Mendelian randomization is an instrumental variable method that utilizes genetic information to investigate the causal effect of a modifiable exposure on an outcome. In most cases, the exposure changes over time. Understanding the time-varying causal effect of the exposure can yield detailed insights into mechanistic effects and the potential impact of public health interventions. Recently, a growing number of Mendelian randomization studies have attempted to explore time-varying causal effects. However, the proposed approaches oversimplify temporal information and rely on overly restrictive structural assumptions, limiting their reliability in addressing time-varying causal problems. This paper considers a novel approach to estimate time-varying effects through continuous-time modelling by combining functional principal component analysis and weak-instrument-robust techniques. Our method effectively utilizes available data without making strong structural assumptions and can be applied in general settings where the exposure measurements occur at different timepoints for different individuals. We demonstrate through simulations that our proposed method performs well in estimating time-varying effects and provides reliable inference results when the time-varying effect form is correctly specified. The method could theoretically be used to estimate arbitrarily complex time-varying effects. However, there is a trade-off between model complexity and instrument strength. Estimating complex time-varying effects requires instruments that are unrealistically strong. We illustrate the application of this method in a case study examining the time-varying effects of systolic blood pressure on urea levels.

翻译：孟德尔随机化是一种工具变量方法，利用遗传信息研究可调控暴露因素对结果的因果效应。在多数情况下，暴露因素会随时间变化。理解暴露因素的时变因果效应可深入揭示机制性影响及公共卫生干预措施的潜在作用。近年来，越来越多的孟德尔随机化研究试图探索时变因果效应，但现有方法过度简化时间信息且依赖过于严格的结构假设，限制了其处理时变因果问题的可靠性。本文提出了一种新方法，通过结合函数主成分分析与弱工具变量稳健技术，利用连续时间建模估计时变效应。该方法无需强结构假设即可有效利用可用数据，适用于不同个体暴露测量时间点不同的通用场景。模拟研究表明，当时变效应形式正确指定时，所提方法在估计时变效应方面表现良好，并能提供可靠的推断结果。理论上，该方法可用于估计任意复杂度的时变效应，但模型复杂度与工具强度之间存在权衡。估计复杂时变效应需要现实中难以实现的强工具变量。我们通过案例研究展示了该方法在分析收缩压对尿素水平的时变效应中的应用。