UNSW Biostatistics Seminar

The 2019 UNSW Biostatistics Seminar in December will be held at

The George Institute for Global Health

1 King Street, Newtown

Level 5 - Conference Hub

Sydney, NSW 2042

https://goo.gl/maps/7s1Tq582j8y22kCaA

Date: Wednesday December 4 2019 @ 3pm

 

Details of presenters and presentations. There will be an opportunity for networking (and maybe a glass of wine or drinks) afterwards.

Should you have any questions do not hesitate to contact me.

 

You can register through Eventbrite by clicking on the link below:

REGISTER HERE

Disease incidence estimation in routinely collected longitudinal medical data using time between tests methods Dr Hamish McManus In this seminar Dr Hamish McManus will discuss disease incidence estimation in routinely collected longitudinal medical data using time between tests methods. He will compare several commonly used methods which make limited assumptions about timing of disease incidence between testing intervals and propose an updated method building on these and based on the Poisson-binomial distribution. He will apply this method to the TAIPAN study of HIV incidence in men who have sex with men in NSW and Victoria.   Estimating MIC distributions and cutoffs through mixture models: an application to establish M. Tuberculosis resistance Dr Clara Grazian Antimicrobial resistance is becoming a major threat to public health throughout the world. Researchers from around the world are attempting to contrast it by developing both new antibiotics and patient-specific treatments. It is, therefore, necessary to study these treatments, via phenotypic tests, and it is essential to have robust methods available to analyze the resistance patterns to medication, which could be applied to both new treatments and to new phenotypic tests. A general method will be presented to study minimal inhibitory concentration (MIC) distributions and fixed breakpoints in order to separate sensible from resistant strains. The method has been applied to a new 96-well microtiter plate.