References¶
NOTE: You are not expected to read all of these references! It is intended as a list of resources that you may find useful in the future or if you wish to follow-up on some of the topics discussed in more detail.
Bandoli G., Palmsten K., Chambers C.D., et al. Revisiting the Table 2 fallacy: A motivating example examining preeclampsia and preterm birth. Pediatric and Perinatal Epidemiology 2018; 32: 390-397.
D’Agostino R.B., Vasan R.S., Pencina M.J., et al. General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study. Circulation 2008; 117: 743–753.
Etminan M, Collins GS, Mansournia MA. Using Causal Diagrams to Improve the Design and Interpretation of Medical Research. CHEST 2020; 158: Supplement S21-S28.
Greenland S., Pearl J., Robins J.M. Causal diagrams for epidemiological research. Epidemiology 1999; 10:37–48.
Hand D. What is the Purpose of Statistical Modelling? Harvard Data Science Review 2019 https://doi.org/10.1162/99608f92.4a85af74
Hernan M.A. Does water kill? A call for less casual causal inferences. Annals of Epidemiology 2016; 26: 674-680.
Hernan M.A. The C-Word: Scientific Euphemisms Do Not Improve Causal Inference From Observational Data. Am J Public Health. 2018;108: 616–619.
Hernan M.A., Hsu J., Healy B.. A second chance to get causal inference right: a classification of data science tasks. Chance 2019; 32: 42-49.
Huebner M., le Cessie S., Schmidt C., Wach W. A Contemporary Conceptual Framework for Initial Data Analysis. Observational Studies 2019; 4: 171-192.
Riley R.D. et al. Prognosis Research in Healthcare: Concepts, Methods, and Impact. 2019. Oxford University Press.
Schmueli. To explain or to predict? Statistical Science 2010; 25: 289-310.
Schooling CM, Jones H. Clarifying questions about “risk factors”: predictors versus explanation. Emerging Themes in Epidemiology 2018; 15: 10.
Schrier I., Platt R.W. Reducing bias through directed acyclic graphs. BMC Medical Research Methodology 2008; 8: 70.
Spiegelhalter D. The Art of Statistics: Learning from Data. 2019. Penguin.
Steyerberg E. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. 2nd Edition. 2019. Springer.
Tennant PWG, Harrison WJ, Murray EJ, et al. Use of directed acyclic graphs (DAGs) in applied health research: review and recommendations. MedRxiv 2019. https://www.medrxiv.org/content/10.1101/2019.12.20.19015511v1
VanderWeele T.J., Shpitser I. On the definition of a confounder. Annals of Statistics 2013; 41: 196-220.
Westreich D., Greenland S. The Table 2 Fallacy: Presenting and Interpreting Confounder and Modifier Coefficients. American Journal of Epidemiology 2013; 177: 292-298.