Flexible statistical models for dynamic prediction in survival analysis with time-varying prognostic factors

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The long-term objective of my research is to develop new, flexible statistical methods that will enhance the scientific validity of clinical studies on the occurrence, progression, treatment and outcomes of many diseases. Studies of human health face many conceptual and methodological challenges, and the mission of biostatistics is to elaborate sophisticated methods to address these challenges.******In the next 5 years, my research will focus on improving the existing statistical methods to handle complexities related to a) changes over time in risk and prognostic factors, as well as in their impact on the health outcomes, and b) multitude of intermittent and final clinical outcomes individual patients may experience during the evolution of their disease. In clinical practice, patients and their physicians have to understand how changes over time, in lifestyle, diet and other modifiable risk factors, as well as in the laboratory tests and biomarkers of disease progression, may affect the risk of hospitalizations, injuries, disease recurrence, or death. Disentangling the impact of various patient characteristics, biomarkers and treatments is complicated by several aspects of dynamic, longitudinal processes being analyzed. First, while many factors that affect simultaneously the risk of a given clinical endpoint, are correlated with each other. Another challenge is related to complex temporal relationships between changes in risk factors and treatments, including delayed or cumulative effects of past treatments or risk factor values, which may also affect each other. For example, whereas an anti-hypertensive treatment will affect the patient's current blood pressure, the decision to prescribe, discontinue or change the dose of such treatment may, in turn, depend on the patient's previous blood pressure and its recent changes. In addition, the final outcome (such as a stroke) may be affected by blood pressure history, as well as by the other effects of the treatment, including both indirect benefits and possible unintended “side effects” (adverse reactions) of the treatment.******To address these challenges, my research will build on the recent progresses in both statistical theory and computational techniques, as well as on my past experience in developing new methods for analyzing longitudinal studies of health outcomes.******Through my numerous clinical collaborations, the results of the proposed research will help improve clinical prognosis and treatment of many diseases.**