根据恶性肿瘤患者的长期随访数据，评估治疗效果需要仔细考虑竞争风险。常用的特定原因风险比（CHR）和次分布风险比（SHR）是相对指标，可能在比例风险假设和临床解释方面提出挑战。最近，限制平均损失时间（RMTL）被推荐作为更好的临床解释的补充措施。此外，对于流行病学和临床环境中的观察性研究数据，由于混杂因素的影响，协变量调整对于确定治疗的因果效应至关重要。我们基于逆概率加权方法调整协变量后构建RMTL估计量，并导出方差以根据大样本属性构建区间估计。我们使用模拟研究来研究该估计器在各种情况下的统计性能。此外，我们进一步考虑了治疗效果随时间的变化，构建了动态 RMTL 差异曲线以及曲线相应的置信带。模拟结果表明，调整后的 RMTL 估计量与未调整的 RMTL 相比表现出更小的偏差，并在所有场景。该方法应用于真实的宫颈癌患者数据，揭示了宫颈小细胞癌患者预后的改善。结果显示，手术的保护作用仅在前20个月内显着，但长期效果并不明显。放疗在 17 至 57 个月的随访期间显着改善了患者的预后，而放疗联合化疗在整个期间显着改善了患者的预后。我们提出了一种易于解释和实施的方法，用于评估观察性竞争风险中的治疗效果数据。© 2024。作者。

According to long-term follow-up data of malignant tumor patients, assessing treatment effects requires careful consideration of competing risks. The commonly used cause-specific hazard ratio (CHR) and sub-distribution hazard ratio (SHR) are relative indicators and may present challenges in terms of proportional hazards assumption and clinical interpretation. Recently, the restricted mean time lost (RMTL) has been recommended as a supplementary measure for better clinical interpretation. Moreover, for observational study data in epidemiological and clinical settings, due to the influence of confounding factors, covariate adjustment is crucial for determining the causal effect of treatment.We construct an RMTL estimator after adjusting for covariates based on the inverse probability weighting method, and derive the variance to construct interval estimates based on the large sample properties. We use simulation studies to study the statistical performance of this estimator in various scenarios. In addition, we further consider the changes in treatment effects over time, constructing a dynamic RMTL difference curve and corresponding confidence bands for the curve.The simulation results demonstrate that the adjusted RMTL estimator exhibits smaller biases compared with unadjusted RMTL and provides robust interval estimates in all scenarios. This method was applied to a real-world cervical cancer patient data, revealing improvements in the prognosis of patients with small cell carcinoma of the cervix. The results showed that the protective effect of surgery was significant only in the first 20 months, but the long-term effect was not obvious. Radiotherapy significantly improved patient outcomes during the follow-up period from 17 to 57 months, while radiotherapy combined with chemotherapy significantly improved patient outcomes throughout the entire period.We propose the approach that is easy to interpret and implement for assessing treatment effects in observational competing risk data.© 2024. The Author(s).