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Printable Handouts
Navigable Slide Index
- Introduction
- Three questions
- Hypothesis testing (1)
- Hypothesis testing (2)
- Significance testing (1)
- Significance testing (2)
- Bayesian inference (1)
- Bayesian inference (2)
- A diagnostic test (1)
- A diagnostic test (2)
- The law of likelihood
- The law says
- Degrees of strength
- Three categories of the strength of evidence
- Diagnostic test, revisited
- A positive test result and disease presence
- Irrelevant for data interpretation
- The diagnostic test
- Another diagnostic test
- Question
- Answer
- An observed positive result can be misleading
- The key to the argument
- Three key concepts (1)
- Three key concepts (2)
- Old news
- The University Group Diabetes Program
- UGDP data
- Binomial likelihood
- Probability of cardiovascular death: placebo (1)
- Probability of cardiovascular death: placebo (2)
- Probability of cardiovascular death: tolbutamide (1)
- Probability of cardiovascular death: placebo (3)
- Probability of cardiovascular death: tolbutamide (2)
- Relative risk of cardiovascular death (1)
- Relative risk of cardiovascular death (2)
- 'Undesirable' evidence
- Design considerations
- Probabilities of misleading and weak evidence (1)
- Probabilities of misleading and weak evidence (2)
- Efficiency of sequential designs
- Simulation
- Results
- The universal bound
- Universal bound holds for sequential designs
- The bump function
- Bump equations
- Probabilities of generating misleading evidence (1)
- The tepee function
- Tepee equations
- Probabilities of generating misleading evidence (2)
- Probabilities of generating misleading evidence (3)
- Composite hypothesis (1)
- Composite hypothesis (2)
- Relative risk of cardiovascular death (3)
- So what?
- Extensions
- References
Topics Covered
- Using likelihood ratios to measure the strength of statistical evidence in data
- Paradigms for measuring statistical evidence: the three questions
- Likelihood ratios
- The law of likelihood
- The likelihood principle
- Misleading evidence
- Probabilities of observing misleading evidence and their bounds
- Sequential designs in the likelihood paradigm
- Composite hypothesis and extensions
- Example using real data from a well known clinical trial
Links
Series:
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Talk Citation
Blume, J. (2017, September 28). Likelihood ratios and the strength of statistical evidence [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved December 6, 2024, from https://doi.org/10.69645/JOSG3257.Export Citation (RIS)
Publication History
Financial Disclosures
- Prof. Jeffrey Blume has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.