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

简介：我们的目标是开发统计降维技术，用于控制具有潜在混杂因素的高维空间的药物安全性观察性研究中的混杂因素。这些数据通常是通过关于药物处方或报销和住院的例行行政报告系统生成的。此类研究的挑战在于，常规数据收集尚未针对研究目的进行优化，因此通常缺乏直接解决混杂因素的变量，因此，通常使用大量可能提供潜在混杂因素信息的可用指标。传统的方法来提取混淆信息在很大程度上是基于变量选择技术，而我们认为，这个问题是更好地解决通过特征选择。长期研究目标：除了高维度之外，来自常规报告系统的数据还具有连续时间的特征。为了适应这一点，我们将调查动态概括的降维方法，以控制由于时变的个人水平的特点混淆。这就要求建模的维数减少作为连续的平滑函数的time.Recent进展：由于利益的不良后果往往是罕见的相比，曝光，这是处方药，它是最好的方法控制混淆通过建模的曝光/处方，而不是通过建模的结果。这两个众所周知的方法是倾向评分调整/匹配或逆概率治疗（IPT）加权，我们已经开发了一个连续时间的变化，前者和后者的贝叶斯版本。如果暴露模型仅包含预测暴露的变量，则效应估计的方差可能会被夸大，从而促使研究将暴露模式建模与降维相结合。未来五年的短期目标：我们将研究针对特定结局和暴露变量的降维，以捕获由高维协变量空间解释的最大混杂量，并制定估算方法。此外，我们将开发推断程序，估计暴露效应的基础上使用估计的降维结合倾向评分调整和IPT加权。由于记录的协变量是由大事件，而不是连续值的测量，估计的维数减少需要基于似然性，包括贝叶斯，approaches.Impact：监测药物治疗的长期安全性是最可行的，通过观察性人群为基础的研究。通过我们的研究，我们的目标是促进更有效地利用行政卫生保健数据库作为监测药物安全性的资源。这种观察数据的一个主要来源是加拿大通过省级保健计划实施的全民公共保健系统。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数据库以及加拿大观察性药物效应研究网络（hocDES）的合作框架。我们研究的目的是产生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.