Adjunct Professor of Biostatistics
Many important questions in public health are about the effects of interventions, e.g. changing health policy, approving new drugs or implementing optimal treatment strategies. The answer to these questions often relies on either non-experimental, i.e. observational, data or on imperfect experimental data, i.e. randomized trial data from suffering from non-compliance, drop-outs, intermittent non-response, censoring, etc.
My research is in the development of analytical tools for estimating, from non or imperfect experimental data, the effects of interventions This work falls into the general area of causal inference and missing and censored data analysis.
I am primarily interested in the development of (semiparametric efficient) methods that exploit the information in the available data without making unnecessary assumptions about the parts of the data generating process that are not of substantive interest.
My latest work includes methods for
1) estimating, from longitudinal health care databases, optimal dynamic treatment regime strategies,
2) evaluating diagnostic markers from studies that suffer from verification bias,
3) correcting for intermittent non-response in longitudinal studies,
4) analyzing failure time and quality of life adjusted failure time endpoints in studies with competing informative causes of censoring,
5) analyzing clinical trials with non-compliance and
6) estimating from randomized trials, the effect of treatments when the outcomes are available only if a postrandomization event has occurred.
Licenciate, Mathematics, 1982, University of Buenos Aires
Ph.D., Statistics, 1988, University of California, Berkeley