Grant: Foundation Grant

While our current understanding of how well biomedical treatments work is based on population averages, the average effect of a treatment across a population may not be a good indicator of whether or not to recommend a particular treatment for an individual patient. This is because individual patients may respond differently to the same treatment due to their unique combinations of factors such as pre-existing health conditions, genetics, environmental factors, and lifestyle choices. Moreover, a patient may respond differently to the same treatment over time, e.g. as he/she ages. These two types of variability (between individuals and within a single individual over time) motivate the need for targeted treatment plans in which patient care is improved by using information on an individual's changing health status to adapt treatment recommendations accordingly. Examples include the management of chronic conditions, treatments that require frequent adjustments, or clinical decisions based on events such as remissions or intolerable side effects of a drug therapy. This approach, known as precision medicine, relies on "adaptive treatment strategies" where treatment plans account for between and within individual differences over time. The goal is to help physicians deliver the best treatment for each patient each time a treatment decision is made. These strategies provide answers to questions like "If a patient has not responded to an antidepressant, should the dose of that drug be increased, or should a new drug be prescribed?" While the concept of adaptive treatment strategies has existed for more than 2 decades, new statistical methods and tools are needed to gain maximum insight from contemporary "big data" datasets and to accommodate new treatments that have been developed . My research goals for the next 7 years are to advance statistical theory and develop the needed methods and tools for answering clinically relevant questions related to HIV and mental health.