Marcos Vera Hernandez (who’s one of the co-director of our MSc programme) and his colleague Toru Kitagawa have been involved in the organisation of a couple of very interesting seminars on the econometrics of personalised medicine at CeMMAP/UCL.
The first one is a masterclass by Charles Manski of Northwestern University in the US. His talk will be a two-day events on the 28th-29th March. Here’s a link to register to the masterclass. The specific topic is “Personalised Patient Care Under Uncertainty”. The event is sponsored by CeMMAP and the Economics Research Initiative at Duke University and the masterclass will focus on evidence-based decision making for patient care under uncertainty, in which clinicians face only limited ability to predict patients’ future illness and treatment response. To deal with this inherent uncertainty, partial identification analysis can be applied to make credible predictions for patient outcomes. This analysis motivates the use of decision criteria with well understood properties. Particular focus will be given to the minimax-regret criteria, which specifies a decision rule as uniformly close to the optimal decision rule as possible given the underlying uncertainty of patient outcomes. This Masterclass is ideal for economists, epidemiologists, bio-statisticians, and medical researchers who perform trials or do observational studies. Faculty, postdocs and graduate students are also welcome to apply for this masterclass. The fees for registration to this event are £75 for Higher Education delegates; £200 for Charity/Government delegates: and £450 for other delegates (all prices are exclusive of VAT).
The second event is organised for the 26th March by Toru Kitagawa (UCL) and Aleksey Tetenov (University of Bristol). The title is “Personalised Treatment: Learning and Decision” and more information can be found here. The workshop aims at presenting recent developments on evidence-based design of personalized treatment and targeting policies. The idea is to bring together students and researchers in economics, epidemiology, medicine and statistics, with research interest in various topics, including Econometric and machine learning methods for personalized treatment/policy; medical or policy decision under ambiguity; and meta-analysis for medical or policy decision making. Confirmed speakers include Jason Abaluck (Yale), Karun Adusumilli (U Penn), Rachel Cassidy (IFS), Sukjin Han (UT Austin), Charles Manski (Northwestern) and Stefan Wager (Stanford).