Biostatistics Team

The Biostatistics Team aims to gather statistical and methodological expertise across the Department of Global Public Health and Centre for Epidemiology and Community Medicine.

Our goal is strengthening our research capacity in epidemiology and public health and exploiting the richness and interdisciplinary work carried out within the department.

The objectives of the team are to:

  • interact with the research groups
  • provide strategic quantitative methodological support
  • teach biostatistics in undergraduate and postgraduate courses
  • organize regular workshops and short courses on special topics
  • develop sound and innovative statistical methods
  • share user-friendly statistical tools
  • offer statistical support for grant proposals
  • co-supervise doctoral students

​Group members

Nicola Orsini

Head of Biostatistics Team
K9 Department of Global Public Health

Alessio Crippa

Senior research specialist
C8 Department of Medical Epidemiology and Biostatistics



Dose-response meta-analysis

A dose-response analysis describes the changes of a response across levels of a quantitative factor. A meta-analysis of dose-response (exposure-disease) relations aims at identifying the summary trend emerging from multiple studies trying to answer the same research question. Examples of questions usually asked are:

  • Is there any association at all?
  • Is the response changing approximately at a constant rate throughout the observed exposure range?
  • Is there any substantial change in the outcome beyond a certain exposure level?

We focus on addressing these questions by developing statistical methods for the analysis of multiple summarized data arising from experimental or observational data.

A percentile approach for time-to-event analysis

The percentile can be an attractive measure because it makes explicit the link between two pieces of information: survival probability and a specific time point. Results from prospective studies can be presented in terms of differences or ratios of survival times, facilitating both interpretation and communication of scientific findings.

The introduction of a statistical technique to estimate conditional survival percentiles has substantially enriched its potentialities and eased its application in research. We exploit the potential of the percentile in epidemiological and public health research.

Time-series analysis to assess the effectiveness of interventions

Randomized controlled trials are considered the ideal approach for assessing the effectiveness of interventions. Not all interventions, however, can be assessed with a trial simply because it is not affordable, ethical, or possible. In addition, even well designed randomized trial can be susceptible to biases, particularly when generalizing results to “real world” settings.

Interrupted time-series analysis is a suitable evaluation approach when a single unit (N=1) is being studied (i.e., individual, city, state, country), the outcome is serially ordered as a time series, and multiple observations are collected at equally space intervals.

We focus on methodological questions such as how to adjust for correlated outcome measures and regular seasonal fluctuations that could potentially bias the time-series analysis, how to deal with time-varying confounding due to changes in outcome coding, co-interventions, or changes in the population under study.

Research support



  • Sander Greenland. Professor of Epidemiology and Statistics. Department of Epidemiology, School of Public Health, University of California.
  • Matteo Bottai. Professor of Biostatistics. Unit of Biostatistics. Institute of Environmental Medicine.
  • Donna Spiegelman. Professor of Epidemiological Methods. Department of Epidemiology and Biostatistics. Harvard T.H. Chan School of Public Health.
  • Rino Bellocco. Professor of Statistics, Department of Statistics and Quantitative Methods, University of Milano-Bicocca and Department of Medical Epidemiology and Biostatistics, Karolinska Institutet.

Recent publications

Multiplicative models for survival percentiles: estimating percentile ratios and multiplicative interaction in the metric of time. Bellavia A, Bottai M, Orsini N. Epidemiology, Biostatistics and Public Health. 2016.

Dose-response meta-analysis of differences in means.
Crippa A, Orsini N
BMC Med Res Methodol 2016 08;16():91

Multivariate Dose-Response Meta-Analysis: the dosresmeta R Package. Crippa A, Orsini N. Journal of statistical software. August 2016, Volume 72.

A new measure of between-studies heterogeneity in meta-analysis. Crippa A, Khudyakov P, Wang M, Orsini N, Spiegelman D. Statistics in Medicine. 2016 Sep 20;35(21):3661-75.

Evaluating additive interaction using survival percentiles. Bellavia A, Bottai M, Orsini N. Epidemiology. 2016 May;27(3):360-4.

On the interpretation of risk and rate advancement periods. Discacciati A, Bellavia A, Orsini N, Greenland S. International Journal of Epidemiology. 2015. doi:10.1093/ije/dyv320

Goodness of fit tools for dose-response meta-analysis of binary outcomes. Discacciati A, Crippa A, Orsini N. Research Synthesis Methods. 2015. DOI: 10.1002/jrsm.1194.

Using Laplace regression to model and predict percentiles of age at death, when age is the primary time-scale. Bellavia A, Discacciati A, Bottai M, Wolk A, Orsini N. Am J Epidemiol. 2015. Aug 1;182(3):271-7.

Adjusted survival curves with multivariable Laplace regression. Bellavia A, Bottai M, Orsini N. Epidemiology. 2015. Volume 26 - Issue 2. pp: 137-288,e14-e30.

A Gradient Search Maximization Algorithm for the Asymmetric Laplace Likelihood. Bottai M, Orsini N, Geraci M. Journal of Statistical Computation and Simulation. 2015. Volume 85, Issue 10.


Nicola Orsini

Head of the Biostatistics Team
K9 Department of Global Public Health
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