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 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.
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|Issue||Vol 47 No 5 (2018)|
|Cox regression model Fractional polynomials Inverse probability weighting Marginal structural model Renal transplantation Stabilized weight|
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