In longitudinal research, interest often centers on individual trajectories of change over time. When there is missing data, a concern is whether data are systematically missing as a function of the individual trajectories. Such a missing data process, termed random coefficient-dependent missingness, is statistically nonignorable and can bias parameter estimates obtained from conventional growth models that assume missing data are missing at random. This article describes a shared parameter mixture model (SPMM) for testing the sensitivity of growth model parameter estimates to a random coefficient-dependent missingness mechanism. Simulations show that the SPMM recovers trajectory estimates as well as or better than a standard growth model across a range of missing data conditions. The article concludes with practical advice for longitudinal data analysts.

中文翻译：

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对存在非随机缺失数据的变化建模：评估共享参数混合模型

在纵向研究中，兴趣通常集中在个人随时间变化的轨迹上。当存在缺失数据时，一个问题是数据是否作为单个轨迹的函数系统地缺失。这种缺失的数据过程，称为随机系数相关缺失，在统计上是不可忽略的，并且会使从假设缺失数据随机缺失的传统增长模型中获得的参数估计产生偏差。本文描述了一个共享参数混合模型 (SPMM)，用于测试增长模型参数估计对随机系数相关缺失机制的敏感性。模拟表明，SPMM 在一系列缺失数据条件下恢复的轨迹估计与标准增长模型一样好或更好。