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The effect of treatment delay on time‐to‐recovery in the presence of unobserved heterogeneity
Biometrical Journal ( IF 1.7 ) Pub Date : 2020-01-20 , DOI: 10.1002/bimj.201900131
Nan van Geloven 1 , Theodor A Balan 1 , Hein Putter 1 , Saskia le Cessie 1, 2
Affiliation  

Abstract We study the effect of delaying treatment in the presence of (unobserved) heterogeneity. In a homogeneous population and assuming a proportional treatment effect, a treatment delay period will result in notably lower cumulative recovery percentages. We show in theoretical scenarios using frailty models that if the population is heterogeneous, the effect of a delay period is much smaller. This can be explained by the selection process that is induced by the frailty. Patient groups that start treatment later have already undergone more selection. The marginal hazard ratio for the treatment will act differently in such a more homogeneous patient group. We further discuss modeling approaches for estimating the effect of treatment delay in the presence of heterogeneity, and compare their performance in a simulation study. The conventional Cox model that fails to account for heterogeneity overestimates the effect of treatment delay. Including interaction terms between treatment and starting time of treatment or between treatment and follow up time gave no improvement. Estimating a frailty term can improve the estimation, but is sensitive to misspecification of the frailty distribution. Therefore, multiple frailty distributions should be used and the results should be compared using the Akaike Information Criterion. Non‐parametric estimation of the cumulative recovery percentages can be considered if the dataset contains sufficient long term follow up for each of the delay strategies. The methods are demonstrated on a motivating application evaluating the effect of delaying the start of treatment with assisted reproductive techniques on time‐to‐pregnancy in couples with unexplained subfertility.

中文翻译:

在存在未观察到的异质性的情况下,延迟治疗对恢复时间的影响

摘要 我们研究了存在(未观察到的)异质性时延迟治疗的影响。在同质人群中并假设治疗效果成比例,治疗延迟期将导致显着降低累积恢复百分比。我们在使用衰弱模型的理论场景中表明,如果人口是异质的,延迟期的影响要小得多。这可以通过由脆弱引起的选择过程来解释。较晚开始治疗的患者群体已经经历了更多的选择。在这样一个更同质的患者组中,治疗的边际风险比会有所不同。我们进一步讨论了在存在异质性的情况下估计治疗延迟影响的建模方法,并在模拟研究中比较了它们的性能。未能考虑异质性的传统 Cox 模型高估了治疗延迟的影响。包括治疗和治疗开始时间之间或治疗和随访时间之间的相互作用项没有得到改善。估计脆弱项可以改进估计,但对脆弱性分布的错误指定很敏感。因此,应使用多个衰弱分布,并应使用 Akaike 信息准则比较结果。如果数据集包含每个延迟策略的足够长期随访,则可以考虑对累积恢复百分比进行非参数估计。
更新日期:2020-01-20
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