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Bias in Epidemiology

Bias in Epidemiology. Wenjie Yang ywjie@zzu.edu.cn 2007.12. “The search for subtle links between diet, lifestyle, or environmental factors and disease is an unending source of fear but often yields little certainty.” ____ Epidemiology faces its limits.

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Bias in Epidemiology

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  1. Bias in Epidemiology Wenjie Yang ywjie@zzu.edu.cn 2007.12

  2. “The search for subtle links between diet, lifestyle, or environmental factors and disease is an unending source of fearbut often yields little certainty.” ____Epidemiology faces its limits. Science 1995; 269: 164-169.

  3. Residential Radon—lung cancer Sweden Yes Canada No

  4. DDT metabolite in blood stream Breast Cancer Abortion Maybe yes,maybe no

  5. Electromagnetic fields(EMF) Canada & France: Leukemia America: Brain Cancer

  6. Random error Results in low precision of the epidemiological measure  measure is not precise, but true 1 Imprecise measuring 2 Too small groups What can be wrong in the study? Systematic errors(= bias) Results in low validity of the epidemiological measure  measure is not true 1 Selection bias 2 Information bias 3 Confounding

  7. Random errors

  8. Systematic errors

  9. Errors in epidemiological studies Error Random error (chance) Systematic error (bias) Study size

  10. Random error • Low precision because of • Imprecise measuring • Too small groups • Decreases with increasing group size • Can be quantified by confidence interval

  11. 1 Concept of bias Bias in epidemiology 2 Classification and controlling of bias 2.1 selective bias 2.2 information bias 2.3 confounding bias

  12. Overestimate? Underestimate?

  13. Random error： Definition Deviation of results and inferences from the truth, occurring only as a result of the operation of chance.

  14. Bias: Definition: Systematic, non-random deviation of results and inferences from the truth.

  15. 2 Classification and controlling of bias Time Assembling subjects collecting data analyzing data Selection bias Information bias Confounding bias

  16. VALIDITY OF EPIDEMIOLOGIC STUDIES Reference Population External Validity Study Population Exposed Unexposed Internal Validity

  17. 2.1 Selection bias 2.1.1 definition Due to improper assembling method or limitation, research population can not represent the situation of target population, and deviation arise from it. 2.1.2 several common Selection biases

  18. （1）Admission bias （Berkson’s bias) There are 50,000 male citizen aged 30-50 years old in a community. The prevalence of hypertension and skin cancer are considerably high. Researcher A want to know whether hypertension is a risk factor of lung cancer and conduct a case-control study in the community .

  19. case control sum Hypertension 1000 9000 10000 No hypertension 4000 36000 40000 sum 5000 45000 50000 χ2 =0 OR=(1000×36000)/(9000 ×4000)=1

  20. Researcher B conduct another case-control study in hospital of the community.(chronic gastritis patients as control) .

  21. No association between hypertension and chronic gastritis

  22. admission rate Lung cancer & hypertension 20% Lung cancer without hypertension 20% chronic gastritis & hypertension 20% chronic gastritis without hypertension 20%

  23. case control sum hypertension 200 (1000) 200 (2000) 400 No hypertension 800 (4000) 400 (8000) 1200 sum 1000 (5000) 600 (10000) 1600

  24. case control sum hypertention 40 100 140 No hypertention 160 200 360 sum 200 300 500 χ2 =10.58 P<0.01 OR=(40×200)/(100×160)=0.5

  25. (2)prevalence-incidence bias（Neyman’s bias)

  26. Risk factor A Prognostic B

  27. A case control sum exposed 50 25 75 unexposed 50 75 125 sum 100 100 200 χ2 =13.33, P<0.01 OR=3

  28. Risk Factor A Prognostic Factor B

  29. Risk Factor A Prognostic Factor B

  30. A case control sum exposed 50 25 75 unexposed 50 75 125 sum 100 100 200 χ2 =13.33, P<0.01 OR=3

  31. B case control sum exposed 80 100 180 unexposed 40 100 140 sum 120 200 320 χ2 =8.47 P<0.01 OR=2.0

  32. （3）non-respondent bias

  33. Survey skills to sensitive question Abortion

  34. Abortion yes no 1 2 2 1

  35. number of subjects:N proportion of red ball:A numbers who’s answer is “1”:K Abortion rate: X Abortion Yes No 1 2 2 1

  36. number of subjects:N=1000 proportion of red ball:A=40% numbers who’s answer is “1”:K=540 Abortion rate: X=? Abortion Yes No 1 2 2 1 N*A *X+ N*(1-A) *(1-X)=K

  37. （4）detection signal bias Intake estrogen Endometrium cancer

  38. （4）detection signal bias 50% 50%

  39. Early stage Medium stage Terminal stage

  40. Early stage:90% Medium stage:30% Terminal stage 5% 50%

  41. Intake estrogen Uterus bleed Early findout Frequently check

  42. （5）susceptibility bias： E Physical check drop out UE

  43. 2.2 Information Bias

  44. （1）recalling bias

  45. （2）report bias

  46. （3）diagnostic/exposure suspicion bias

  47. (4) Measurement bias

  48. 2.3 Confounding bias • Definition: • The apparent effect of the exposure of interest is distorted because the effect of an extraneous factor is mistaken for or mixed with the actual exposure effect.

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