Statistically Efficient Ways to Quantify Added Predictive Value of New Measurements

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.

Viewpoints on Heterogeneity of Treatment Effect and Precision Medicine

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.

Clinicians' Misunderstanding of Probabilities Makes Them Like Backwards Probabilities Such As Sensitivity, Specificity, and Type I Error

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. Corollary: Since p-values are P(someone else’s data are more extreme than mine if H0 is true) and we don’t know whether H0 is true, it is a non-predictive probability that is not useful for decision making.