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Analyzing Interval Censored Data Using Different Parametric Model
Abstract
In this publication, we analyze IC (interval censored) data with the aim of estimating the actual occurrence time of an targeted event of interest, utilizing two consecutive known time points, Li and Ri. Estimation was performed using both Frequentist, or we often call it classical, and Bayesian approaches. To determine the exact time of the targeted event occurrence, different parametric models were evaluated. Following this estimation, traditional survival analysis was conducted to compare our proposed methodologies against established conventional methods for estimating event times. The proposed methods demonstrated promising results in both simulation studies and with real-life data, indicating their
utility as a sensitivity analysis to assess the robustness of conventional methods. Our approach effectively mitigates the potential for time bias that arises from assuming the reported event date is the true event occurrence date.
Keywords
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