Economist Charles Manski will give a special seminar on Thursday Sept. 12 on the topic: “Predicting Kidney Transplant Outcomes with Partial Knowledge of HLA Mismatch.”
This talk is an application of the ideas Dr. Manksi presented last year at a CHEPA-sponsored short-course entitled “Techniques to Inform Development of Clinical Guidelines and Treatment Choice” and which inform his book: Patient Care under Uncertainty: How cutting-edge economics can improve decision-making methods for doctors.
Dr. Manski, a Professor of Economics at Northwestern University in Illinois, is an econometrician specializing in rational choice theory, and an innovator in parameter identification. His research spans econometrics, judgment and decision, and the analysis of social policy.
He is known in the economics field for landmark work on partial identification, identification of discrete choice models, and the identification of social interactions, as well as empirical research on measurement of expectations in surveys.
The seminar, sponsored by McMaster’s Department of Economics and CHEPA, will take place on Thursday, September 12 from 3-4:30 p.m. in room CRL-B119. All are welcome to attend.
It will also be available online through Webex. The password is Manskiseminar. To connect, copy and paste this link into your browser:
The meeting number is 665 960 940
Here is the abstract for his presentation:
We consider prediction of graft survival when a kidney from a deceased donor is transplanted into a recipient, with focus on the variation of survival with degree of Human Leukocyte Antigen (HLA) mismatch. Previous studies have used data from the Scientific Registry of Transplant Recipients (SRTR) to predict survival conditional on partial characterization of HLA mismatch. Whereas earlier studies assumed proportional hazards models, we used nonparametric regression methods. These do not make the unrealistic assumption that relative risks are invariant as a function of time since transplant and, hence, should be more accurate. To refine the predictions possible with partial knowledge of HLA mismatch, it has been suggested that HaploStats statistics on the frequencies of haplotypes within specified ethnic/national populations be used to impute complete HLA types. We counsel against this, showing that it cannot improve predictions on average and sometimes yields suboptimal transplant decisions. We show that the HaploStats frequency statistics are nevertheless useful when combined appropriately with the SRTR data. Analysis of the ecological inference problem shows that informative bounds on graft survival probabilities conditional on refined HLA typing are achievable by combining SRTR and HaploStats data with immunological knowledge of the relative effects of mismatch at different HLA loci.