Early intervention in neurodegenerative diseases requires identifying periods before diagnosis when decline is rapid enough to detect whether a therapy is slowing progression. Since rapid decline typically occurs close to diagnosis, identifying these periods requires knowing each patient's time of diagnosis. Yet many patients exit studies before diagnosis, making time of diagnosis right-censored by time of study exit -- creating a right-censored covariate problem when estimating decline. Existing estimators either assume noninformative covariate censoring, where time of study exit is independent of time of diagnosis, or allow informative covariate censoring, but require correctly specifying how these times are related. We developed SPIRE (Semi-Parametric Informative Right-censored covariate Estimator), a super doubly robust estimator that remains consistent without correctly specifying densities governing time of diagnosis or time of study exit. Typical double robustness requires at least one density to be correct; SPIRE requires neither. When both densities are correctly specified, SPIRE achieves semiparametric efficiency. We also developed a test for detecting informative covariate censoring. Simulations with 85% right-censoring demonstrated SPIRE's robustness, efficiency and reliable detection of informative covariate censoring. Applied to Huntington disease data, SPIRE handled informative covariate censoring appropriately and remained consistent regardless of density specification, providing a reliable tool for early intervention.

翻译：神经退行性疾病的早期干预需要在诊断前识别出衰退速度足够快、足以检测疗法是否减缓病程的时期。由于快速衰退通常发生在接近诊断时，识别这些时期需要知晓每位患者的诊断时间。然而，许多患者在诊断前退出研究，使得诊断时间被研究退出时间右删失——这在估计衰退时构成了右删失协变量问题。现有估计器要么假设非信息性协变量删失（即研究退出时间与诊断时间独立），要么允许信息性协变量删失，但需要正确指定这两个时间之间的关联方式。我们开发了SPIRE（半参数信息性右删失协变量估计器），这是一种超双重稳健估计器，即使未正确指定诊断时间或研究退出时间的密度函数，仍能保持一致性。典型的双重稳健性要求至少一个密度函数正确；而SPIRE两者皆不需要。当两个密度函数均正确指定时，SPIRE达到半参数效率。我们还开发了一种检测信息性协变量删失的检验方法。在85%右删失率的模拟中，SPIRE展现了其稳健性、高效性以及对信息性协变量删失的可靠检测能力。应用于亨廷顿病数据时，SPIRE能恰当处理信息性协变量删失，且无论密度函数设定如何均保持一致性，为早期干预提供了可靠工具。