Case-I interval-censored (current status) data from multistate systems are often encountered in cancer and other epidemiological studies. In this article, we focus on the problem of estimating state entry distribution and occupation probabilities, contingent on a preceding state occupation. This endeavor is particularly complex owing to the inherent challenge of the unavailability of directly observed counts of individuals at risk of transitioning from a state, due to the cross-sectional nature of the data. We propose two nonparametric approaches, one using the fractional at-risk set approach recently adopted in the right-censoring framework and the other a new estimator based on the ratio of marginal state occupation probabilities. Both estimation approaches utilize innovative applications of concepts from the competing risks paradigm. The finite-sample behavior of the proposed estimators is studied via extensive simulation studies where we show that the estimators based on severely censored current status data have good performance when compared with those based on complete data. We demonstrate the application of the two methods to analyze data from patients diagnosed with breast cancer.

翻译：在癌症及其他流行病学研究中，多状态系统的第一类区间删失（当前状态）数据较为常见。本文聚焦于估计在已知先前状态占据的情况下，后续状态进入分布及占据概率的问题。由于数据呈横截面特性，无法直接观测到处于风险转移状态个体的计数，这一研究挑战尤为突出。我们提出两种非参数方法：其一采用最近在右删失框架中应用的分数风险集方法，其二构建基于边际状态占据概率比值的新型估计量。两种估计方法均创新性地运用了竞争风险范式的相关概念。通过大规模模拟研究检验所提估计量的有限样本表现，发现基于严重删失当前状态数据的估计器相较于完整数据估计器具有良好性能。我们通过乳腺癌患者诊断数据的分析实践，展示了两种方法的应用价值。