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ABSTRACT
In some survival studies, the exact time of the event of interest is unknown, but the event is known to have occurred during a particular period of time (interval-censored data). If the diagnostic tool used to detect the event of interest is not perfectly sensitive and specific, outcomes may be mismeasured; a healthy subject may be diagnosed as sick and a sick one may be diagnosed as healthy. In such cases, traditional survival analysis methods produce biased estimates for the time-to-failure distribution parameters (Paggiaro and Torelli Citation2004). In this context, we developed a parametric model that incorporates sensitivity and specificity into a grouped survival data analysis (a case of interval-censored data in which all subjects are tested at the same predetermined time points). Inferential aspects and properties of the methodology, such as the likelihood function and identifiability, are discussed in this article. Assuming known and non differential misclassification, Monte Carlo simulations showed that the proposed model performed well in the case of mismeasured outcomes; the estimates of the relative bias of the model were lower than those provided by the naive method that assumes perfect sensitivity and specificity. The proposed methodology is illustrated by a study related to mango tree lifetimes.
MATHEMATICS SUBJECT CLASSIFICATION:
Acknowledgments
We would like to express our gratitude for the careful refereeing, which indeed has led to substantial improvement of the article.
Funding
The first author's research was partially supported by the Programa Institucional de Auxílio á Pesquisa de Doutores Recém-Contratados/PRPq-UFMG. Grants from FAPEMIG, CNPq and CAPES also supported this research.