Rethinking intercurrent events in defining estimands for tuberculosis trials.
Rethinking intercurrent events in defining estimands for tuberculosis trials.
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DOI：
10.1177/17407745221103853
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发表时间：
2022-10
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--
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中科院分区：
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结核病仍然是全球传染病致死的主要原因之一。结果定义的选择和处理随机化后发生事件的方法都可以改变所估计的治疗效果，但这些通常是不一致的描述，从而阻碍了试验之间的清晰解释和比较。从ICH E9（R1）附录对估计的定义开始，我们利用我们开展大型III期结核病治疗试验的经验和我们对估计框架的理解，确定在主要结果定义中如何处理不同事件类型的关键决策，以及在做出此类决策时应考虑的要点。一个关键问题是处理并发（即随机化后）事件（ICEs），这些事件会影响对预期最终结果的解释或排除测量。我们考虑了常见的ice，包括治疗改变和延长治疗，对随机治疗的依从性差，与原始感染不同的新结核菌株的再次感染以及死亡。我们以两项已完成的结核病试验（REMoxTB和STREAM第一阶段）为例进行说明。这些试验检验了新的结核病治疗方案与对照方案的非劣效性。主要结局是一个二元复合终点，“有利”或“不利”，由几个组成部分组成。我们建议在处理上述ICE和随访损失（随机化后事件，本身不是ICE）方面进行以下改进。首先，分配制度的改变不一定被视为不利的结果；从患者的角度来看，与改变治疗方案相关的潜在危害应该直接量化。其次，使用每个方案分析来处理随机治疗依从性差的问题不一定针对明确的评估；相反，最好是开发出与方案设置更相关的方法来评估治疗效果。第三，可以采用不同的策略处理新结核菌株的再次感染，这取决于感兴趣的结果是否能够从感染任何结核菌株或特别是呈现结核菌株中获得培养阴性。第四，在可能的情况下，可将死亡分为与结核病有关的死亡和与非结核病有关的死亡，并采用适当的战略加以处理。最后，虽然一些随访失败会导致早期治疗中断，但在试验结束前随访失败的患者不应总是被归类为具有不利结果。相反，随访失败应该与未完成治疗分开，后者是ICE，可能被认为是不利的结果。评估框架澄清了结核病试验中的许多问题，但也要求试验人员证明并改进其结果定义。未来的试验者在确定结果时应考虑上述所有要点。
Tuberculosis remains one of the leading causes of death from an infectious disease globally. Both choices of outcome definitions and approaches to handling events happening post-randomisation can change the treatment effect being estimated, but these are often inconsistently described, thus inhibiting clear interpretation and comparison across trials. Starting from the ICH E9(R1) addendum’s definition of an estimand, we use our experience of conducting large Phase III tuberculosis treatment trials and our understanding of the estimand framework to identify the key decisions regarding how different event types are handled in the primary outcome definition, and the important points that should be considered in making such decisions. A key issue is the handling of intercurrent (i.e. post-randomisation) events (ICEs) which affect interpretation of or preclude measurement of the intended final outcome. We consider common ICEs including treatment changes and treatment extension, poor adherence to randomised treatment, re-infection with a new strain of tuberculosis which is different from the original infection, and death. We use two completed tuberculosis trials (REMoxTB and STREAM Stage 1) as illustrative examples. These trials tested non-inferiority of new tuberculosis treatment regimens versus a control regimen. The primary outcome was a binary composite endpoint, ‘favourable’ or ‘unfavourable’, which was constructed from several components. We propose the following improvements in handling the above-mentioned ICEs and loss to follow-up (a post-randomisation event that is not in itself an ICE). First, changes to allocated regimens should not necessarily be viewed as an unfavourable outcome; from the patient perspective, the potential harms associated with a change in the regimen should instead be directly quantified. Second, handling poor adherence to randomised treatment using a per-protocol analysis does not necessarily target a clear estimand; instead, it would be desirable to develop ways to estimate the treatment effects more relevant to programmatic settings. Third, re-infection with a new strain of tuberculosis could be handled with different strategies, depending on whether the outcome of interest is the ability to attain culture negativity from infection with any strain of tuberculosis, or specifically the presenting strain of tuberculosis. Fourth, where possible, death could be separated into tuberculosis-related and non-tuberculosis-related and handled using appropriate strategies. Finally, although some losses to follow-up would result in early treatment discontinuation, patients lost to follow-up before the end of the trial should not always be classified as having an unfavourable outcome. Instead, loss to follow-up should be separated from not completing the treatment, which is an ICE and may be considered as an unfavourable outcome. The estimand framework clarifies many issues in tuberculosis trials but also challenges trialists to justify and improve their outcome definitions. Future trialists should consider all the above points in defining their outcomes.
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影响因子：
9.3
作者：
Phillips PPJ;Van Deun A;Ahmed S;Goodall RL;Meredith SK;Conradie F;Chiang CY;Rusen ID;Nunn AJ
通讯作者：
Nunn AJ
影响因子：
105.7
作者：
White, Ian R.;Horton, Nicholas J.;Pocock, Stuart J.
通讯作者：
Pocock, Stuart J.
影响因子：
15.8
作者：
Phillips, Patrick P. J.;Mitnick, Carole D.;Nunn, Andrew J.
通讯作者：
Nunn, Andrew J.
影响因子：
2.5
作者：
Nunn AJ;Rusen ID;Van Deun A;Torrea G;Phillips PP;Chiang CY;Squire SB;Madan J;Meredith SK
通讯作者：
Meredith SK
影响因子：
1.5
作者：
Ratitch, Bohdana;Goel, Niti;O'Kelly, Michael
通讯作者：
O'Kelly, Michael