Missing data

[malcolmbarrett]

Lucy's slides
https://onlinelibrary.wiley.com/doi/full/10.1002/sim.8355:

Results indicated that the Within approach produced unbiased estimates with appropriate confidence intervals, whereas the Across approach produced biased results and unrealistic confidence intervals

[malcolmbarrett]

https://journals.sagepub.com/doi/abs/10.1177/0962280210395740?journalCode=smma
https://pubmed.ncbi.nlm.nih.gov/29165572/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860553/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860387/
https://academic.oup.com/aje/article/190/4/663/5923802?searchresult=1

[malcolmbarrett]

Articles on when complete case is ok and when it is not:

https://statisticalhorizons.com/listwise-deletion-its-not-evil/
https://arxiv.org/pdf/1801.03583.pdf
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7705610/
http://people.csail.mit.edu/jrennie/trg/papers/rubin-missing-76.pdf
Figure 4 https://researchonline.lshtm.ac.uk/id/eprint/1198/1/research_online_1198.pdf

[malcolmbarrett]

it seems like null effect vs non-null effect might be relevant, perhaps by changing the dag

[malcolmbarrett]

Imputation of some sort allows you to calculate a marginal ATE instead of using a conditional with the predictors of y

[malcolmbarrett]

More notes after Lucy's in-depth analysis of this problem. We'll use her writing as a basis for this but a few big picture ideas:

• Stochastic (single imputation, norm.predict in mice) without Y works because the ratio of the covariance and variance is correct
• Full on MICE with Y also works but is more complex
• Ignorable vs nonignorable for causal. Does it induce confounding bias? Dags useful here
• If ignorable, imputation can improve precision because it increases N but model mentioned above does add variance, too, so it's kind of a wash. A little improvement

[malcolmbarrett]

callout box for why we're not using terms MAR, MNCAR, etc. Not clear if talking about causal mechanism or missingness mechanism

[malcolmbarrett]

Benefit of full MICE: CAN recover correct SE to true value

[malcolmbarrett]

MB Question: does propensity score model fitted with variables from deterministic model without Y need any different approaches?

Lucy doesn't think so but will investigate

[malcolmbarrett]

exclusion criteria + missingness: https://onlinelibrary.wiley.com/doi/10.1002/sim.9685

[malcolmbarrett]

checks for partially missing covariates: https://janickweberpals.gitlab-pages.partners.org/smdi/articles/smdi.html
course materials from a class on causal effects and missingness: Missing Data Theory and Causal Effects

[malcolmbarrett]

MI + gcomp: https://thestatsgeek.com/2023/01/31/g-formula-for-causal-inference-via-multiple-imputation/

[malcolmbarrett]

https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-024-02157-x