In active surveillance of prostate cancer, cancer progression is interval-censored and the examination to detect progression is subject to misclassification, usually false negatives. Meanwhile, patients may initiate early treatment before progression detection, constituting a competing risk. We developed the Misclassification-Corrected Interval-censored Cause-specific Joint Model (MCICJM) to estimate the association between longitudinal biomarkers and cancer progression in this setting. The sensitivity of the examination is considered in the likelihood of this model via a parameter that may be set to a specific value if the sensitivity is known, or for which a prior distribution can be specified if the sensitivity is unknown. Our simulation results show that misspecification of the sensitivity parameter or ignoring it entirely impacts the model parameters, especially the parameter uncertainty and the baseline hazards. Moreover, specification of a prior distribution for the sensitivity parameter may reduce the risk of misspecification in settings where the exact sensitivity is unknown, but may cause identifiability issues. Thus, imposing restrictions on the baseline hazards is recommended. A trade-off between modelling with a sensitivity constant at the risk of misspecification and a sensitivity prior at the cost of flexibility needs to be decided.

翻译：在主动监测前列腺癌的过程中，癌症进展呈现区间删失特征，而用于检测进展的检查存在误分类（通常为假阴性）问题。同时，患者在检测到进展前可能启动早期治疗，构成竞争风险。我们提出了误分类校正的区间删失原因特异性联合模型（Misclassification-Corrected Interval-censored Cause-specific Joint Model, MCICJM），以估计该场景下纵向生物标志物与癌症进展之间的关联。该模型的似然函数通过一个参数考虑了检查敏感度：若敏感度已知，则该参数可设定为特定值；若敏感度未知，则可为其指定先验分布。模拟结果表明，错误设定敏感度参数或完全忽略该参数会影响模型参数，尤其是参数不确定性和基线风险函数。此外，在确切敏感度未知的情况下，为敏感度参数指定先验分布可降低误设定风险，但可能引发可识别性问题。因此，建议对基线风险函数施加约束。研究者需在以下两者间权衡：以误设定风险为代价使用恒定敏感度建模，或以灵活性损失为代价使用敏感度先验。