Document information
- University
- Politecnico di Milano
- Degree programme
- Biomedical Engineering
- Subject
- Clinical Technology Assessment
- Classification
- Other study material
- Original format
- Text
- Searchable text
University study material for Clinical Technology Assessment in the Biomedical Engineering degree programme at Politecnico di Milano. The document covers: Clinical Technology Assessment – Exam Questions • Decision tree, ICER, PSA and selection bias • Survival analysis, CEAC, CEAFs, beta and Dirichlet distributions, Markov models • Outcome measures, effect measures of dichotomous (RR and OR), ways of solving Markov models
University study material for Clinical Technology Assessment in the Biomedical Engineering degree programme at Politecnico di Milano. The document covers: Clinical Technology Assessment – Exam Questions • Decision tree, ICER, PSA and selection bias • Survival analysis, CEAC, CEAFs, beta and Dirichlet distributions, Markov models • Outcome measures, effect measures of dichotomous (RR and OR), ways of solving Markov models
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Clinical Technology Assessment – Exam Questions • Decision tree, ICER, PSA and selection bias • Survival analysis, CEAC, CEAFs, beta and Dirichlet distributions, Markov models • Outcome measures, effect measures of dichotomous (RR and OR), ways of solving Markov models (montecarlo, cohort and matrix) • Clinical trials (what it is and how to perform and report it) – randomization, blinding, types of bias. Sample size formula, cost-effectiveness plane, sensitivity analysis and possible distributions, CEAC. • Decision model, ICER, scatter plot, different probability distributions for inputs and why (no formulas or graphs), selection bias and the 3 methods for randomizing the sequence generation, how to compute the CEAC and CEAFs in details • Decision tree, funnl plot, meta-analysis and distribution probabilities • PSA, CEAC and CEAFs, EVPI, primary data methods, factors a ffecting the power of a study (presentation on the sample size) and power curve • Decision tree, ICER, deterministic sensitivity analysis (the 4 types, but specifically the extremes), sources of bias (selection: types of randomization of the allocation sequence & reporting: funnel plot) The questions are usually the titles of the lectures, and if you know enough she doesn’t interrupt you and lets you talk without going into det ail. If you don’t answer of if you start saying some thing wrong, she’ll probably go into detail to see what you don’t know. The grades depend but are pretty high even when it goes really bad.
First page of the document.