October 18, 2021

An explanation of p-values, effect sizes and more

1 - p-values, misconceptions and different approaches

There are a lot of critics on how scientists have been using p-values. In the blog post below they discuss more about the definitions of p-values and possible alternatives to its usage.

P-values Are Tough And S-values Can Help | Less Likely

An extensive discussion about what P-values are, their properties, common interpretations, misinterpretations, and how a measure called an S-value may better help us interpret them.

2 - Effect sizes or p-values?

On the other hand, several other people claim that p-values receive too much attention and effect sizes are left behind. Below the author discusses more about the implications of using p-values without considering the effect sizes.

arg min | Ben Recht | Substack

arg min: a blog of minimum value. on the history, foundations, and validity of "optimally" automated decision making. Click to read arg min, by Ben Recht, a Substack publication with thousands of subscribers.

3 - Tuning parameters in small and sparse datasets. How good is it?

In the paper below the authors study the effects of ridge regression and its parameters complexities in small and sparse datasets. They describe possible problems and in the end they provide some recommendations on how to deal with these cases. One point worth noting is the specification of priors to the parameters to stabilize the estimates.

To tune or not to tune, a case study of ridge logistic regression in small or sparse datasets | BMC Medical Research Methodology | Full Text

Background For finite samples with binary outcomes penalized logistic regression such as ridge logistic regression has the potential of achieving smaller mean squared errors (MSE) of coefficients and predictions than maximum likelihood estimation. There is evidence, however, that ridge logistic regression can result in highly variable calibration slopes in small or sparse data situations. Methods In this paper, we elaborate this issue further by performing a comprehensive simulation study, inves...

4 - To umap or not to umap?

Recently several researchers have been discussing the validity of umap in single cell RNA-seq datasets. In the following papers and threads, researchers debate some technical issues and whether one should use or not umap.

The links below should be read in sequence, as they represent the flow of the debate.

5 - Integrating Clinical Omics in Precision Management of Breast Cancer

This week there will be a virtual meeting discussing the current and future challenges of using omics datasets for biomarker and clinical diagnostic assays development in breast cancer. The registration fee to participate in both days is 120 pounds.

https://www.royalmarsden.nhs.uk/clinical-omics-technologies-precision-management-cancer