Patient-reported outcome (PRO) measures are increasingly collected as a means of measuring healthcare quality and value. The capability to predict such measures enables patient-provider shared decision making and the delivery of patient-centered care. However, due to their voluntary nature, PRO measures often suffer from a high missing rate, and the missingness may depend on many patient factors. Under such a complex missing mechanism, statistical inference of the parameters in prediction models for PRO measures is challenging, especially when flexible imputation models such as machine learning or nonparametric methods are used. Specifically, the slow convergence rate of the flexible imputation model may lead to non-negligible bias, and the traditional missing propensity, capable of removing such a bias, is hard to estimate due to the complex missing mechanism. To efficiently infer the parameters of interest, we propose to use an informative surrogate that can lead to a flexible imputation model lying in a low-dimensional subspace. To remove the bias due to the flexible imputation model, we identify a class of weighting functions as alternatives to the traditional propensity score and estimate the low-dimensional one within the identified function class. Based on the estimated low-dimensional weighting function, we construct a one-step debiased estimator without using any information of the true missing propensity. We establish the asymptotic normality of the one-step debiased estimator. Simulation and an application to real-world data demonstrate the superiority of the proposed method.

翻译：患者报告结局（PRO）测量作为衡量医疗质量与价值的手段日益普及。预测此类结局的能力有助于患者与医疗服务提供者共同决策及实施以患者为中心的照护。然而，由于其自愿性，PRO测量常面临高缺失率问题，且缺失机制可能依赖于多种患者因素。在此类复杂缺失机制下，基于PRO测量预测模型参数的统计推断具有挑战性，尤其当采用灵活插补模型（如机器学习或非参数方法）时。具体而言，灵活插补模型的慢收敛速度可能导致不可忽略的偏差，而传统上可消除此类偏差的缺失倾向性因复杂缺失机制而难以估计。为高效推断目标参数，我们提出利用信息性代理变量，使灵活插补模型位于低维子空间中。为消除灵活插补模型导致的偏差，我们识别出一类加权函数作为传统倾向性得分的替代，并在该函数类中估计低维加权函数。基于所估计的低维加权函数，我们构建了不依赖真实缺失倾向性信息的单步去偏估计量。我们建立了该单步去偏估计量的渐近正态性。模拟实验及真实数据应用证明了所提方法的优越性。