Despite the need for sensitivity analysis to nonignorable missingness in intensive longitudinal data (ILD), such analysis is greatly hindered by novel ILD features, such as large data volume and complex nonmonotonic missing‐data patterns. Likelihood of alternative models permitting nonignorable missingness often involves very high‐dimensional integrals, causing curse of dimensionality and rendering solutions computationally prohibitive to obtain. We aim to overcome this challenge by developing a computationally feasible method, nonlinear indexes of local sensitivity to nonignorability (NISNI). We use linear mixed effects models for the incomplete outcome and covariates. We use Markov multinomial models to describe complex missing‐data patterns and mechanisms in ILD, thereby permitting missingness probabilities to depend directly on missing data. Using a second‐order Taylor series to approximate likelihood under nonignorability, we develop formulas and closed‐form expressions for NISNI. Our approach permits the outcome and covariates to be missing simultaneously, as is often the case in ILD, and can capture U‐shaped impact of nonignorability in the neighborhood of the missing at random model without fitting alternative models or evaluating integrals. We evaluate performance of this method using simulated data and real ILD collected by the ecological momentary assessment method.

中文翻译：

---

解决密集纵向数据中高维不可忽略缺失数据的一种易于处理的方法。

尽管需要对密集纵向数据（ILD）中不可忽略的缺失进行敏感性分析，但这种新的ILD功能（例如大数据量和复杂的非单调缺失数据模式）极大地阻碍了这种分析。允许不可忽略的缺失的替代模型的可能性通常涉及非常高维的积分，从而导致维数的诅咒并使得解决方案在计算上难以获得。我们旨在通过开发一种计算上可行的方法来克服这一挑战，该方法是对不可燃性局部敏感的非线性指标（NISNI）。对于不完全的结果和协变量，我们使用线性混合效应模型。我们使用马尔可夫多项式模型来描述ILD中的复杂缺失数据模式和机制，从而允许缺失概率直接取决于缺失数据。使用二阶泰勒级数逼近不可忽略性下的可能性，我们开发了NISNI的公式和闭式表达式。我们的方法允许结果和协变量同时缺失，这在ILD中很常见，且无需拟合替代模型或评估积分，就可以捕获随机模型缺失部分附近不可燃性的U形影响。我们使用模拟数据和通过生态瞬时评估方法收集的实际ILD评估该方法的性能。