This article provides my reflections after the PCORI/PACE Evidence and the Individual Patient meeting on 2018-05-31. The discussion includes a high-level view of heterogeneity of treatment effect in optimizing treatment for individual patients.

This article elaborates on Frank Harrell’s post providing guidance in choosing between machine learning and statistical modeling for a prediction project.

This article provides general guidance to help researchers choose between machine learning and statistical modeling for a prediction project.

Deep learning and other forms of machine learning are getting a lot of press in medicine. The reality doesn’t match the hype, and interpretable statistical models still have a lot to offer.

It is easy to compute the sample size N_{1} needed to reliably estimate how one predictor relates to an outcome. It is next to impossible for a machine learning algorithm entertaining hundreds of features to yield reliable answers when the sample size < N_{1}.

Frank Harrell
Professor of Biostatistics
Vanderbilt University School of Medicine
Laura Lazzeroni
Professor of Psychiatry and, by courtesy, of Medicine (Cardiovascular Medicine) and of Biomedical Data Science
Stanford University School of Medicine
Revised July 17, 2017 It is often said that randomized clinical trials (RCTs) are the gold standard for learning about therapeutic effectiveness. This is because the treatment is assigned at random so no variables, measured or unmeasured, will be truly related to treatment assignment.

Misinterpretation of P-values and Main Study Results Dichotomania Problems With Change Scores Improper Subgrouping Serial Data and Response Trajectories Cluster Analysis As Doug Altman famously wrote in his Scandal of Poor Medical Research in BMJ in 1994, the quality of how statistical principles and analysis methods are applied in medical research is quite poor. According to Doug and to many others such as Richard Smith, the problems have only gotten worse.

I discussed the many advantages or probability estimation over classification. Here I discuss a particular problem related to classification, namely the harm done by using improper accuracy scoring rules. Accuracy scores are used to drive feature selection, parameter estimation, and for measuring predictive performance on models derived using any optimization algorithm. For this discussion let Y denote a no/yes false/true 0/1 event being predicted, and let Y=0 denote a non-event and Y=1 the event occurred.

Methods used to obtain unbiased estimates of future performance of statistical prediction models and classifiers include data splitting and resampling. The two most commonly used resampling methods are cross-validation and bootstrapping. To be as good as the bootstrap, about 100 repeats of 10-fold cross-validation are required.
As discussed in more detail in Section 5.3 of Regression Modeling Strategies Course Notes and the same section of the RMS book, data splitting is an unstable method for validating models or classifiers, especially when the number of subjects is less than about 20,000 (fewer if signal:noise ratio is high).

It is important to distinguish prediction and classification. In many decisionmaking contexts, classification represents a premature decision, because classification combines prediction and decision making and usurps the decision maker in specifying costs of wrong decisions. The classification rule must be reformulated if costs/utilities or sampling criteria change. Predictions are separate from decisions and can be used by any decision maker. Classification is best used with non-stochastic/deterministic outcomes that occur frequently, and not when two individuals with identical inputs can easily have different outcomes.

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