评估纵向临床轨迹的插补方法：头颈癌治疗中的毒性反应
Evaluating imputation methods for longitudinal clinical trajectories: Toxicities in head and neck cancer treatment.
文献信息
| PMID | 42858855 |
|---|---|
| 原文 | 在 PubMed 查看原文 ↗ |
| 发表日期 | 2026 |
| 作者 | Luuk van der Hoek |
| 作者单位 | Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands. Electronic address: l.van.der.hoek02@umcg.nl. |
| 期刊 | Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology |
| SCI 分区 | Q1 |
| IF | 5.8 |
| 研究类型 | AI/ML · 临床 |
| 所属专科 | 耳科 |
中文摘要
目的： 纵向收集的医学数据常受缺失值影响，因此减轻缺失值的方法具有重要价值。本研究评估了九种纵向数据插补方法，使用头颈癌放疗后多个时间点测量的毒性评分。
方法： 针对四种毒性（口干、吞咽困难、味觉改变和黏稠唾液），收集了16个时间点（治疗前1个、治疗期间7个、治疗后8个）的毒性评分，共纳入1442例患者，平均约有11.6个可用时间点。插补方法从简单方法到基于深度学习的方法不等。采用系统性移除策略评估各方法，从而能够比较真实值与插补值。分别进行了患者层面、队列层面和下游模型评估，以评价患者纳入标准、训练数据需求和建模效果。
结果： 总体插补质量在各方法之间差异极小，但简单方法表现较差。患者层面评估发现，当患者可用时间点少于50%时，插补开始使结果变差。队列层面评估显示，先进方法在患者数量较少（150例患者）时即可达到良好性能，但受平均时间点可用性低的影响更为严重。下游模型评估显示，除简单方法导致模型系数不同外，各方法之间差异可忽略不计。
结论： 研究发现最好避免使用简单插补方法。此外，在将插补应用于纵向数据时，实践者应审慎考虑某些患者的纳入，以及其数据集是否允许使用更先进的插补方法。
英文摘要
PURPOSE: Longitudinally collected medical data is often afflicted by missingness, making methods mitigating missingness valuable. This study evaluated nine imputation methods for longitudinal data, using toxicity scores measured at multiple timepoints resulting from head and neck cancer treatment with radiotherapy.
METHODS AND MATERIALS: For four toxicities (xerostomia, dysphagia, taste alteration, and sticky saliva), toxicity scores at 16 timepoints (1 prior, 7 during, and 8 after treatment) were collected, resulting in 1442 patients with on average ∼11.6 available timepoints. Imputation methods ranged from simple to deep learning-based. Methods were evaluated using a systematic removal strategy enabling comparison of true and imputed values. Patient-level, cohort-level and downstream model evaluation was done, evaluating inclusion criteria for patients, training data requirement and modelling effect respectively.
RESULTS: General imputation quality showed minimal differences between methods, except simple methods, which performed worse. For patient-level evaluation, it was found that when patients had <50% of timepoints available, imputation started to worsen. Cohort-level evaluation showed that advanced methods could reach good performances at low patient counts (150 patients) but were more severely affected by low average timepoint availability. Downstream model evaluation showed negligible differences between methods, other than simple methods which resulted in different model coefficients.
CONCLUSIONS: It was found that it is best to avoid simple imputation methods. Furthermore, when applying imputation to longitudinal data practitioners should be conscientious about inclusion of certain patients and if their dataset permits the use of more advanced imputation methods.