- Physicians come to know that precision/personalized medicine for the most part is based on a false premise
- Machine learning/deep learning is understood to not find previously unknown information in data in the majority of cases, and tends to work better than traditional statistical models only when dominant non-additive effects are present and the signal:noise ratio is decently high
- Practitioners will make more progress in correctly using "old" statistical tools such as regression models
- Medical diagnosis is finally understood as a task in probabilistic thinking, and sensitivity and specificity (which are characteristics not only of tests but also of patients) are seldom used
- Practitioners using cutpoints/thresholds for inherently continuous measurements will finally go back to primary references and find that the thresholds were never supported by data
- Dichotomania is seen as a failure to understand utility/loss/cost functions and as a tragic loss of information
- Clinical quality improvement initiatives will rely on randomized trial evidence and de-emphasize purely observational evidence; learning health systems will learn things that are actually true
- Clinicians will give up on the idea that randomized clinical trials do not generalize to real-world settings
- Fewer pre-post studies will be done
- More research will be reproducible with sounder sample size calculations, all data manipulation and analysis fully scripted, and data available for others to analyze in different ways
- Fewer sample size calculations will be based on a 'miracle' effect size
- Non-inferiority studies will no longer use non-inferiority margins that are far beyond clinically significant
- Fewer sample size calculations will be undertaken and more sequential experimentation done
- More Bayesian studies will be designed and executed
- Classification accuracy will be mistrusted as a measure of predictive accuracy
- More researchers will realize that estimation rather than hypothesis testing is the goal
- Change from baseline will seldom be computed, not to mention not used in an analysis
- Percents will begin to be replaced with fractions and ratios
- Fewer researchers will draw any conclusion from large p-values other than "the money was spent"
- Fewer researchers will draw conclusions from small p-values
This blog is devoted to statistical thinking and its impact on science and everyday life. Emphasis is given to maximizing the use of information, avoiding statistical pitfalls, describing problems caused by the frequentist approach to statistical inference, describing advantages of Bayesian and likelihood methods, and discussing intended and unintended differences between statistics and data science. I'll also cover regression modeling strategies, clinical trials, and drug evaluation.
Friday, December 29, 2017
New Year Goals
Here are some goals related to scientific research and clinical medicine that I'd like to see accomplished in 2018.
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