Towards more dynamic modeling of high-dimensional register-based data

前言：我们的目标是开发统计降维技术来控制药物安全性观察性研究中潜在混杂因素的高维空间的混杂。这类数据通常是通过关于药品处方或报销和住院情况的常规行政报告系统产生的。这类研究面临的挑战是，常规数据收集没有针对研究目的进行优化，因此往往缺乏直接处理混淆问题的变量，因此，通常使用大量可能对潜在混淆因素提供信息的现有指标。传统的提取混杂信息的方法很大程度上是基于变量选择技术，而我们认为通过特征选择可以更好地解决这个问题。然而，为此目的，需要制定专门的统计方法。 长期研究目标：除了高维之外，来自常规报告系统的数据还具有连续时间的特点。为了适应这一点，我们将研究降维方法的动态推广，以控制由于时变的个体水平特征造成的混杂。这需要将降维建模为时间的连续平滑函数。 最新进展：由于与处方药的暴露相比，感兴趣的不良后果往往很少见，因此更可取的做法是通过建立暴露/处方的模型而不是结果的模型来控制混淆。这两种众所周知的方法是倾向分数调整/匹配或治疗概率(IPT)加权；我们开发了前者的连续时间变化和后者的贝叶斯版本。如果曝光模型包括仅预测曝光的变量，则效果估计的方差可能被夸大，从而促使研究将曝光图案的建模与降维相结合。 未来五年的短期目标：我们将调查针对特定结果和暴露变量量身定做的降维，以捕捉高维协变量空间所解释的最大混乱量，并开发它们的估计方法。此外，我们将开发基于估计的降维与倾向性分数调整和IPT加权相结合的估计暴露影响的推理程序。由于记录的协变量大体上是事件，而不是连续值测量，因此估计降维需要基于可能性的方法，包括贝叶斯方法。 影响：通过基于人群的观察性研究来监测药物治疗的长期安全性是最可行的。通过我们的研究，我们的目标是更有效地利用行政卫生保健数据库作为监测药物安全的资源。这种观察性数据的一个主要来源是加拿大通过省级医疗保健计划实施的全民公共医疗保健系统。作为省级卫生保健数据库及其利用的一个例子，我们提到了魁北克的RAMQ和MED-ECHO数据库以及加拿大药物效应观察性研究网络(CNODES)的合作框架。我们研究的目的是提出直接适用于此类研究的统计方法。

Introduction: We aim to develop statistical dimension reduction techniques for controlling for confounding in observational studies of drug safety with a high-dimensional space of potential confounders. Such data are commonly generated through routine administrative reporting systems on drug prescription or reimbursement and hospitalizations. The challenge in such studies is that the routine data collection has not been optimized for research purposes, thus often lacking variables to directly address confounding, and as a result, a large number of available indicators potentially informative of the underlying confounders are typically used instead. The conventional approaches to extract confounder information have largely been based on variable selection techniques, whereas we argue that the problem is better addressed through feature selection. However, development of specialized statistical methodology is needed for this purpose. Long-term research objectives: In addition to high dimensionality, data coming through routine reporting systems are characterized by continuous time. To accommodate this, we will investigate dynamic generalizations of the dimension reduction approaches to control for confounding due to time-varying individual-level characteristics. This calls for modeling of the dimension reductions as continuous smooth functions of time. Recent progress: Since the adverse outcomes of interest are often rare compared to the exposures, which are prescription medications, it is preferable to approach controlling for confounding through modeling of the exposure/prescription rather than through modeling of the outcome. The two well known methods for this are propensity score adjustment/matching or inverse probability of treatment (IPT) weighting; we have developed a continuous-time variation of the former and a Bayesian version of the latter. If the exposure model includes variables only predictive of the exposure, variance of the effect estimate may be inflated, motivating research into combining modeling of the exposure patterns with dimension reduction. Short-term objectives for the next five years: We will investigate dimension reductions tailored to specific outcome and exposure variables to capture maximal amount of confounding accounted for by the high-dimensional covariate space, and develop methodology for their estimation. Furthermore, we will develop inference procedures for estimating exposure effects based on using the estimated dimension reductions in conjunction with propensity score adjustment and IPT weighting. Because the recorded covariates are by and large events, rather than continuous-valued measurements, estimation of the dimension reductions requires likelihood-based, including Baysian, approaches. Impact: Monitoring the long-term safety of drug therapies is most feasible through observational population-based studies. Through our research, we aim to contribute to more efficient utilization of administrative health care databases as a resource for monitoring drug safety. A major source for such observational data is the universal public health care system in place in Canada, implemented through provincial health care plans. As an example the provincial health care databases and their utilization, we mention the RAMQ and MED-ECHO databases of Quebec and the collaborative framework of the Canadian Network for Observational Drug Effect Studies (CNODES). The objective of our research is to produce statistical methodology directly applicable in such studies.