A 2-Day Workshop by Professor Charles Manski
April 11 (10am to 5pm, followed by an optional dinner) and April 12 (10am to 3pm), 2018 at McMaster Innovation Park, Hamilton, Ontario
To register: Click here (workshop: $190 non-students; $50 for students. Optional dinner: $50 per person). This workshop is geared to PhDs, post-doctoral fellows, researchers, clinicians, health economists, statisticians, and others interested in statistical methods in healthcare.
This is a 2-day course focussed on cutting edge statistical approaches that can be used to enhance the development of clinical guidelines and in informing treatment choice.
Clinical research has favoured trial data because of the limited internal validity of observational studies. Yet trials have methodological problems as well. Small trial sizes limit subgroup specific conclusions for personalized care decisions of multimorbid patients. Extrapolation from trials to clinical practice may be difficult for multiple reasons. Partial identification analysis can address this challenge by providing reliable information on the range of treatment effect sets that are consistent with observational or trial data by considering all credible configurations of counterfactuals (i.e., the possible values of outcomes under alternative treatments).
Prof. Charles Manski pioneered advances in partial identification across many policy applications. He is a superb speaker who can make technical information accessible to broad audiences. His recent work addresses opportunities to build further robustness and rigour into clinical decision-making in relation to patient care under uncertainty with applications to clinical guideline development, prognosis and treatment.
Course topics to be covered include:
- Review of psychological and economic research comparing evidence-based prediction with clinical judgment in personalized medicine
- Critical appraisal of inappropriate extrapolation of results from trials and meta-analysis, and limitations of hypothesis testing for treatment decisions and power-based approaches to choosing trial sample size
- Formal analysis of partial identification and extrapolation of treatment effects with experimental and observational data, including missing data problems
- Formal analysis of decision under uncertainty with experimental and observational data, including discussion of optimal and reasonable decision criteria; rational expectations and subjective utility-based decisions; and decisions under ambiguity
- Case study with course methods: nodal observation or dissection in treatment of melanoma
- An adaptive diversification approach to public health policy which holds promise for reducing uncertainty over time, while fulfilling reasonable decision criteria in the present