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Survival analysis of Breast and Small-Cell Lung Cancer Patients using Conditional Logistic Regression Models

Waheed Babatunde Yahya, Kurt Ulm


In this study we investigated the use of (conditional) logistic regression (CLR) techniques to model failure-time data. The widely adopted semi-parametric approach of Cox proportional hazard (CPH) model has been reported to be a successive combination of the CLR approach. This implies that the CLR method could as well be appropriate to model event history situations. The use of this regression technique to model survival data is hereby examined. It is established that this regression method, apart from sharing some features with the Cox model, is an easy alternative to model survival data. The procedure is found to be more flexible to apply especially when time-varying effects of some prognostic factors are suspected. In the two situations considered, results from the CLR models are found to be similar with those obtained from the classical Cox models. Data sets on breast and small-cell lung cancer patients were used to demonstrate our results.


Conditional logistic regression, Cox proportional hazard model, Log partial likelihood function, Odds-ratio

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