smartdata-analysis-and-statistics / precmed

A doubly robust precision medicine approach to estimate and validate conditional average treatment effects
https://smartdata-analysis-and-statistics.github.io/precmed/
Apache License 2.0
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Revise Count-examples #47

Closed NightlordTW closed 1 year ago

NightlordTW commented 1 year ago

It would be helpful to restructure the example section as follows:

  1. Introduction of the example (please add a baseline table)
  2. Estimation of avarage treatment effect (atefit) => introduce concept of confounding adjustment (formula PS with logistic regression), prognostic prediction (formula for y; use of Poisson regression; and some explanaition why we are adjusting for prognostic covariates when estimating the ATE), need for bootstrapping when estimating standard error.
  3. Estimation of individual treatment effect (catefit) . Explain what kind of ITE model is being estimated; how do we allow for treatment-covariate interaction?
  4. Internal validation (catecv): Why do we need it? What can we compare? What is the output?

Source: https://github.com/smartdata-analysis-and-statistics/precmed/blob/main/vignettes/Count-examples.Rmd

StanWijn commented 1 year ago

Resolved in recent commits