Causal Effect of Donor Source on Survival of Renal Transplantation Using Marginal Structural Models
AbstractBackground: 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.
Ahmadi AR, Lafranca JA, Claessens LA et al (2015). Shifting paradigms in eligibility criteria for live kidney donation: a systematic review. Kidney Int, 87(1):31-45.
Clemens KK, Thiessen-Philbrook H, Parikh CR et al (2006). Psychosocial health of living kidney donors: a systematic review. Am J Transplant, 6(12):2965-77.
Hassanzadeh J, Hashiani AA, Rajaeefard A et al (2010). Long-term survival of living donor renal transplants: A single center study. Indian J Nephrol. 20(4):179-84.
Hashiani AA, Rajaeefard A, Hasanzadeh J et al (2010). Ten-year graft survival of deceased-donor kidney transplantation: a single-center experience. Ren Fail, 32(4):440-7.
Denhaerynck K, Schmid-Mohler G, Kiss A et al (2014). Differences in Medication Adherence between Living and Deceased Donor Kidney Transplant Patients. Int J Organ Transplant Med. 2014;5(1):7-14.
Cho AJ, Jang HR, Lee JE et al (2014). Comparison of Cadaveric Kidney Transplantation From In-center and External Center Donors. Transplant proc, 46(10):3396-9.
Zafarghandi RM, Zeraati AA, Nazemian F et al (2010). Patient and Graft Outcome in Related, Unrelated and Deceased Renal Transplant Recipients: Single Center Experience. Nephro Urol Mon, 2(4):514-9.
Almasi-Hashiani A, Rajaeefard AR, Hassanzade J et al (2011). Graft Survival Rate of Renal Transplantation: A Single Center Experience, (1999-2009). Iran Red Crescent Med J, 13(6):392-7.
Robins J (1999). Association, Causation, And Marginal Structural Models. Synthese, 151-79.
Cole SR, Hernan MA, Margolick JB et al (2005). Marginal structural models for estimating the effect of highly active antiretroviral therapy initiation on CD4 cell count. Am J epidemiol, 162(5):471-8.
Godin O, Elbejjani M, Kaufman JS (2012). Body mass index, blood pressure, and risk of depression in the elderly: a marginal structural model. Am J epidemiol, 176(3):204-13.
Sato T, Matsuyama Y (2003). Marginal structural models as a tool for standardization. Epidemiology, 14(6):680-6.
Greenland S, Brumback B (2002). An overview of relations among causal modelling methods. Int J Epidemiol, 31(5):1030-7.
Mansournia MA, Altman DG (2016). Inverse probability weighting. BMJ, 2016;352:i189.
Mansournia MA, Danaei G, Forouzanfar MH et al (2012). Effect of physical activity on functional performance and knee pain in patients with osteoarthritis: analysis with marginal structural models. Epidemiology, 23(4):631-40.
Almasi-Hashiani A, Nedjat S, Mansournia MA (2018). Causal Methods for Observational Research: A Primer. Arch Iran Med, 21(4):In Press.
Mansournia MA, Etminan M, Danaei G et al (2017). Handling time varying confounding in observational research. BMJ, 359:j4587.
Abdollahpour I, Nedjat S, Mansournia MA et al (2018). Estimating the Marginal Causal Effect of Fish Consumption during Adolescence on Multiple Sclerosis: A Population – Based Incident Case–Control Study. Neuroepidemiology, In Press.
Robins JM, Hernan MA, Brumback B (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5):550-60.
Hernan MA, Brumback B, Robins JM (2000). Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology,11(5):561-70.
Tsai AC, Weiser SD, Petersen ML et al (2010). A Marginal Structural Model to Estimate the Causal Effect of Antidepressant Medication Treatment on Viral Suppression among Homeless and Marginally Housed Persons Living with HIV. Arch Gen Psychiatry, 67(12):1282-90.
Hernán MA, Brumback B, Robins JM (2001). Marginal Structural Models to Estimate the Joint Causal Effect of Nonrandomized Treatments. J Am Stat Assoc, 96(454):440-8.
Royston P, Ambler G, Sauerbrei W (1999). The use of fractional polynomials to model continuous risk variables in epidemiology. Int J Epidemiol, 28(5):964-74.
Hess KR (1995). Graphical methods for assessing violations of the proportional hazards assumption in Cox regression. Stat Med, 14(15):1707-23.
Cole SR, Hernán MA (2008). Constructing Inverse Probability Weights for Marginal Structural Models. Am J Epidemiol, 168(6):656-64.
Hernan MA, Robins JM (2006). Estimating causal effects from epidemiological data. J Epidemiol Community Health, 60(7):578-86.
Matas AJ, Payne WD, Sutherland DE et al (2001). 2,500 Living Donor Kidney Transplants: A Single-Center Experience. Ann Surg, 234(2):149-64.
Nemati E, Einollahi B, Lesan Pezeshki M, et al (2014). Does Kidney Transplantation With Deceased or Living Donor Affect Graft Survival? Nephrourol Mon, 6(4):e12182.
Simpkins CE, Montgomery RA, Hawxby AM et al (2007). Cold Ischemia Time and Allograft Outcomes in Live Donor Renal Transplantation: Is Live Donor Organ Transport Feasible? Am J Transplant, 7(1):99-107.
Perez Valdivia MA, Gentil MA, Toro M et al (2011). Impact of cold ischemia time on initial graft function and survival rates in renal transplants from deceased donors performed in Andalusia. Transplant Proc, 43(6):2174-6.
Opelz G, Wujciak T, Dohler B et al (1999). HLA compatibility and organ transplant survival. Collaborative Transplant Study. Rev immunogenet, 1(3):334-42.
Cook NR, Cole SR, Hennekens CH (2002). Use of a Marginal Structural Model to Determine the Effect of Aspirin on Cardiovascular Mortality in the Physicians' Health Study. Am J Epidemiol, 155(11):1045-53.