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LINDLEY DISTRIBUTION AS FRAILTY MODELS WITH APPLICATION TO LIFETIME DATA
Keywords:
Lindley distribution, frailty models, Lindley with frailty models, survival analysis.DOI:
https://doi.org/10.17654/0972361722031Abstract
Unobserved heterogeneity is called frailty, measuring frailty and multiplying it with the baseline distribution is critical for clustered survival data analysis. Lindley distribution is the one among classical distribution, yet it has limited applications in lifetime data analysis. Therefore, the objective of the study is to fit the frailty models for Lindley distribution and to compare the results with other existing distributions such as Exponential, Weibull, Lognormal and Log-logistic to test the effectiveness. Two real-life data sets and simulated data were used to fit the baseline distributions with frailty models. The study results revealed that Lindley with Gamma frailty model is a good choice for kidney infection data and Lindley with Inverse Gaussian frailty model is the best fit for CGD (Chronic Granulomatous Disease) and the simulated data set. Further, Lindley with frailty models points out the lowest Akaike’s Information Criteria (AIC) and Bayesian Information Criteria (BIC) values than other baseline distributions. So we suggest that Lindley baseline distribution with the frailty models is a potential alternative approach for clustered survival data analysis.
Received: November 8, 2021
Revised: January 20, 2022
Accepted: March 7, 2022
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