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)

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The cardinal rule of epidemiology

• Remember that all results based on epidemiology studies are likely to be …

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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)

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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”?

• “We can never finally prove our scientific theories, we can merely (provisionally) confirm or (conclusively) refute them.”– - Karl PopperSir 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 asone 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)

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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?

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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 340No matches 180 660

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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?

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 CancerNo

cancer

820180Cancer810

340660No cancer270

Matches No Matches Smokers Matches

No Matches Non-smoker Matches

No Matches2014 Page 9

90Cancer10

90

30No cancer70

630 13

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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?

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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 300Smoking 100 700

No cancer 270

Matches Smoking

No Smoking No matches Smoking No Smoking

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10Cancer90

90

70No cancer30

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Confounder?

? ?

? Unadjusted RRExposure Disease

? Adjusted RR

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BMJ 2004;329:868-869 (16 October)

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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.”

Overview

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● 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