Skip to main content

ETHNOS_APP

Home • Search • Journals • List 0

Quantifying the Treatment Effect of Kidney Transplantation Relative to Dialysis on Survival Time

New Results Based on Propensity Score Weighting and Longitudinal Observational Data from Sweden

Bibliographic Data

ID15470794
AuthorsYe Zhang (0009-0002-2442-4804, Lund University, corresponding author), Ulf-Göran Gerdtham (0000-0002-0647-7817, Lund University), Helena Rydell (0000-0003-1123-1835, Karolinska Institutet), Johan Jarl (0000-0002-6123-2433, Lund University)
Year2020
Volume17
Issue19
Pages7318-7318
Publication date2020-10-07
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph17197318
PMID33036407
OpenAlexW3091931922
LanguageEN
Citations received1
References cited8

Using observational data to assess the treatment effects on outcomes of kidney transplantation relative to dialysis for patients on renal replacement therapy is challenging due to the non-random selection into treatment. This study applied the propensity score weighting approach in order to address the treatment selection bias of kidney transplantation on survival time compared with dialysis for patients on the waitlist. We included 2676 adult waitlisted patients who started renal replacement therapy in Sweden between 1 January 1995, and 31 December 2012. Weibull and logistic regression models were used for the outcome and treatment models, respectively. The potential outcome mean and the average treatment effect were estimated using an inverse-probability-weighted regression adjustment approach. The estimated survival times from start of renal replacement therapy were 23.1 years (95% confidence interval (CI): 21.2-25.0) and 9.3 years (95% CI: 7.8-10.8) for kidney transplantation and dialysis, respectively. The survival advantage of kidney transplantation compared with dialysis was estimated to 13.8 years (95% CI: 11.4-16.2). There was no significant difference in the survival advantage of transplantation between men and women. Controlling for possible immortality bias reduced the survival advantage to 9.1-9.9 years. Our results suggest that kidney transplantation substantially increases survival time compared with dialysis in Sweden and that this consequence of treatment is equally distributed over sex

Confidence interval · Dialysis · Inverse probability weighting · Kidney transplantation · Logistic regression · Observational study · Propensity score matching · Renal replacement therapy · Survival analysis · Advanced Causal Inference Techniques · Medicine · Pregnancy and Medication Impact · Renal Transplantation Outcomes and Treatments · Internal Medicine · Transplantation

  • Women’s Access to Kidney Transplantation in France

    Open Access•Latame Komla Adoli, Latame Adoli et al.•International Journal of…•2022

  • Applied Logistic Regression

    Open Access•David W Hosmer, David W Jr Hosmer et al.•Applied Logistic Regression•2013

  • Validating recommendations for coronary angiography following acute myocardial infarction in the elderly

    Open Access•Sharon‐Lise T Normand, Sharon-Lise T Normand et al.•Journal of Clinical Epidemiology•2001

  • Inverse probability weighted estimation for general missing data problems

    Open Access•Jeffrey M Wooldridge•Journal of Econometrics•2007

  • Goodness of Link Tests for Generalized Linear Models

    Daryl Pregibon•Applied Statistics•1980

  • A new look at the statistical model identification

    Open Access•Hirotugu Akaike•IEEE Transactions on Automatic…•1974

  • The central role of the propensity score in observational studies for causal effects

    Paul R Rosenbaum, Donald B Rubin•Biometrika•1983

  • A new method of classifying prognostic comorbidity in longitudinal studies

    Open Access•Mary E Charlson, Peter Pompei et al.•Journal of Chronic Diseases•1987

  • Using Inverse Probability-Weighted Estimators in Comparative Effectiveness Analyses With Observational Databases

    Lesley H Curtis, Bradley G Hammill et al.•Medical Care•2007

Unique citing works1
Citations per year0,25
Citation span2022 - 2022 (1)
Citation velocityhistorical
Highly citedNo
Citation typesNeutral: 1
Ethnos_APP • Open Source Project • MIT License • Frontend v2.0.0 • Privacy and Cookies • API Documentation: api.ethnos.app/docs • API Source Code: GitHub • DOI: 10.5281/zenodo.17049435 • Frontend Source Code: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae