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Is it causal, is it prediction or is it neither?

This document discusses the differences between explanatory models, predictive models, and descriptive models. Explanatory models aim to understand causal relationships by examining regression coefficients and testing theories. Predictive models focus on predicting future observations without considering causality, and addressing overfitting. Descriptive models simply capture data structures. While the goals differ, the document notes problems like generalizability and model misspecification are common challenges. It provides examples of epidemiological and medical prediction models and emphasizes the need for external validation of predictive performance.

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Is it causal, is it prediction or is it neither?
Maarten van Smeden, Department of Clinical Epidemiology,
Leiden University Medical Center, Leiden, Netherlands
Seminar Erasmus School of Health Policy & Management
June 24 2019
Is it causal, is it prediction or is it neither?
Nature's survey of 1,576 researchers
3
Nature news (May 25, 2016) https://www.nature.com/news/1-500-scientists-lift-the-lid-on-reproducibility-1.19970
Cookbook review
4
Schoenfeld & Ioannidis, Am J Clin Nutr 2013, DOI: 10.3945/ajcn.112.047142
“We selected 50 common ingredients from random
recipes of a cookbook”
Cookbook review
veal, salt, pepper spice, flour, egg, bread, pork, butter, tomato,
lemon, duck, onion, celery, carrot, parsley, mace, sherry, olive,
mushroom, tripe, milk, cheese, coffee, bacon, sugar, lobster,
potato, beef, lamb, mustard, nuts, wine, peas, corn, cinnamon,
cayenne, orange, tea, rum, raisin, bay leaf, cloves, thyme, vanilla,
hickory, molasses, almonds, baking soda, ginger, terrapin
5
Schoenfeld & Ioannidis, Am J Clin Nutr 2013, DOI: 10.3945/ajcn.112.047142
Studies relating the ingredients to cancer: 40/50
veal, salt, pepper spice, flour, egg, bread, pork, butter, tomato,
lemon, duck, onion, celery, carrot, parsley, mace, sherry, olive,
mushroom, tripe, milk, cheese, coffee, bacon, sugar, lobster,
potato, beef, lamb, mustard, nuts, wine, peas, corn, cinnamon,
cayenne, orange, tea, rum, raisin, bay leaf, cloves, thyme, vanilla,
hickory, molasses, almonds, baking soda, ginger, terrapin
6
Schoenfeld & Ioannidis, Am J Clin Nutr 2013, DOI: 10.3945/ajcn.112.047142

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Is it causal, is it prediction or is it neither?

  • 1. Is it causal, is it prediction or is it neither? Maarten van Smeden, Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, Netherlands Seminar Erasmus School of Health Policy & Management June 24 2019
  • 3. Nature's survey of 1,576 researchers 3 Nature news (May 25, 2016) https://www.nature.com/news/1-500-scientists-lift-the-lid-on-reproducibility-1.19970
  • 4. Cookbook review 4 Schoenfeld & Ioannidis, Am J Clin Nutr 2013, DOI: 10.3945/ajcn.112.047142 “We selected 50 common ingredients from random recipes of a cookbook”
  • 5. Cookbook review veal, salt, pepper spice, flour, egg, bread, pork, butter, tomato, lemon, duck, onion, celery, carrot, parsley, mace, sherry, olive, mushroom, tripe, milk, cheese, coffee, bacon, sugar, lobster, potato, beef, lamb, mustard, nuts, wine, peas, corn, cinnamon, cayenne, orange, tea, rum, raisin, bay leaf, cloves, thyme, vanilla, hickory, molasses, almonds, baking soda, ginger, terrapin 5 Schoenfeld & Ioannidis, Am J Clin Nutr 2013, DOI: 10.3945/ajcn.112.047142
  • 6. Studies relating the ingredients to cancer: 40/50 veal, salt, pepper spice, flour, egg, bread, pork, butter, tomato, lemon, duck, onion, celery, carrot, parsley, mace, sherry, olive, mushroom, tripe, milk, cheese, coffee, bacon, sugar, lobster, potato, beef, lamb, mustard, nuts, wine, peas, corn, cinnamon, cayenne, orange, tea, rum, raisin, bay leaf, cloves, thyme, vanilla, hickory, molasses, almonds, baking soda, ginger, terrapin 6 Schoenfeld & Ioannidis, Am J Clin Nutr 2013, DOI: 10.3945/ajcn.112.047142
  • 7. Increased/decreased risk of developing cancer: 36/40 veal, salt, pepper spice, flour, egg, bread, pork, butter, tomato, lemon, duck, onion, celery, carrot, parsley, mace, sherry, olive, mushroom, tripe, milk, cheese, coffee, bacon, sugar, lobster, potato, beef, lamb, mustard, nuts, wine, peas, corn, cinnamon, cayenne, orange, tea, rum, raisin, bay leaf, cloves, thyme, vanilla, hickory, molasses, almonds, baking soda, ginger, terrapin 7 Schoenfeld & Ioannidis, Am J Clin Nutr 2013, DOI: 10.3945/ajcn.112.047142
  • 8. 8 Cartoon of Jim Borgman, first published by the Cincinnati Inquirer and King Features Syndicate April 27 1997
  • 10. Published 43 articles on statistical significance testing (vol73,2019)
  • 11. https://bit.ly/2KyLXxo (winner VWN publication prize for best science journalism article in 2018) Read 19 peer reviewed articles using data from Dutch cohort studies: 15 had serious limitations
  • 13. 13 Credits to Peter Tennant for identifying this example
  • 15. To explain or to predict? Explanatory models • Theory: interest in regression coefficients • Testing and comparing existing causal theories • e.g. aetiology of illness, effect of treatment Predictive models • Interest in (risk) predictions of future observations • No concern about causality • Concerns about overfitting and optimism • e.g. prognostic or diagnostic prediction model Descriptive models • Capture the data structure 15 Shmueli, Statistical Science 2010, DOI: 10.1214/10-STS330
  • 16. To explain or to predict? Explanatory models • Theory: interest in regression coefficients • Testing and comparing existing causal theories • e.g. aetiology of illness, effect of treatment Predictive models • Interest in (risk) predictions of future observations • No concern about causality • Concerns about overfitting and optimism • e.g. prognostic or diagnostic prediction model Descriptive models • Capture the data structure 16 A L Y exposure outcome confounder Shmueli, Statistical Science 2010, DOI: 10.1214/10-STS330
  • 17. Causal effect estimate 17 What would have happened with a group of individuals had they received some treatment or exposure rather than another?
  • 18. 18
  • 19. Causal effect estimate 19 What would have happened with a group of individuals had they received some treatment or exposure rather than another?
  • 23. Observational study: diet -> diabetes, age 23 Age No diabetes Diabetes No diabetes Diabetes RR < 50 years 19 1 37 3 1.50 ≥ 50 years 28 12 12 8 1.33 Total 47 13 49 11 0.88 Traditional Exotic diet 50% 40% 30% 20% 10% ≥ 50 years > 50 years Total Diabetes risk < 50 years Numerical example adapted from Peter Tennant with permission: http://tiny.cc/ai6o8y
  • 24. Observational study: diet -> diabetes, weight loss 24 Weight No diabetes Diabetes No diabetes Diabetes RR Lost 19 1 37 3 1.50 Gained 28 12 12 8 1.33 Total 47 13 49 11 0.88 Traditional Exotic diet 50% 40% 30% 20% 10% Gained wt Lost wt Total Diabetes risk < 50 years Numerical example adapted from Peter Tennant with permission: http://tiny.cc/ai6o8y
  • 27. 12 RCTs; 52 nutritional epidemiology claims 0/52 replicated 5/52 effect in the opposite direction 27 Young & Karr, Significance, 2001, DOI: 10.1111/j.1740-9713.2011.00506.x
  • 28. But… 28 Ellie Murray (Jul 13 2018): https://twitter.com/EpiEllie/status/1017622949799571456
  • 29. 29
  • 30. To explain or to predict? Explanatory models • Theory: interest in regression coefficients • Testing and comparing existing causal theories • e.g. aetiology of illness, effect of treatment Predictive models • Interest in (risk) predictions of future observations • No concern about causality • Concerns about overfitting and optimism • e.g. prognostic or diagnostic prediction model Descriptive models • Capture the data structure 30 Shmueli, Statistical Science 2010, DOI: 10.1214/10-STS330
  • 31. Apgar 31 Apgar, JAMA, 1958. doi: 10.1001/jama.1958.03000150027007
  • 32. Risk estimation example: SCORE 32 Conroy, European Heart Journal, 2003. doi: 10.1016/S0195-668X(03)00114-3
  • 33. 33
  • 37. 1961 37 James & Stein. Proceedings of the fourth Berkeley symposium on mathematical statistics and probability. Vol. 1. 1961.
  • 38. 38 Efron & Morris Scientific American, 1977
  • 39. 39 Efron & Morris Scientific American, 1977
  • 43. Prediction model landscape >110 models for prostate cancer (Shariat 2008) >100 models for Traumatic Brain Injury (Perel 2006) 83 models for stroke (Counsell 2001) 54 models for breast cancer (Altman 2009) 43 models for type 2 diabetes (Collins 2011; Dieren 2012) 31 models for osteoporotic fracture (Steurer 2011) 29 models in reproductive medicine (Leushuis 2009) 26 models for hospital readmission (Kansagara 2011) >25 models for length of stay in cardiac surgery (Ettema 2010) >350 models for CVD outcomes (Damen 2016) • Few prediction models are externally validated • Predictive performance often poor 43
  • 44. 44
  • 45. To explain or to predict? Explanatory models • Theory: interest in regression coefficients • Testing and comparing existing causal theories • e.g. aetiology of illness, effect of treatment Predictive models • Interest in (risk) predictions of future observations • No concern about causality • Concerns about overfitting and optimism • e.g. prognostic or diagnostic prediction model Descriptive models • Capture the data structure 45 Shmueli, Statistical Science 2010, DOI: 10.1214/10-STS330
  • 46. To explain or to predict? Explanatory models • Causality • Understanding the role of elements in complex systems • ”What will happen if….” Predictive models • Forecasting • Often, focus is on the performance of the forecasting • “What will happen ….” Descriptive models • “What happened?” 46 Require different research design and analysis choices • Confounding • Stein’s paradox • Estimators
  • 47. Problems in common (selection) • Generalizability/transportability • Missing values • Model misspecification • Measurement and misclassification error 47 https://osf.io/msx8d/ preprint
  • 48. 48
  • 49. Two hour tutorial to R (free): www.r-tutorial.nl Repository of open datasets: http://mvansmeden.com/post/opendatarepos/ 49