|dc.description.abstract||Identifying interventions that are optimally tailored to each individual is of significant interest in various fields, in particular precision medicine. Dynamic treatment regimes (DTRs) employ sequences of decision rules that utilize individual patient information to recommend treatments. However, the assumption that an individual's treatment does not impact the outcomes of others, known as the no interference assumption, is often challenged in practical settings. For example, in infectious disease studies, the vaccine status of individuals in close proximity can influence the likelihood of infection. Imposing this assumption when it, in fact, does not hold, may lead to biased results and impact the validity of our resulting DTR optimization.
In this thesis, we extend the estimation method of dynamic weighted ordinary least squares (dWOLS), a doubly robust and easily implemented approach for estimating optimal DTRs, to incorporate the presence of interference. Specifically, we develop new methodologies to optimize DTRs in the presence of interference for both binary and continuous treatments. Through comprehensive simulations and analysis of the Population Assessment of Tobacco and Health (PATH) data, we demonstrate the performance of the proposed joint optimization strategy compared to the current state-of-the-art conditional optimization methods. Furthermore, we extend the dWOLS method to accommodate multiple outcomes and patient-specific costs, enhancing its flexibility and applicability in complex health contexts.||en