Posts Tagged ‘why0mg’

Confounders, Mediators, or Colliders: What Types of Shared Covariates Does a Sibling Comparison Design Control For? – PubMed – NCBI

Sunday, January 13th, 2019

https://www.ncbi.nlm.nih.gov/pubmed/28575894/

Estimating the Size of Treatment Effects

Sunday, January 13th, 2019

Estimating the Size of Treatment Effects
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2791668/

Randomized controlled trial – Wikipedia

Sunday, January 13th, 2019

https://en.m.wikipedia.org/wiki/Randomized_controlled_trial

Confounding: What it is and how to deal with it – ScienceDirect

Sunday, January 13th, 2019

https://www.sciencedirect.com/science/article/pii/S0085253815529748

Low birth-weight paradox – Wikipedia

Sunday, January 13th, 2019

https://en.wikipedia.org/wiki/Low_birth-weight_paradox

Quantification of collider-stratification bias and the birthweight paradox. – PubMed – NCBI

Sunday, January 13th, 2019

https://www.ncbi.nlm.nih.gov/pubmed/19689488

QT:{{”
Recently, causal diagrams have been used to illustrate the possibility for collider-stratification bias in models adjusting for birthweight. When two variables share a common effect, stratification on the variable representing that effect induces a statistical relation between otherwise independent factors.
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Simpson’s paradox – Wikipedia

Sunday, January 13th, 2019

https://en.wikipedia.org/wiki/Simpson‘s_paradox

popular account of Simpson’s paradox

Sunday, January 13th, 2019

Martin Gardner wrote a popular account of Simpson’s paradox in his March 1976 Mathematical Games column in Scientific American. ==
http://flowcytometry.sysbio.med.harvard.edu/files/flowcytometryhms/files/herzenbergfacshistory.pdf#129

Collider (epidemiology) – Wikipedia

Sunday, January 13th, 2019

https://en.wikipedia.org/wiki/Collider_(epidemiology)

How to control confounding effects by statistical analysis

Sunday, January 13th, 2019

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4017459/