We are inviting submissions for a special issue of Computational Statistics and Data Analysis
dealing with advanced methods and innovative applications related to the broad area of medical
statistics.

Research in the medical area is rapidly developing and new challenges for statisticians are continuously
emerging. Personalized medicine, rare diseases and observational studies are only a few
catchphrases that motivate statisticians all over the world to develop new methods and advanced
analysis strategies. This special issue focuses on modern statistical methods related to any aspect
and application in medical research. However new methods or analysis strategies should always
be directly related to medical research questions and a motivating applied example is strongly
recommended.

The papers should have a significant novel computational or advanced data analytic component in
order to be considered for publication. In particular simulation papers will be considered only if
they include a novel methodological component. Authors who are uncertain about the suitability
of their papers should contact the special issue editors. All submissions must contain original
unpublished work not being considered for publication elsewhere.

Submissions will be refereed according to standard procedures for Computational Statistics & Data Analysis. Information about the journal can be found at http://www.elsevier.com/locate/csda.

The deadline for submissions is 28th February 2016. However, papers can be submitted at any time;
and, when they have been received, they will enter the editorial system immediately.

Papers for the special issue should be submitted using the Elsevier Electronic Submission tool EES:
http://ees.elsevier.com/csda. In the EES please choose the special issue on Advances in Medical
Statistics, and the Co-Editor responsible for the special issues.

For further information please send an email to: [log in to unmask]

The special issue editors:
Jae Won Lee, Korea University, Seoul, South Korea, E-mail: [log in to unmask]
Nan Lin, Washington University in St. Louis, USA, E-mail: [log in to unmask]
Martina Mittlboeck, Medical University of Vienna, Vienna, Austria, E-mail: [log in to unmask]