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 fear but 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. What can be wrong in the study? Random error Results in low precision of the epidemiological measure  measure is not precise, but true 1 Imprecise measuring 2 Too small groups 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 Study size Systematic error (bias) Random error (chance)

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

11. Bias in epidemiology 1 Concept of bias 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. Definition: Systematic, non-random deviation of results and inferences from the truth. Bias:

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

16. VALIDITY OF EPIDEMIOLOGIC STUDIES Reference Population Study Population External Validity ExposedUnexposed 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 χ 2 =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 χ 2 =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 χ 2 =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 χ 2 =13.33, P<0.01 OR=3

31. B case control sum exposed 80 100 180 unexposed 40 100 140 sum 120 200 320 χ 2 =8.47 P<0.01 OR=2.0

32. （ 3 ） non-respondent bias

33. Survey skills to sensitive question Abortion

34. yes no 1 2 2 1

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

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

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

38. （ 4 ） detection signal bias 50%

39. Early stage Terminal stage Medium stage

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

41. Intake estrogenUterus bleed Frequently check Early findout

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

44. 2.2 Information Bias

45. （ 1 ） recalling bias

47. （ 2 ） report bias

48. （ 3 ） diagnostic/exposure suspicion bias

49. (4) Measurement bias

50. 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.

51. Properties of a Confounder: A confounding factor must be a risk factor for the disease. The confounding factor must be associated with the exposure under study in the source population. A confounding factor must not be affected by the exposure or the disease. The confounder cannot be an intermediate step in the causal path between the exposure and the disease.

52. 2.3.2 Control of confounding bias 1 ) restriction 2) randomization 3) matching 1 In designing phase

53. 2 In analysis phase 1) Stratified analysis (Mantal-Hazenszel’s method) 2) Standardized 3) logistic analysis

54. A case-control study of Oral contraceptive to myocardial infarction OC MI control sum + 29 135 164 - 205 1607 1812 sum 234 1742 1976 χ 2 =5.84,P<0.05 cOR=1.68 OR 95C.I.(1.10,2.56)

55. Is age a potential confounding factor?

56. Age distribution in 2 group age （ year ） MI proportion （ % ） case proportion （ % ） OR 25~ 6 2.6 286 16.4 1.0 30~ 21 9.0 423 24.3 2.36 35~ 37 15.8 356 20.4 4.95 40~ 71 30.3 371 21.3 9.12 45~49 99 42.3 306 17.6 15.42 合计 234 100.0 1742 100.0 ----

57. OC exposure proportion in different age groups （ % ） OC exposure in MI Age （ year ） + - sum exposure Proportion(%) OC exposure in control + - sum exposure Proportion(%) 25~ 4 2 6 66.7 62 224 286 21.7 30~ 9 12 21 42.9 33 390 423 7.8 35~ 4 33 37 10.8 26 330 356 7.3 40~ 6 65 71 8.5 9 362 371 2.4 45~49 6 93 99 6.1 5 301 306 1.6 sum 29 205 234 12.4 135 1607 1742 7.7 χ 2 =38.99 P<0.01 χ 2 =108.43 P<0.01

58. Stratified analysis age （ year ） OCMIControlOR 25~ + 4 62 - 2 224 OR95%C.I. 7.2 (1.64,31.65) 30~ + 9 33 - 12 390 8.9 (3.96,19.98) 35~ + 4 26 - 33 330 1.5 (0.53,4.24) 40~ + 6 9 - 65 362 3.7 (1.36,10.04) 45~49 + 6 5 - 93 301 3.9 (1.26,12.10)

59. Woolf’s Chi-square test

60. χ 2 =6.212 P<0.05, ν=5-1=4 Incorporate OR

61. OR MH =3.97

62. Analytic epidemiology : Case-control study; HIV “carried” by mosquitoes ? HIV + Controls Mosquito exposure No exposure O.R. = 5.38

63. Analytic epidemiology : stratification for confounding ; Case-control study. HIV “carried” by mosquitoes ? No exposure Mosquito Exposure Females HIV + 3 2 166 133 304 Males HIV + 155 15 controls 81 10 261 Mosquito Exposure O.R. = 1.21 O.R. = 1.27