Department of Statistics and Clinical Research, NHS Blood and Transplant, Bristol, UK.
Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
Stat Med. 2024 Mar 15;43(6):1238-1255. doi: 10.1002/sim.10011. Epub 2024 Jan 22.
In clinical studies, multi-state model (MSM) analysis is often used to describe the sequence of events that patients experience, enabling better understanding of disease progression. A complicating factor in many MSM studies is that the exact event times may not be known. Motivated by a real dataset of patients who received stem cell transplants, we considered the setting in which some event times were exactly observed and some were missing. In our setting, there was little information about the time intervals in which the missing event times occurred and missingness depended on the event type, given the analysis model covariates. These additional challenges limited the usefulness of some missing data methods (maximum likelihood, complete case analysis, and inverse probability weighting). We show that multiple imputation (MI) of event times can perform well in this setting. MI is a flexible method that can be used with any complete data analysis model. Through an extensive simulation study, we show that MI by predictive mean matching (PMM), in which sampling is from a set of observed times without reliance on a specific parametric distribution, has little bias when event times are missing at random, conditional on the observed data. Applying PMM separately for each sub-group of patients with a different pathway through the MSM tends to further reduce bias and improve precision. We recommend MI using PMM methods when performing MSM analysis with Markov models and partially observed event times.
在临床研究中，多状态模型（MSM）分析常用于描述患者经历的事件序列，从而更好地了解疾病的进展。在许多 MSM 研究中，一个复杂的因素是确切的事件时间可能并不为人所知。受接受干细胞移植的患者真实数据集的启发，我们考虑了一些事件时间被准确观察到，而另一些则缺失的情况。在我们的设置中，关于缺失事件时间发生的时间间隔的信息很少，并且由于分析模型协变量的存在，缺失性取决于事件类型。这些额外的挑战限制了一些缺失数据方法（最大似然法、完全案例分析和逆概率加权法）的实用性。我们表明，事件时间的多次插补（MI）在这种情况下可以很好地发挥作用。MI 是一种灵活的方法，可以与任何完整数据分析模型一起使用。通过广泛的模拟研究，我们表明，当事件时间在随机缺失时，基于观察数据，预测均值匹配（PMM）的 MI 几乎没有偏差，其中抽样来自一组没有依赖特定参数分布的观察时间。为每个具有不同 MSM 途径的患者亚组分别应用 PMM 通常会进一步减少偏差并提高精度。当使用马尔可夫模型和部分观察到的事件时间进行 MSM 分析时，我们建议使用 PMM 方法进行 MI。
