Causal Effect of Donor Source on Survival of Renal Transplantation Using Marginal Structural Models

Background: Marginal Structural Models (MSMs) are novel methods to estimate causal effect in epidemiology by using Inverse Probability of Treatment Weighting (IPTW) and Stabilized Weight to reduce confounding effects. This study aimed to estimate causal effect of donor source on renal transplantati...

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Main Authors: Amir ALMASI-HASHIANI, Mohammad Ali MANSOURNIA, Abdolreza REZAEIFARD, Kazem MOHAMMAD
Format: Article
Language:English
Published: Tehran University of Medical Sciences 2018-05-01
Series:Iranian Journal of Public Health
Subjects:
Online Access:https://ijph.tums.ac.ir/index.php/ijph/article/view/13353
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spelling doaj-122fd3d8add64532ac5fce4c7a1c717e2021-01-02T15:43:32ZengTehran University of Medical SciencesIranian Journal of Public Health2251-60852251-60932018-05-01475Causal Effect of Donor Source on Survival of Renal Transplantation Using Marginal Structural ModelsAmir ALMASI-HASHIANI0Mohammad Ali MANSOURNIA1Abdolreza REZAEIFARD2Kazem MOHAMMAD3Dept. of Epidemiology and Reproductive Health, Reproductive Epidemiology Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran AND Dept. of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, IranDept. of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, IranDept. of Epidemiology, School of Health, Shiraz University of Medical Sciences, Shiraz, IranDept. of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran Background: Marginal Structural Models (MSMs) are novel methods to estimate causal effect in epidemiology by using Inverse Probability of Treatment Weighting (IPTW) and Stabilized Weight to reduce confounding effects. This study aimed to estimate causal effect of donor source on renal transplantation survival. Methods: In this cohort study, 1354 transplanted patients with a median 42.55 months follow-up in Namazee Hospital Transplantation Center, Shiraz from Mar 1999 to Mar 2009, were included to use marginal structural Cox regression, binomial logistic regression model to estimate causal effect of donor source on the survival of renal transplantation. IPTW and stabilized inverse probability of treatment weighting are used as weights. Results: The un-weighted (crude) hazard ratios for live unrelated donor and deceased donor in comparison to live related donor as reference group was (HR: 1.03, 95% CI: 0.58-1.83, P=0.89) and (HR: 2.69, 95% CI: 1.67-4.31, P=0.001), respectively. Using a marginal structural Cox regression model and by stabilized weight, the hazard ratios for live-unrelated donor and cadaveric donor were (HR: 1.08, 95% CI: 0.47-2.45, P=0.84) and (HR: 3.63, 95% CI: 1.59-8.26, P=0.002), respectively. There was no difference between estimated effect size from marginal structural Cox regression, marginal structural logistic regression, and marginal structural Weibull regression model. Conclusion: There is no difference between related and unrelated donor source hazard ratio; however, hazard ratio for cadaveric donor was 3.63 times of hazard ratio for related donor and 3.34 times of it for unrelated donor. Therefore, the live donor (related or unrelated) has a better survival of renal transplantation than cadaveric donor.   https://ijph.tums.ac.ir/index.php/ijph/article/view/13353Cox regression modelFractional polynomialsInverse probability weightingMarginal structural modelRenal transplantationStabilized weight
collection DOAJ
language English
format Article
sources DOAJ
author Amir ALMASI-HASHIANI
Mohammad Ali MANSOURNIA
Abdolreza REZAEIFARD
Kazem MOHAMMAD
spellingShingle Amir ALMASI-HASHIANI
Mohammad Ali MANSOURNIA
Abdolreza REZAEIFARD
Kazem MOHAMMAD
Causal Effect of Donor Source on Survival of Renal Transplantation Using Marginal Structural Models
Iranian Journal of Public Health
Cox regression model
Fractional polynomials
Inverse probability weighting
Marginal structural model
Renal transplantation
Stabilized weight
author_facet Amir ALMASI-HASHIANI
Mohammad Ali MANSOURNIA
Abdolreza REZAEIFARD
Kazem MOHAMMAD
author_sort Amir ALMASI-HASHIANI
title Causal Effect of Donor Source on Survival of Renal Transplantation Using Marginal Structural Models
title_short Causal Effect of Donor Source on Survival of Renal Transplantation Using Marginal Structural Models
title_full Causal Effect of Donor Source on Survival of Renal Transplantation Using Marginal Structural Models
title_fullStr Causal Effect of Donor Source on Survival of Renal Transplantation Using Marginal Structural Models
title_full_unstemmed Causal Effect of Donor Source on Survival of Renal Transplantation Using Marginal Structural Models
title_sort causal effect of donor source on survival of renal transplantation using marginal structural models
publisher Tehran University of Medical Sciences
series Iranian Journal of Public Health
issn 2251-6085
2251-6093
publishDate 2018-05-01
description Background: Marginal Structural Models (MSMs) are novel methods to estimate causal effect in epidemiology by using Inverse Probability of Treatment Weighting (IPTW) and Stabilized Weight to reduce confounding effects. This study aimed to estimate causal effect of donor source on renal transplantation survival. Methods: In this cohort study, 1354 transplanted patients with a median 42.55 months follow-up in Namazee Hospital Transplantation Center, Shiraz from Mar 1999 to Mar 2009, were included to use marginal structural Cox regression, binomial logistic regression model to estimate causal effect of donor source on the survival of renal transplantation. IPTW and stabilized inverse probability of treatment weighting are used as weights. Results: The un-weighted (crude) hazard ratios for live unrelated donor and deceased donor in comparison to live related donor as reference group was (HR: 1.03, 95% CI: 0.58-1.83, P=0.89) and (HR: 2.69, 95% CI: 1.67-4.31, P=0.001), respectively. Using a marginal structural Cox regression model and by stabilized weight, the hazard ratios for live-unrelated donor and cadaveric donor were (HR: 1.08, 95% CI: 0.47-2.45, P=0.84) and (HR: 3.63, 95% CI: 1.59-8.26, P=0.002), respectively. There was no difference between estimated effect size from marginal structural Cox regression, marginal structural logistic regression, and marginal structural Weibull regression model. Conclusion: There is no difference between related and unrelated donor source hazard ratio; however, hazard ratio for cadaveric donor was 3.63 times of hazard ratio for related donor and 3.34 times of it for unrelated donor. Therefore, the live donor (related or unrelated) has a better survival of renal transplantation than cadaveric donor.  
topic Cox regression model
Fractional polynomials
Inverse probability weighting
Marginal structural model
Renal transplantation
Stabilized weight
url https://ijph.tums.ac.ir/index.php/ijph/article/view/13353
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