Adjustment for treatment changes in epilepsy trials: A comparison of causal methods for time-to-event outcomes.
Adjustment for treatment changes in epilepsy trials: A comparison of causal methods for time-to-event outcomes.
复制标题
DOI：
10.1177/0962280217735560
复制
发表时间：
2019-03
影响因子：
2.3
通讯作者：
White IR
中科院分区：
文献类型：
--
作者：
Dodd S;Williamson P;White IR
当试验偏离随机化治疗时，旨在估计治疗疗效的简单统计方法，如符合方案或治疗分析，通常会引入选择偏倚。很少采用更合适的方法来调整随机化治疗的偏离，主要是由于其复杂性和不熟悉。我们展示了因果方法的使用，用于生产的被估量与有效的因果解释的时间到事件的结果在分析一个复杂的癫痫试验，作为一个例子，以指导非专业分析师进行类似的分析。两种因果关系的方法，结构故障时间模型和截尾加权的逆概率，进行了调整，以允许倾斜的随时间变化的混杂因素，治疗变化的竞争原因和一个复杂的缓解时间的结果。我们证明了各种因素的影响：方法的选择（结构失效时间模型与截尾加权的逆概率），截尾加权的逆概率模型（合并逻辑回归与考克斯模型），时间间隔（用于创建随时间变化的混杂因素和结果的面板数据），混杂因素的选择和（在合并逻辑回归中）使用样条估计潜在风险。结构失效时间模型可以调整试验治疗之间的转换，但调整本癫痫试验中发生的其他治疗变化的能力有限。截尾权重的逆概率能够针对所有治疗变化进行调整，并证明了与考克斯和汇总logistic回归模型非常相似的结果。考虑到越来越多的随时间变化的混杂因素和治疗变化的原因，对照治疗比意向治疗具有更明显的优势。在一项以缓解结局为特征的复杂试验中，可能会违反结构失效时间模型的基本假设，而删失加权的逆概率可能提供最有用的选择，假设有适当的数据和足够的样本量。当分析人员考虑在给定的试验环境中应用这些方法时，提供了建议。
When trials are subject to departures from randomised treatment, simple statistical methods that aim to estimate treatment efficacy, such as per protocol or as treated analyses, typically introduce selection bias. More appropriate methods to adjust for departure from randomised treatment are rarely employed, primarily due to their complexity and unfamiliarity. We demonstrate the use of causal methodologies for the production of estimands with valid causal interpretation for time-to-event outcomes in the analysis of a complex epilepsy trial, as an example to guide non-specialist analysts undertaking similar analyses. Two causal methods, the structural failure time model and inverse probability of censoring weighting, are adapted to allow for skewed time-varying confounders, competing reasons for treatment changes and a complicated time to remission outcome. We demonstrate the impact of various factors: choice of method (structural failure time model versus inverse probability of censoring weighting), model for inverse probability of censoring weighting (pooled logistic regression versus Cox models), time interval (for creating panel data for time-varying confounders and outcome), choice of confounders and (in pooled logistic regression) use of splines to estimate underlying risk. The structural failure time model could adjust for switches between trial treatments but had limited ability to adjust for the other treatment changes that occurred in this epilepsy trial. Inverse probability of censoring weighting was able to adjust for all treatment changes and demonstrated very similar results with Cox and pooled logistic regression models. Accounting for increasing numbers of time-varying confounders and reasons for treatment change suggested a more pronounced advantage of the control treatment than that obtained using intention to treat. In a complex trial featuring a remission outcome, underlying assumptions of the structural failure time model are likely to be violated, and inverse probability of censoring weighting may provide the most useful option, assuming availability of appropriate data and sufficient sample sizes. Recommendations are provided for analysts when considering which of these methods should be applied in a given trial setting.
登录
查看更多内容
影响因子：
2.3
作者：
Williamson, Elizabeth;Morley, Ruth;Carpenter, James
通讯作者：
Carpenter, James
影响因子：
3.6
作者：
Latimer, Nicholas R.;Abrams, Keith R.;Campbell, Michael J.
通讯作者：
Campbell, Michael J.
影响因子：
1.5
作者：
Watkins, Claire;Huang, Xin;Wright, Elaine J.
通讯作者：
Wright, Elaine J.
影响因子：
2
作者：
Williamson, P. R.;Kolamunnage-Dona, R.;Marson, A. G.
通讯作者：
Marson, A. G.
DOI：
10.1046/j.1369-7412.2003.00417.x
发表时间：
2003-01-01
影响因子：
5.8
作者：
Vansteelandt, S;Goetghebeur, E
通讯作者：
Goetghebeur, E