Longitudinal targeted maximum likelihood estimation (LTMLE) has very rarely been used to estimate dynamic treatment effects in the context of time-dependent confounding affected by prior treatment when faced with long follow-up times, multiple time-varying confounders, and complex associational relationships simultaneously. Reasons for this include the potential computational burden, technical challenges, restricted modeling options for long follow-up times, and limited practical guidance in the literature. However, LTMLE has desirable asymptotic properties, i.e. it is doubly robust, and can yield valid inference when used in conjunction with machine learning. We use a topical and sophisticated question from HIV treatment research to show that LTMLE can be used successfully in complex realistic settings and compare results to competing estimators. Our example illustrates the following practical challenges common to many epidemiological studies 1) long follow-up time (30 months), 2) gradually declining sample size 3) limited support for some intervention rules of interest 4) a high-dimensional set of potential adjustment variables, increasing both the need and the challenge of integrating appropriate machine learning methods 5) consideration of collider bias. Our analyses, as well as simulations, shed new light on the application of LTMLE in complex and realistic settings: we show that (i) LTMLE can yield stable and good estimates, even when confronted with small samples and limited modeling options; (ii) machine learning utilized with a small set of simple learners (if more complex ones can't be fitted) can outperform a single, complex model, which is tailored to incorporate prior clinical knowledge; (iii) performance can vary considerably depending on interventions and their support in the data, and therefore critical quality checks should accompany every LTMLE analysis.
翻译:长期有针对性的纵向最大可能性估算(LTMLE)很少用于估算在长期后续时间、多重时间变化的困惑和复杂的关联关系同时同时出现同时出现,受先前治疗影响的时间依赖时间的混乱影响的情况下,对动态处理效果的估计(LTMLE)很少使用。 原因包括潜在的计算负担、技术挑战、长期后续时间的有限模型选项以及文献中有限的实际指导。然而,LTMLE具有可取的无症状性能,即它具有双重强力,在与机器学习同时使用时能够产生有效的推断。 我们使用艾滋病毒治疗研究中的一个时尚复杂的复杂问题来表明LTMLTMLL可以成功地在复杂的现实环境中使用,并将结果与相互竞争的估测者进行比较。 我们的例子说明了许多流行病学研究中常见的实际挑战:(1) 长期后续时间(30个月),(2) 样本规模逐渐缩小 3 对某些利益干预规则的支持有限 4 一套高度的潜在调整变量模型,增加了对适当机器学习方法的需求和挑战 5 考虑卷式质量分析 。我们的分析, 以现实的模型的形式分析, 将新的模型显示, 之前的模型显示我们是如何学习的模型 。