Causal Effect of Donor Source on Survival of Renal Transplantation Using Marginal Structural Models.
Year: 2018
Type: article
Study design: Observational Study
Abstract: 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: =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.
Source: Europe PMC (PubMed Central)
Authors: Amir Almasi-Hashiani, Mohammad Ali Mansournia, Abdolreza Rezaeifard, Kazem Mohammad
Language: English
Subfield: Transplantation
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2802961574