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

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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.