In attempt to advance the current practice for assessing and predicting the primary ovarian insufficiency (POI) risk in female childhood cancer survivors, we propose two estimating function based approaches for age-specific logistic regression. Both approaches adapt the inverse probability of censoring weighting (IPCW) strategy and yield consistent estimators with asymptotic normality. The first approach modifies the IPCW weights used by Im et al. (2023) to account for doubly censoring. The second approach extends the outcome weighted IPCW approach to use the information of the subjects censored before the analysis time. We consider variance estimation for the estimators and explore by simulation the two approaches implemented in the situations where the conditional right-censoring time distribution required in the IPCW weighs is unknown and approximated using the survival random forest approaches, stratified empirical distribution functions, or the estimator under the Cox proportional hazards model. The numerical studies indicate that the second approach is more efficient when right-censoring is relatively heavy, whereas the first approach is preferable when the right-censoring is light. We also observe that the performance of the two approaches heavily relies on the estimation of censoring distribution in our simulation settings. The POI data from a childhood cancer survivor study are employed throughout the paper for motivation and illustration. Our data analysis provides new insight into understanding the POI risk among cancer survivors.

翻译：为推进女性儿童期癌症幸存者原发性卵巢功能不全（POI）风险评估与预测的现行实践，我们提出两种基于估计函数的年龄特异性逻辑回归方法。两种方法均采用逆删失概率加权（IPCW）策略，并生成具有渐近正态性的一致估计量。第一种方法修正了Im等人（2023）使用的IPCW权重，以处理双删失情况。第二种方法扩展了结果加权IPCW方法，利用分析时间之前被删失受试者的信息。我们考虑了估计量的方差估计，并通过模拟研究探讨了两种方法在以下情况下的实施：当IPCW权重计算所需的条件右删失时间分布未知时，采用生存随机森林方法、分层经验分布函数或Cox比例风险模型下的估计量进行近似。数值研究表明，当右删失相对严重时，第二种方法效率更高；而右删失较轻时，第一种方法更优。我们还观察到，在我们的模拟设定中，两种方法的性能高度依赖于删失分布的估计。本文全程采用儿童期癌症幸存者研究的POI数据进行动机说明与示例分析。我们的数据分析为理解癌症幸存者POI风险提供了新见解。