Clayton copula for survival data with dependent censoring: An application to a tuberculosis treatment adherence data.

Autor: Schneider S; Department of Statistics, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.; Graduate Program in Statistics, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil., Dos Reis RCP; Department of Statistics, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.; Graduate Program in Epidemiology, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil., Gottselig MMF; Department of Statistics, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil., Fisch P; Graduate Program in Epidemiology, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.; Epidemiology Department, Hospital Nossa Senhora da Conceição, Porto Alegre, Rio Grande do Sul, Brazil., Knauth DR; Graduate Program in Epidemiology, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil., Vigo Á; Department of Statistics, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.; Graduate Program in Epidemiology, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil.
Jazyk: angličtina
Zdroj: Statistics in medicine [Stat Med] 2023 Oct 15; Vol. 42 (23), pp. 4057-4081. Date of Electronic Publication: 2023 Aug 01.
DOI: 10.1002/sim.9858
Abstrakt: 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. So, this proposed approach is based on Clayton's copula, to provide the relation between survival and dependent censoring times. In addition, the Weibull and the piecewise exponential marginal distributions are considered in order to fit the times. A simulation study is carried out to perform comparisons between different scenarios of dependence, different specifications of prior distributions, and comparisons with the maximum likelihood inference. Finally, we apply the proposed approach to a tuberculosis treatment adherence dataset of an HIV cohort from Alvorada-RS, Brazil. Results show that cure and abandonment outcomes are negatively correlated, that is, as long as the chance of abandoning the treatment increases, the chance of tuberculosis cure decreases.
(© 2023 John Wiley & Sons Ltd.)
Databáze: MEDLINE
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