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  • Ciocanea-Teodorescu, Iuliana (author)

Causal inference in survival analysis under deterministic missingness of confounders in register data

  • Article/chapterEnglish2023

Publisher, publication year, extent ...

  • 2023-03-08
  • John Wiley & Sons,2023
  • electronicrdacarrier

Numbers

  • LIBRIS-ID:oai:DiVA.org:uu-498055
  • https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-498055URI
  • https://doi.org/10.1002/sim.9706DOI
  • http://kipublications.ki.se/Default.aspx?queryparsed=id:152067185URI

Supplementary language notes

  • Language:English
  • Summary in:English

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  • Subject category:ref swepub-contenttype
  • Subject category:art swepub-publicationtype

Notes

  • Long-term register data offer unique opportunities to explore causal effects of treatments on time-to-event outcomes, in well-characterized populations with minimum loss of follow-up. However, the structure of the data may pose methodological challenges. Motivated by the Swedish Renal Registry and estimation of survival differences for renal replacement therapies, we focus on the particular case when an important confounder is not recorded in the early period of the register, so that the entry date to the register deterministically predicts confounder missingness. In addition, an evolving composition of the treatment arms populations, and suspected improved survival outcomes in later periods lead to informative administrative censoring, unless the entry date is appropriately accounted for. We investigate different consequences of these issues on causal effect estimation following multiple imputation of the missing covariate data. We analyse the performance of different combinations of imputation models and estimation methods for the population average survival. We further evaluate the sensitivity of our results to the nature of censoring and misspecification of fitted models. We find that an imputation model including the cumulative baseline hazard, event indicator, covariates and interactions between the cumulative baseline hazard and covariates, followed by regression standardization, leads to the best estimation results overall, in simulations. Standardization has two advantages over inverse probability of treatment weighting here: it can directly account for the informative censoring by including the entry date as a covariate in the outcome model, and allows for straightforward variance computation using readily available software.

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Added entries (persons, corporate bodies, meetings, titles ...)

  • Goetghebeur, Els (author)
  • Waernbaum, Ingeborg,1972-Uppsala universitet,Statistiska institutionen(Swepub:uu)ingwa901 (author)
  • Schön, Staffan (author)
  • Gabriel, Erin E. (author)
  • Uppsala universitetStatistiska institutionen (creator_code:org_t)

Related titles

  • In:Statistics in Medicine: John Wiley & Sons42:12, s. 1946-19640277-67151097-0258

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Ciocanea-Teodore ...
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Schön, Staffan
Gabriel, Erin E.
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NATURAL SCIENCES
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and Probability Theo ...
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Uppsala University
Karolinska Institutet

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