Targeted Empirical Super Learning in HIV Research

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DESCRIPTION (provided by applicant): The aim of this project is to study and extend a general statistical methodology, called Targeted Empirical Learning, which includes a recently developed Targeted Maximum Likelihood methodology. The fundamental theoretical underpinnings of this new and unified approach to statistical learning have been developed and we propose to expand Targeted Empirical Learning into a practical product that can be applied to pressing scientific questions. Building on long-standing collaborations with leading scientists in the areas of clinical AIDS research, we will use this novel methodology to address research questions concerning HIV. Given observed data consisting of a realization of n independently and identically distributed random variables, Targeted Empirical Learning employs the following elements: i) defining the parameter of interest; 2) modeling the parameter of interest, leaving the nuisance parameters unspecified or only including truly known modeling assumptions; 3) developing targeted robust and highly efficient (maximum likelihood) estimators of the parameter of interest. The methodology relies on unified cross- validation to choose between competitive estimators indexed by, for example, choices of sieves parameterizations, algorithms, and/or dimension reductions (in particular for the nuisance parameters). Importantly, the cross-validation criterion employed evaluates the performance of these candidate estimators with respect to the parameter of interest. Specific applications to be addressed include the following: i) develop models and corresponding targeted empirical learners of optimal individualized treatment rules for treating HIV-infected patients, 2) estimate measures of variable importance/causal effects for mutations in the HIV virus for predicting clinical response to drug combinations; 3) estimate causal effects of adherence profiles on virologic suppression for HIV-infected patients. We will further develop and apply a novel resampling-based multiple testing methodology to properly address our simultaneous testing and estimation of many scientific parameters of interest.