combined with other missing data patterns. In this article, we follow incident cases of end-stage renal disease to examine the effect on all-cause mortality of starting treatment with transplant, so-called pre-emptive kidney transplantation, vs starting with dialysis possibly followed by delayed transplantation. The question is relatively simple: which start-off treatment is expected to bring the best survival for a target population? To address it, we emulate a target trial drawing on the long term Swedish Renal Registry, where a growing common set of baseline covariates was measured nationwide. Several lessons are learned which pertain to long term disease registers more generally. With characteristics of cases and versions of treatment evolving over time, informative censoring is already introduced in unadjusted Kaplan-Meier curves. This leads to misrepresented survival chances in observed treatment groups. The resulting biased treatment association may be aggravated upon implementing IPW for treatment. Aware of additional challenges, we further recall how similar studies to date have selected patients into treatment groups based on events occurring post treatment initiation. Our study reveals the dramatic impact of resulting immortal time bias combined with other typical features of long-term incident disease registers, including missing covariates during the early phases of the register. We discuss feasible ways of accommodating these features when targeting relevant estimands, and demonstrate how more than one causal question can be answered relying on the no unmeasured baseline confounders assumption.

KW - causal inference

KW - disease registries

KW - kidney transplantation

KW - observational study

KW - survival analysis

KW - target trial emulation

UR - http://www.scopus.com/inward/record.url?scp=85133589709&partnerID=8YFLogxK

U2 - 10.1002/sim.9503

DO - 10.1002/sim.9503

M3 - Article

C2 - 35808992

VL - 41

SP - 4176

EP - 4199

JO - Statistics in medicine

JF - Statistics in medicine

SN - 0277-6715

IS - 21

ER -

ID: 24985987