Target trial emulation with multi-state model analysis to assess treatment effectiveness using clinical COVID-19 data.
Target trial emulation with multi-state model analysis to assess treatment effectiveness using clinical COVID-19 data.
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DOI：
10.1186/s12874-023-02001-8
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发表时间：
2023-09-02
影响因子：
4
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中科院分区：
文献类型：
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真实世界的观察数据是2019冠状病毒病（COVID-19）住院患者治疗有效性的重要证据来源。然而，基于纵向数据评价治疗有效性的观察性研究往往容易出现方法学偏倚，如永恒时间偏倚、混杂偏倚和竞争风险。对于示例性目标试验模拟，我们使用了一个中心的COVID-19住院患者队列（n = 501）。我们描述了评估单剂量治疗有效性的方法，使用真实世界数据模拟试验，并起草了一份描述主要成分的假设研究方案。为了避免不朽的时间和时间固定的混杂偏倚，我们应用了克隆-删失-权重技术。我们设定了5天的宽限期，作为可以开始治疗的时间。我们使用截尾权重的逆概率来解释人工截尾引入的选择偏倚。为了估计治疗效果，我们采用了多状态模型方法。我们考虑了一个有五个状态的多状态模型。主要终点定义为临床严重程度状态，在第30天通过5分顺序量表进行评估。使用比例优势模型计算治疗组和标准治疗组之间的差异，并显示为优势比。此外，还列出了每个治疗组的加权原因特异性危害和转移概率。我们的研究表明，采用多状态模型分析的试验模拟是解决观察数据局限性、评价临床异质性院内死亡和出院存活终点的治疗效果以及考虑入住ICU的中间状态的合适方法。多状态模型分析允许我们使用堆叠概率图来总结结果，从而更容易解释结果。将模拟靶向试验方法扩展到多状态模型分析，通过获得竞争事件的信息来补充治疗有效性分析。结合两种方法提供了一种解决不朽的时间偏差，混杂偏差和竞争风险事件的选择。这种方法可以为决策提供额外的见解，特别是当随机对照试验（RCT）的数据不可用时。在线版本包含补充材料，可通过10.1186/s12874-023-02001-8获得。
Real-world observational data are an important source of evidence on the treatment effectiveness for patients hospitalized with coronavirus disease 2019 (COVID-19). However, observational studies evaluating treatment effectiveness based on longitudinal data are often prone to methodological biases such as immortal time bias, confounding bias, and competing risks. For exemplary target trial emulation, we used a cohort of patients hospitalized with COVID-19 (n = 501) in a single centre. We described the methodology for evaluating the effectiveness of a single-dose treatment, emulated a trial using real-world data, and drafted a hypothetical study protocol describing the main components. To avoid immortal time and time-fixed confounding biases, we applied the clone-censor-weight technique. We set a 5-day grace period as a period of time when treatment could be initiated. We used the inverse probability of censoring weights to account for the selection bias introduced by artificial censoring. To estimate the treatment effects, we took the multi-state model approach. We considered a multi-state model with five states. The primary endpoint was defined as clinical severity status, assessed by a 5-point ordinal scale on day 30. Differences between the treatment group and standard of care treatment group were calculated using a proportional odds model and shown as odds ratios. Additionally, the weighted cause-specific hazards and transition probabilities for each treatment arm were presented. Our study demonstrates that trial emulation with a multi-state model analysis is a suitable approach to address observational data limitations, evaluate treatment effects on clinically heterogeneous in-hospital death and discharge alive endpoints, and consider the intermediate state of admission to ICU. The multi-state model analysis allows us to summarize results using stacked probability plots that make it easier to interpret results. Extending the emulated target trial approach to multi-state model analysis complements treatment effectiveness analysis by gaining information on competing events. Combining two methodologies offers an option to address immortal time bias, confounding bias, and competing risk events. This methodological approach can provide additional insight for decision-making, particularly when data from randomized controlled trials (RCTs) are unavailable. The online version contains supplementary material available at 10.1186/s12874-023-02001-8.
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DOI：
10.3390/life13030777
发表时间：
2023-03-13
期刊：
Life (Basel, Switzerland)
影响因子：
--
作者：
Martinuka O;Cube MV;Hazard D;Marateb HR;Mansourian M;Sami R;Hajian MR;Ebrahimi S;Wolkewitz M
通讯作者：
Wolkewitz M
影响因子：
4.5
作者：
Gran JM;Lie SA;Øyeflaten I;Borgan Ø;Aalen OO
通讯作者：
Aalen OO
影响因子：
7.7
作者：
Graffeo, Nathalie;Latouche, Aurelien;Chevret, Sylvie
通讯作者：
Chevret, Sylvie
影响因子：
105.7
作者：
Coemans, Maarten;Verbeke, Geert;Naesens, Maarten
通讯作者：
Naesens, Maarten
影响因子：
5
作者：
Cole, Stephen R.;Hernan, Miguel A.
通讯作者：
Hernan, Miguel A.