I develop designs and methods of analyses to evaluate treatments in medicine, public health and policy (causal inference). The increased quality and number of available treatments, and increasing ethical and practical constraints, are transforming the field of intervention research: the factors of research interest are no longer (and correctly so) the same as factors that we can intervene on humans. To address this, we have been developing new designs and methods for partially controlled studies, that is, studies that explore the factors that can be controlled, in order to investigate the effects of the factors of research interest.
For example, even in the most reliable medical studies -- the ``randomized studies'', patients often do not comply with the assigned treatments and drop out. We have shown that the ``intention-to-treat method'', which has been widely used for those situations, is not suitable to generally estimate even the ``intention-to-treat effects'', and we have provided appropriate methodology. We have recently integrated this work with Don Rubin in a unifying statistical framework, ``principal stratification". Principal stratification allows researchers to formulate designs and address a challenging statistical problem with partial control: to find the degree to which the effect of a controlled treatment or factor on a main outcome is explained by the effect of the controlled treatment on the activation of intermediate causal pathways that are not directly controlled.
Principal stratification has now been applied in a broad range of areas, including HIV; cancer; ophthalmology; orthopedics; mental health; nephrology; surrogate endpoints; noncompliance with missing outcomes; and effects of vaccines on viral load for those infected.