在观察性竞争风险数据中，使用调整后的受限平均时间损失评估治疗效果。

Assessing treatment effects with adjusted restricted mean time lost in observational competing risks data.

机构信息

Department of Biostatistics, School of Public Health (Guangdong Provincial Key Laboratory of Tropical Disease Research), Southern Medical University, No. 1023, South Shatai Road, Guangzhou, China.

Department of Statistics and Data Science, School of Economics, Jinan University, Guangzhou, China.

出版信息

BMC Med Res Methodol. 2024 Aug 26;24(1):186. doi: 10.1186/s12874-024-02303-5.

DOI:10.1186/s12874-024-02303-5

PMID:39187791

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11346024/

Abstract

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

We propose the approach that is easy to interpret and implement for assessing treatment effects in observational competing risk data.

摘要

背景

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。常用的病因特异性风险比（CHR）和亚分布风险比（SHR）是相对指标，在比例风险假设和临床解释方面可能存在挑战。最近，受限平均生存时间（RMTL）已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。

方法

我们基于逆概率加权法构建了一个调整协变量后的 RMTL 估计量，并推导了方差以基于大样本性质构建区间估计。我们使用模拟研究来研究这个估计器在各种场景下的统计性能。此外，我们进一步考虑了治疗效果随时间的变化，构建了一个动态 RMTL 差异曲线和相应的曲线置信带。

结果

模拟结果表明，调整后的 RMTL 估计量与未经调整的 RMTL 相比具有较小的偏差，并且在所有场景下都提供了稳健的区间估计。该方法应用于真实的宫颈癌患者数据，揭示了小细胞宫颈癌患者预后的改善。结果表明，手术的保护作用仅在最初的 20 个月内显著，而长期效果不明显。放疗在 17 至 57 个月的随访期间显著改善了患者的预后，而放疗联合化疗则在整个期间显著改善了患者的预后。