Prediction of time to reflux using accelerated failure time model of Weibull distribution in children with antenatal hydronephrosis
AbstractBackground & Aim: Prediction of time to reflux can aid healthcare providers and preparation programs. We constructed a risk prediction instrument for occurrence reflux in children with antenatal hydronephrosis.Methods & Materials: Demographic and clinical information was collected retrospectively in children with the antenatal hydronephrosis and mostly with reflux, followed at least 5 years. Results: Accelerated failure time model of data from 333 children was developed to assess the risk of time to reflux. Likelihood ratio tests of statistical significant were used to identify best fitting predictive function. Variables “gender”, “Sr”, and “severity of ANH (in severe level)” were highly significant (p<0.05) in multivariate model, adjusting for some traditional risk factors.Conclusion: This proposed risk probability model allows prediction of time to reflux for children with antenatal hydronephrosis to better inform parents from possible time of occurrence reflux and treatment strategies.
Vesicoureteric reflux: all in the genes? Report of a meeting of physicians at the Hospital for Sick Children, Great Ormond Street, London. Lancet. 1996;348(9029):725-8.
Kramer S. Vesico-Ureteral reflux. In Clinical Pediatric Urology. 4th ed. Belman AK, Kramer SA, editors. London: Martin Dunitz; 2002. 749 p.
Williams G, Fletcher JT, Alexander SI, Craig JC. Vesicoureteral reflux. Journal of the American Society of Nephrology : JASN. 2008;19(5):847-62.
Mathews R, Carpenter M, Chesney R, Hoberman A, Keren R, Mattoo T, et al. Controversies in the management of vesicoureteral reflux: the rationale for the RIVUR study. Journal of pediatric urology. 2009;5(5):336-41.
Sharbaf FG, Fallahzadeh MH, Modarresi AR, Esmaeili M. Primary vesicoureteral reflux in Iranian children. Indian Pediatr. 2007;44(2):128-30.
Putter H, Fiocco M, Geskus RB. Tutorial in biostatistics: competing risks and multi-state models. Stat Med. 2007;26(11):2389-430.
Hosmer DW, Lemeshow S, May S. Applied survival analysis : regression modeling of time-to-event data. 2nd ed. Hoboken, N.J.: Wiley-Interscience; 2008. xiii, 392 p. p.
Southern DA, Faris PD, Brant R, Galbraith PD, Norris CM, Knudtson ML, et al. Kaplan-Meier methods yielded misleading results in competing risk scenarios. Journal of clinical epidemiology. 2006;59(10):1110-4.
Cox DR. Regression models and life tables. JRoyal Statistical Society 1972;34(B):187-202.
Collett D. Modelling survival data in medical research. 2nd ed. Boca Raton, Fla.: Chapman & Hall/CRC; 2003. 391 p. p.
Lawless JF. Statistical models and methods for lifetime data. 2nd ed. Hoboken, N.J.: Wiley-Interscience; 2003. xx, 630 p. p.
Anderson KM, Wilson PW, Odell PM, Kannel WB. An updated coronary risk profile. A statement for health professionals. Circulation. 1991;83(1):356-62.
Harrell JFE. Regression modeling strategies : with applications to linear models, logistic regression, and survival analysis. New York: Springer-Verlag; 2001. xxii, 568 p. p.
Hosmer DW, Lemeshow S. Applied logistic regression. 2nd ed. New York: Wiley; 2000. xii, 375 p. p.