Application of Cox Regression in Modeling Survival Rate of Drug Abuse
American Journal of Theoretical and Applied Statistics
Volume 7, Issue 1, January 2018, Pages: 1-7
Received: Jun. 28, 2017;
Accepted: Jul. 10, 2017;
Published: Dec. 20, 2017
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Robert Kasisi, Department of Mathematics and Computer Science, Moi University, Eldoret, Kenya
Joseph Koske, Department of Mathematics and Computer Science, Moi University, Eldoret, Kenya
Mathew Kosgei, Department of Mathematics and Computer Science, Moi University, Eldoret, Kenya
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Drug and substance abuse is a serious health problem in many countries. In Kenya drug abuse is one of the leading causes of mortality. Modeling the rate of survival of drug users involves determining time to relapse of drug users and the number of treatment episodes for full recovery. A study of the treatment programs that the subjects are enrolled was conducted. Those subjects who completed the treatment program and fully recovered from drug use were said to have survived while those who dropped out of the treatment program were said to have not survived. The objective of this study was to fit a cox repression model in determining a set of significant covariates for survival of drug users in Kenya. The dependent variable was survival time of the subject and the independent variables were age, gender, residence, marital status, job status, mode of drug abused and the type of drug abused. The study used data on drug use from Mathari National Hospital. Cox proportional hazards model was used to establish the hazard rate of a subject entering into drug use at different stages of life. Survival rate was 36.37% with the females having higher survival rates compared to male drug users. Age, gender, marital status and employment status were significant predictors of survival rate of drug users. The study recommended that subjects who were aged below 30 years, single and jobless required more intensive and specialized treatment. More intervention programs should be targeted to these subjects.
Survival Rate, Cox Regression, Intervention Programs, Hazard Rate, Drug Abuse
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Application of Cox Regression in Modeling Survival Rate of Drug Abuse, American Journal of Theoretical and Applied Statistics.
Vol. 7, No. 1,
2018, pp. 1-7.
Copyright © 2017 Authors retain the copyright of this article.
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Arteaga, I., Chen, C. C. & Reynolds, A. J. (2010) Childhood predictors of adult substance abuse “Children and Youth Services” Review, 32, pp. 1108-1120.
Cox, R. G., Zhang, L., Jonhson, W. D. & Bender, D. R. (2007). Academic performance and use: Findings from a state survey of public high school students. Journal of School Health, 77 (3), pp. 105-155.
Breslow N & Crowley, (1974). A Large Sample Study of the Life Table and Product Limit Estimates under Random Censorship. Annals of Statistics. Volume 2, Number 3, 437-453.
Collett, D. (2003). Modeling Binary Data. Chapman and Hall, London, 2nd Edition.
Efron, B, (1977). The Efficiency of Cox’s Likelihood Function for Censored Data. Journal of American Statistical Association, 72, 557-565.
Fisher & Roget, N, (2006). The Drug Abuse Treatment Outcomes. Journal of psychoactive drugs.
Gomberg, E. S. (1994). Risk factors for drinking over a woman’s life span. Alcohol Health & Research World, 18, 220-227.
Guttannova P. (2011). Adolescent transitioning and substance misuse. Journal of Substance Abuse, 5, 1-14.
Hemphill (2011). The role of psychology in the prevention of youth violence. Australian psychological association.
Hosmer, D. W. and Lemeshow, S. (1999). Applied Survival Analysis: Regression Modeling of Time to Event Data. Wiley, New York.
Schoenfeld, D. Partial residuals for the proportional hazards regression model. Biometrika 69, 239–241 (1982).