An illness-death multistate model to implement delta adjustment and reference-based imputation with time-to-event endpoints.

IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY
Pharmaceutical Statistics Pub Date : 2024-03-01 Epub Date: 2023-11-08 DOI:10.1002/pst.2348
Alberto García-Hernandez, Teresa Pérez, María Del Carmen Pardo, Dimitris Rizopoulos
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引用次数: 0

Abstract

With a treatment policy strategy, therapies are evaluated regardless of the disturbance caused by intercurrent events (ICEs). Implementing this estimand is challenging if subjects are not followed up after the ICE. This circumstance can be dealt with using delta adjustment (DA) or reference-based (RB) imputation. In the survival field, DA and RB imputation have been researched so far using multiple imputation (MI). Here, we present a fully analytical solution. We use the illness-death multistate model with the following transitions: (a) from the initial state to the event of interest, (b) from the initial state to the ICE, and (c) from the ICE to the event. We estimate the intensity function of transitions (a) and (b) using flexible parametric survival models. Transition (c) is assumed unobserved but identifiable using DA or RB imputation assumptions. Various rules have been considered: no ICE effect, DA under proportional hazards (PH) or additive hazards (AH), jump to reference (J2R), and (either PH or AH) copy increment from reference. We obtain the marginal survival curve of interest by calculating, via numerical integration, the probability of transitioning from the initial state to the event of interest regardless of having passed or not by the ICE state. We use the delta method to obtain standard errors (SEs). Finally, we quantify the performance of the proposed estimator through simulations and compare it against MI. Our analytical solution is more efficient than MI and avoids SE misestimation-a known phenomenon associated with Rubin's variance equation.

一个疾病死亡多状态模型,用于实现基于时间到事件终点的德尔塔调整和参考插补。
根据治疗策略,无论并发事件(ICEs)引起的干扰如何,都会对治疗进行评估。如果受试者在ICE后没有得到随访,那么实施这一评估要求是具有挑战性的。这种情况可以使用德尔塔调整(DA)或基于参考(RB)的插补来处理。在生存领域,迄今为止,已经使用多重插补(MI)对DA和RB插补进行了研究。在这里,我们提出了一个完全分析的解决方案。我们使用具有以下转变的疾病-死亡多状态模型:(a)从初始状态到感兴趣的事件,(b)从初始态到ICE,以及(c)从ICE到事件。我们使用灵活的参数生存模型来估计跃迁(a)和(b)的强度函数。假设过渡(c)未观察到,但使用DA或RB插补假设可识别。已经考虑了各种规则:无ICE效应、比例危险(PH)或加性危险(AH)下的DA、跳转到参考(J2R)以及(PH或AH)从参考复制增量。我们通过数值积分计算从初始状态转换到感兴趣事件的概率,获得感兴趣的边际生存曲线,无论是否通过ICE状态。我们使用delta方法来获得标准误差(SE)。最后,我们通过模拟量化了所提出的估计器的性能,并将其与MI进行了比较。我们的分析解决方案比MI更有效,避免了SE错误估计——这是与鲁宾方差方程相关的已知现象。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Pharmaceutical Statistics
Pharmaceutical Statistics 医学-统计学与概率论
CiteScore
2.70
自引率
6.70%
发文量
90
审稿时长
6-12 weeks
期刊介绍: Pharmaceutical Statistics is an industry-led initiative, tackling real problems in statistical applications. The Journal publishes papers that share experiences in the practical application of statistics within the pharmaceutical industry. It covers all aspects of pharmaceutical statistical applications from discovery, through pre-clinical development, clinical development, post-marketing surveillance, consumer health, production, epidemiology, and health economics. The Journal is both international and multidisciplinary. It includes high quality practical papers, case studies and review papers.
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