So Many Choices: A Guide to Selecting Among Methods to Adjust for Observed Confounders Non-randomised studies (NRS) typically assume that there are no differences in unobserved baseline characteristics between the treatment groups under comparison. Traditionally regression models have ...

読みます

onlinelibrary.wiley.com/doi/10.1002/...

今年も行けなかったけど来年は疫学会に行きたい

Constructing Inverse Probability Weights for Marginal Structural Models Abstract. The method of inverse probability weighting (henceforth, weighting) can be used to adjust for measured confounding and selection bias under the f

About Doubly robust estimator

academic.oup.com/aje/article/...

明日から新しい会社です〜

買ったぞい

一票の平等の政治経済学: 一人一人の投票価値の平等を追求する amzn.asia/d/5V6EHhe

I've been working on a history of the tidyverse for an upcoming special issue of the Australia/New Zealand Journal of Statistics celebrating 25 years of R. I'd love to get your feedback! What have I missed?

hadley.github.io/25-tidyverse...

#rstats

Professional Growth from Painful Experiences: Developing the Posttraumatic Growth Scale for Workers Mental health problems among workers are important issues. While preventing such problems is crucial, it is also imperative to understand the potential positive transformation that can arise from w...

My first paper about the Posttraumatic growth

www.tandfonline.com/doi/full/10....

Marginal Structural Models and Causal Inference in... : Epidemiology models that allow for improved adjustment of confounding in those situations. The parameters of a marginal structural model can be consistently estimated using a new class of estimators, the inverse-p...

疫学研究で marginal structural models を使う方法

journals.lww.com/epidem/fullt...