Many researchers worry about violations of the proportional hazards assumption when comparing treatments in a randomized study. Besides the fact that this frequently makes them turn to a much worse approach, the harm done by violations of the proportional odds assumption usually do not prevent the proportional odds model from providing a reasonable treatment effect assessment.
This article shows an example formally testing for heterogeneity of treatment effect in the GUSTO-I trial, shows how to use penalized estimation to obtain patient-specific efficacy, and studies variation across patients in three measures of treatment effect.
Researchers have used contorted, inefficient, and arbitrary analyses to demonstrated added value in biomarkers, genes, and new lab measurements. Traditional statistical measures have always been up to the task, and are more powerful and more flexible. It's time to revisit them, and to add a few slight twists to make them more helpful.
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 N1 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 1.
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.
Optimum decision making in the presence of uncertainty comes from probabilistic thinking. The relevant probabilities are of a predictive nature: P(the unknown given the known). Thresholds are not helpful and are completely dependent on the utility/cost/loss function.
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.