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

IF 3.9 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Haoning Shen, Chengfeng Zhang, Yu Song, Zhiheng Huang, Yanjie Wang, Yawen Hou, Zheng Chen
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引用次数: 0

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.

在观察性竞争风险数据中使用调整后的受限平均损失时间评估治疗效果。
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。常用的特异性病因危险比(CHR)和亚分布危险比(SHR)是相对指标,在比例危险假设和临床解释方面可能存在挑战。最近,为了更好地进行临床解释,推荐使用限制性平均损失时间(RMTL)作为补充指标。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:我们根据反概率加权法构建了调整协变量后的 RMTL 估计器,并根据大样本特性推导出方差,从而构建区间估计值。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,我们还进一步考虑了治疗效果随时间的变化,构建了一条动态 RMTL 差异曲线以及曲线的相应置信区间:模拟结果表明,与未调整的 RMTL 相比,调整后的 RMTL 估计器显示出较小的偏差,并且在所有情况下都能提供稳健的区间估计值。该方法被应用于真实世界的宫颈癌患者数据,揭示了宫颈小细胞癌患者预后的改善情况。结果显示,手术的保护作用仅在前 20 个月显著,但长期效果并不明显。放疗在 17 至 57 个月的随访期间明显改善了患者的预后,而放疗联合化疗则在整个随访期间明显改善了患者的预后: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数据中的治疗效果。
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来源期刊
BMC Medical Research Methodology
BMC Medical Research Methodology 医学-卫生保健
CiteScore
6.50
自引率
2.50%
发文量
298
审稿时长
3-8 weeks
期刊介绍: BMC Medical Research Methodology is an open access journal publishing original peer-reviewed research articles in methodological approaches to healthcare research. Articles on the methodology of epidemiological research, clinical trials and meta-analysis/systematic review are particularly encouraged, as are empirical studies of the associations between choice of methodology and study outcomes. BMC Medical Research Methodology does not aim to publish articles describing scientific methods or techniques: these should be directed to the BMC journal covering the relevant biomedical subject area.
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