Title: Targeted Learning with Daily EHR Data,
Abstract: Electronic health records (EHR) data provide a cost and time-effective
opportunity to conduct cohort studies of the effects of multiple time-point
interventions in the diverse patient population found in real-world clinical
settings. Because the computational cost of analyzing EHR data at daily (or
more granular) scale can be quite high, a pragmatic approach has been to
partition the follow-up into coarser intervals of pre-specified length. Current
guidelines suggest employing a 'small' interval, but the feasibility and
practical impact of this recommendation has not been evaluated and no formal
methodology to inform this choice has been developed. We start filling these
gaps by leveraging large-scale EHR data from a diabetes study to develop and
illustrate a fast and scalable targeted learning approach that allows to follow
the current recommendation and study its practical impact on inference. More
specifically, we map daily EHR data into four analytic datasets using 90, 30,
15 and 5-day intervals. We apply a semi-parametric and doubly robust estimation
approach, the longitudinal TMLE, to estimate the causal effects of four dynamic
treatment rules with each dataset, and compare the resulting inferences. To
overcome the computational challenges presented by the size of these data, we
propose a novel TMLE implementation, the 'long-format TMLE', and rely on the
latest advances in scalable data-adaptive machine-learning software, xgboost
and h2o, for estimation of the TMLE nuisance parameters.