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Accounting for time‐dependent treatment use when developing a prognostic model from observational data: A review of methods
Statistica Neerlandica ( IF 1.5 ) Pub Date : 2019-11-07 , DOI: 10.1111/stan.12193
Romin Pajouheshnia 1, 2 , Noah A. Schuster 1 , Rolf H. H. Groenwold 3 , Frans H. Rutten 1 , Karel G. M. Moons 1 , Linda M. Peelen 1
Affiliation  

Failure to account for time-dependent treatment use when developing a prognostic model can result in biased future predictions. We reviewed currently available methods to account for treatment use when developing a prognostic model. First, we defined the estimands targeted by each method and examined their mechanisms of action with directed acyclic graphs (DAGs). Next, methods were implemented in data from 1,906 patients; 325 received selective β-blockers (SBBs) during follow-up. We demonstrated seven Cox regression modeling strategies: (a) ignoring SBB treatment; (b) excluding SBB users or (c) censoring them when treated; (d) inverse probability of treatment weighting after censoring (IPCW), including SBB treatment as (e) a binary or (f) a time-dependent covariate; and (g) marginal structural modeling (MSM). Using DAGs, we demonstrated IPCW and MSM have the best properties and target a similar estimand. In the case study, compared to (a), approaches (b) and (e) provided predictions that were 1% and 2% higher on average. Performance (c-statistic, Brier score, calibration slope) varied minimally between approaches. Our review of methods confirmed that ignoring treatment is theoretically inferior, but differences between the prediction models obtained using different methods can be modest in practice. Future simulation studies and applications are needed to assess the value of applying IPCW or MSM to adjust for treatments in different treatment and disease settings.

中文翻译:

在根据观察数据开发预后模型时考虑时间依赖性治疗的使用:方法回顾

在开发预后模型时未能考虑时间依赖的治疗使用可能会导致未来预测有偏差。我们回顾了目前可用的方法来说明在开发预后模型时使用的治疗方法。首先,我们定义了每种方法所针对的估计量,并使用有向无环图 (DAG) 检查了它们的作用机制。接下来,在 1,906 名患者的数据中实施了方法;325 人在随访期间接受了选择性 β 受体阻滞剂 (SBB)。我们展示了七种 Cox 回归建模策略:(a) 忽略 SBB 处理;(b) 排除 SBB 用户或 (c) 在处理时对其进行审查;(d) 审查后处理加权的逆概率 (IPCW),包括 SBB 处理作为 (e) 二元或 (f) 时间相关协变量;(g) 边际结构建模 (MSM)。使用 DAG,我们证明了 IPCW 和 MSM 具有最好的特性并针对类似的估计量。在案例研究中,与 (a) 相比,方法 (b) 和 (e) 提供的预测平均高出 1% 和 2%。不同方法之间的性能(c 统计量、Brier 分数、校准斜率)差异很小。我们对方法的回顾证实,忽略治疗在理论上是低劣的,但在实践中使用不同方法获得的预测模型之间的差异可能不大。需要未来的模拟研究和应用来评估应用 IPCW 或 MSM 来调整不同治疗和疾病环境中的治疗的价值。Brier 分数、校准斜率)在方法之间的差异很小。我们对方法的回顾证实,忽略治疗在理论上是低劣的,但在实践中使用不同方法获得的预测模型之间的差异可能不大。需要未来的模拟研究和应用来评估应用 IPCW 或 MSM 来调整不同治疗和疾病环境中的治疗的价值。Brier 分数、校准斜率)在方法之间的差异很小。我们对方法的回顾证实,忽略治疗在理论上是低劣的,但在实践中使用不同方法获得的预测模型之间的差异可能不大。需要未来的模拟研究和应用来评估应用 IPCW 或 MSM 来调整不同治疗和疾病环境中的治疗的价值。
更新日期:2019-11-07
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