/explore
|
You are here 
|
hbiostat.org | ||
| | | | |
kgoldfeld.github.io
|
|
| | | | | Simulates data sets in order to explore modeling techniques or better understand data generating processes. The user specifies a set of relationships between covariates, and generates data based on these specifications. The final data sets can represent data from randomized control trials, repeated measure (longitudinal) designs, and cluster randomized trials. Missingness can be generated using various mechanisms (MCAR, MAR, NMAR). | |
| | | | |
www.robertkubinec.com
|
|
| | | | | Ordered beta regression can give you comparable, scale-free ATEs that can still be understood in the scale of the original data-all without using logs. | |
| | | | |
fharrell.com
|
|
| | | | | 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. | |
| | | | |
blog.fastforwardlabs.com
|
|
| | | By Chris and Melanie. The machine learning life cycle is more than data + model = API. We know there is a wealth of subtlety and finesse involved in data cleaning and feature engineering. In the same vein, there is more to model-building than feeding data in and reading off a prediction. ML model building requires thoughtfulness both in terms of which metric to optimize for a given problem, and how best to optimize your model for that metric! | ||
