![• “We can never finally prove our scientific
theories, we can merely (provisionally)
confirm or (conclusively) refute them.”
– - Karl Popper
Sir Karl Raimund Popper CH FBA FRS[4] (28 July 1902 – 17 September 1994) was an Austrian-British[5]
philosopher and professor at the London School of Economics.[6] He is generally regarded o regarded as
one of the greatest philosophers of science of the 20th century.[7][8] Popper is known for his rejection of the
classical inductivist views on the scientific method, in favour of empirical falsification: regarded as one of the
greatest philosophers of science of the 20th century.[7][8] (wikipedia.com)
2014 Page 5](https://image.slidesharecdn.com/1-160809065815/85/4-1-introduction-5-320.jpg)
![2
BMJ 2004;329:868-869 (16 October)
2014 Page 13
Why is confounding so important in
epidemiology?
● BMJ Editorial: “The scandal of poor epidemiological
research” [16 October 2004]
● “Confounding, the situation in which an apparent
effect of an exposure on risk is explained by its
association with other factors, is probably the
most important cause of spurious associations in
observational epidemiology.”](https://image.slidesharecdn.com/1-160809065815/85/4-1-introduction-13-320.jpg)
4.1. introduction
- 1. Threats to Validity from Confounding and Effect Modification • Overview: Random vs. systematic error • Confounding • Effect Modification • Logistic regression (time permitting) • Special thanks for some of the materials in these lecture: – Professor Jen Ahern (UCB) – Professor Madhu Pai (McGilll—a former 250b GSI) 2014 Page 1 1
- 2. 2014 Page 2 1 The cardinal rule of epidemiology • Remember that all results based on epidemiology studies are likely to be …
- 3. 2014 Page 3 The cardinal rule of epidemiology (continued) • WRONG… – unless proper care has been taken to eliminate all sources of error in the estimate (…and sometimes even then the results will be wrong because of unknown sources of error) 2
- 4. 2014 Page 4 Example: Confounding • A colleague with outside funding believes that cigarette smoke is not a “cause” (in any sense) of lung cancer but that exposure to matches (yes, matches) is the cause. This colleague has conducted a large case control study to test the null hypothesis: Ho: “Matches are not associated with lung cancer”. • What’s the rationale (in the Popperian sense) for stating the null hypothesis rather than the alternative: HA: “Matches are associated with lung cancer”. • What does the colleague hope to do (in terms of hypothesis testing) • What do you think of the term “associated” –would it be better to write “a cause of”?
- 5. • “We can never finally prove our scientific theories, we can merely (provisionally) confirm or (conclusively) refute them.” – - Karl Popper Sir Karl Raimund Popper CH FBA FRS[4] (28 July 1902 – 17 September 1994) was an Austrian-British[5] philosopher and professor at the London School of Economics.[6] He is generally regarded o regarded as one of the greatest philosophers of science of the 20th century.[7][8] Popper is known for his rejection of the classical inductivist views on the scientific method, in favour of empirical falsification: regarded as one of the greatest philosophers of science of the 20th century.[7][8] (wikipedia.com) 2014 Page 5
- 6. 2014 Page 6 10 Confounding: smoking, matches, and lung cancer • Your colleague has located 1000 cases of lung cancer, of whom 820 carry matches. • Among 1000 reference patients (selected randomly from a population with recently taken normal chest x-rays), 340 carry matches. • Strengths of the reference selection process? Weaknesses? • Describe the relationship between matches and lung cancer in your colleague’s data. • Would you like to analyze the data in any other fashion?
- 7. 2014 Page 7 Confounding: smoking, matches, and lung cancer • Odds ratio = (820 * 660) / (180 * 340) • OR = 8.8 • 95% CI (7.2, 10.9) Cancer No cancer Matches 820 340 No matches 180 660
- 8. 2014 Page 8 Confounding: smoking, matches, and lung cancer • You decide to look at the relationship between matches and lung cancer in the smokers separately from the non- smokers. • You find that among the 1000 cases, 900 are smokers and 810 (of the 900) carry matches • Among the 1000 reference patients, 300 are smokers and 270 (of the 300) carry matches • Calculate the relevant measure(s) of effect. • What should your colleague do about future funding?
- 9. Confounding: smoking, matches, and lung cancer • ORpooled = 8.84 (7.2, 10.9) • ORsmokers = 1.0 (0.6, 1.5) • ORnonsmokers = 1.0 (0.5, 2.0) Pooled Cancer No cancer 820 180 Cancer 810 340 660 No cancer 270 Matches No Matches Smokers Matches No Matches Non-smoker Matches No Matches 2014 Page 9 90 Cancer 10 90 30 No cancer 70 630 13
- 10. 2014 Page 10 Confounding: smoking, matches, and lung cancer • To be complete, you also decide to examine the relationship between smoking and lung cancer. • What tables should you construct to do this? 14
- 11. Confounding: smoking, matches, and lung cancer ’ • ORpooled = 21.0 (16.3, 27.1) • ORmatches = 21.0 (10.5, 46.2) • ORno matches = 21.0 (12.9, 34.7) • Discuss your intuitions about the 95% CI s Pooled Cancer No cancer Cancer 810 Smoking No 900 300 Smoking 100 700 No cancer 270 Matches Smoking No Smoking No matches Smoking No Smoking 2014 Page 11 10 Cancer 90 90 70 No cancer 30 630 16
- 12. Confounder? ? ? ? Unadjusted RR Exposure Disease ? Adjusted RR 19 2014 Page 12
- 13. 2 BMJ 2004;329:868-869 (16 October) 2014 Page 13 Why is confounding so important in epidemiology? ● BMJ Editorial: “The scandal of poor epidemiological research” [16 October 2004] ● “Confounding, the situation in which an apparent effect of an exposure on risk is explained by its association with other factors, is probably the most important cause of spurious associations in observational epidemiology.”
- 14. Overview 2014 Page 14 3 ● Causality is the central concern of epidemiology ● Confounding is the central concern with establishing causality ● Confounding can be understood using multiple different approaches ● A strong understanding of various approaches to confounding and its control is essential for all those who engage in health research