![]() Medical research is experiencing a paradigm shift from “one-size-fits-all†strategy to a precision medicine approach where the right therapy, for the right patient, and at the right time, will be prescribed. Wang, Yuanjia Wu, Peng Liu, Ying Weng, Chunhua Zeng, Donglin Learning Optimal Individualized Treatment Rules from Electronic Health Record Data We further present the results of a data analysis in which we estimate a statically optimal rule for switching antiretroviral therapy among patients infected with resistant HIV virus. We explain origin-specific static optimality, and discuss the practical importance of the proposed methodology. However, failure to capture all confounding comes at a price the static optimality of the resulting rules becomes origin-specific. (2007) by developing locally efficient double robust estimators of statically optimal individualized treatment rules responding to such a user-supplied subset of the past. The current article provides an important advance on Petersen et. In practice, however, one typically wishes to find individualized treatment rules responding to a user-supplied subset of the complete observed history, which may not be sufficient to capture all confounding. (2007) further developed estimators of statically optimal individualized treatment rules based on a past capturing all confounding of past treatment history on outcome. (2007) clarified that, in order to be statically optimal, an individualized treatment rule should not depend on the observed treatment mechanism. ![]() ![]() (2007)) is a treatment rule which at any point in time conditions on a user-supplied subset of the past, computes the future static treatment regimen that maximizes a (conditional) mean future outcome of interest, and applies the first treatment action of the latter regimen. A statically optimal individualized treatment rule (as introduced in van der Laan et. ![]() The time-dependent process one observes on each subject contains time-dependent covariates, time-dependent treatment actions, and an outcome process or single final outcome of interest. Statistical Learning of Origin-Specific Statically Optimal Individualized Treatment RulesĬonsider a longitudinal observational or controlled study in which one collects chronological data over time on a random sample of subjects. ![]()
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