Sebastien Haneuse
Primary Faculty

Sebastien Haneuse

Professor of Biostatistics


Other Positions

Director of Graduate Studies


Harvard T.H. Chan School of Public Health


My statistical research interests broadly focus on the design and analysis of observational studies, primarily in the context of epidemiology.

I have a relatively broad range of research interests, most of which are within the context of the design and analysis of observational studies. The three areas that collectively represent my primary focus are:

The analysis of semi-competing risks survival data, where interest lies the distribution of some non-terminal event but that observation time is subject to truncation by death.
The use of biased sampling schemes to mitigate biases that commonly arise in observational studies, including confounding and selection bias, as well as to enhance statistical efficiency in resource-limited settings.
The use of data from large, complex electronic health record and administrative databases for public health research.
In addition, there are a number of areas that I have either dabbled in or begun thinking about, but haven't had a chance to fully develop:

Hospital/provider profiling.
Statistical methods for the analysis of multi- and trans-generational studies.
The use of non-parametric Bayesian formulations to (i) gain insights into mechanisms and/or etiology, and (ii) overcome the consequences of model misspecification, particularly in the analysis of correlated or longitudinal data.
Methods for causal inference when the treatment of interest is continuous.
The development of new strategies for monitoring and evaluation of public health programs in resource-limited settings.
The use of biased sampling schemes in the context of prediction studies.
I have also worked and published in a broad range of substantive areas, including:

Breast cancer screening
Alzheimers' disease
Long-term outcomes among patients undergoing bariatric surgery
Readmission and mortality among patients diagnosed with cancer
LGBQT health
Skin cancer prevention among survivors of childhood cancer
HIV/AIDS, particularly in low-income countries



Predicting prenatal care rates in rural Ethiopia

Through predictive models, it may be possible to identify pregnant women in low-resource settings who are at high risk of failing to attend antenatal care, in order to develop interventions to encourage their attendance, according to a new…