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Survival Models for Exploring Tuberculosis Clinical Trial Data - An Empirical Comparison
The proportional hazard (PH) model and its extension are used comprehensively to assess the effect of an intervention in the presence of covariates. The assumptions of PH model may not hold where the effect of the intervention is to accelerate the onset of an event. The accelerated failure time (AFT) model is the alternative when the PH assumption does not hold. The aim of this paper is to formulate a model that yields biological plausible and interpretable estimates of the effect of important covariates on survival time. The data consists of 1236 tuberculosis patients admitted in randomized controlled clinical trial. A total of six covariates are considered for modeling. The AFT model gives better prediction than the Cox PH model.
Keywords
Accelerated Failure Time Model, Proportional Hazards Model, Time Dependent Covariate, Tuberculosis
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- Andersen PK (1991) Survival analysis 1982-1991: The second decade of the proportional hazards regression model. Stat. Med. 10, 1931-1941.
- Cox DR (1972) Regression model and life tables (with discussion). J. Royal Stat. Soc. (B), 34, 187–220.
- Fleming TR and Lin DY (2000) Survival analysis in clinical trials: Past developments and future directions. Biometrics. 56, 971-983.
- Hernan MA, Cole SR, Margolick J, Cohen M and Robins JM (2005) Structural accelerated failure time models for survival analysis in studies with timevarying treatments. Pharmacoepidemiol. Drug safety. 14, 477–491.
- Huang J, Ma S and Xie H (2007) Least absolute deviations estimation for the accelerated failure time model. Statistica Sinica. 17, 1533-1548.
- Jin Z, Lin DY and Ying Z (2006) On least-squares regression with censored data. Biometrika. 93,147- 161.
- Jin Z, Lin DY, Wei LJ and Ying ZL (2003) Rank-based inference for the accelerated failure time model. Biometrika. 90, 341-353.
- Kalbfleisch JD and Prentice RL (1980) The statistical analysis of failure time data, John Wiley & Sons, NY.
- Kay R and Kinnersley N (2002) On the use of the accelerated failure time model as an alternative to the proportional hazards model in the treatment of time to event data: A case study in influenza. Drug Inform. J. 36, 571–579.
- Kleinbaum DG (1996) Survival analysis: A selflearning text. Springer-Verlag, NY.
- Lee ET (1992) Statistical methods for survival data analysis, 2nd edn., John Wiley, NY.
- Orbe J, Ferreira E and Nunez-Anton V (2002) Comparing proportional hazards and accelerated failure time models for survival analysis. Stat. Med. 21, 3493-3510.
- Patel K, Kay R and Rowell L (2006) Comparing proportional hazards and accelerated failure time models: an application in influenza. Pharm. Stat. 5, 213-224.
- Peng L and Huang Y (2008) Survival analysis with quantile regression models. J. Amer. Stat. Assoc. 103, 637-649.
- Robins JM and Tsiatis AA (1992) Semiparametric estimation of an accelerated failure time model with time dependent covariates. Biometrika. 79(2), 311- 319.
- TRC (Tuberculosis Research Centre, ICMR, Chennai, India) (2004) Split-drug regimens for the treatment of patients with sputum smear-positive pulmonary tuberculosis-a unique approach. Tropical Med. Int. Health. 9, 551-558.
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