Clayton copula for survival data with dependent censoring: An application to a tuberculosis treatment adherence data
Silvana Schneider,
Rodrigo Citton P. dos Reis,
Maicon M. F. Gottselig
et al.
Abstract:Ignoring the presence of dependent censoring in data analysis can lead to biased estimates, for example, not considering the effect of abandonment of the tuberculosis treatment may influence inferences about the cure probability. In order to assess the relationship between cure and abandonment outcomes, we propose a copula Bayesian approach. Therefore, the main objective of this work is to introduce a Bayesian survival regression model, capable of taking into account the dependent censoring in the adjustment. … Show more
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