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

Autor: Ciocănea-Teodorescu I; Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.; Victor Babeş National Institute of Pathology, Bucharest, Romania., Goetghebeur E; Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium., Waernbaum I; Department of Statistics, Uppsala University, Uppsala, Sweden., Schön S; Swedish Renal Registry, Jönköping County Hospital, Jönköping, Sweden., Gabriel EE; Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.; Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
Jazyk: angličtina
Zdroj: Statistics in medicine [Stat Med] 2023 May 30; Vol. 42 (12), pp. 1946-1964. Date of Electronic Publication: 2023 Mar 08.
DOI: 10.1002/sim.9706
Abstrakt: 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.
(© 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
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