1. Epidemiologic Methods - Fall 2005

3. Bias in Clinical Research: Selection Bias Framework for understanding error in clinical research –systematic error: threats to internal validity (bias) –random error: sampling error (chance) Selection bias –by study design: descriptive case-control cross-sectional longitudinal cohort studies (observational or experimental)

4. Clinical Research: Sample Measure (Intervene) Analyze Infer Inference –Websters: the act of passing from sample data to generalizations, usually with calculated degrees of certainty

5. Disease Exposure + - +-+- REFERENCE/ TARGET/ SOURCE POPULATION aka STUDY BASE STUDY SAMPLE OTHER POPULATIONS

6. Disease Exposure + - +-+- San Franciscans, 20 to 65 years old SAMPLE of San Franciscans, 20 to 65 yrs old >65 years old in U.S. 20 to 65 year olds, in U.S., outside of San Francisco 20 to 65 year olds, in Europe

7. Disease Exposure + - +-+- REFERENCE/ TARGET/ SOURCE POPULATION aka STUDY BASE STUDY SAMPLE

8. The goal of any study is to find the truth, i.e.: –measure of disease occurrence in a descriptive study –measure of association between exposure and disease in an analytic study Ways of getting the wrong answer: –systematic error; aka bias any systematic process in the conduct of a study that causes a distortion from the truth in a predictable direction captured in the validity of the inference –random error; aka chance occurs because we cannot study everyone (we must sample) captured in the precision of the inference (e.g., confidence interval) Error in Clinical Research

9. Validity and Precision Good Validity Good Precision Poor Validity Poor Precision

10. Validity and Precision Poor Validity Good Precision Good Validity Poor Precision

11. Validity and Precision Poor Validity Good Precision Good Validity Poor Precision Systematic error (bias) Random error (chance) No Systematic error

12. Performing an Actual Study: You only have one shot Field of “statistics” can tell you the random error (precision) Only judgment can tell you about systematic error (validity)

13. Internal vs External Validity Internal validity –Do the results obtained from the actual subjects accurately represent the target/reference/source population? External validity (generalizability) –Do the results obtained from the actual subjects pertain to persons outside of the source population? –Internal validity is a prerequisite for external validity “Validity” to us typically means internal validity

14. Disease Exposure + - +-+- REFERENCE/ TARGET/ SOURCE POPULATION ? INTERNAL VALIDITY OTHER POPULATIONS ? EXTERNAL VALIDITY (generalizability) STUDY SAMPLE

16. MetLife Is Settling Bias Lawsuit BUSINESS/FINANCIAL DESK August 30, 2002, Friday MetLife said yesterday that it had reached a preliminary settlement of a class-action lawsuit accusing it of charging blacks more than whites for life insurance from 1901 to 1972. MetLife, based in New York, did not say how much the settlement was worth but said it should be covered by the $250 million, before tax, that it set aside for the case in February.

17. “Bias” in Webster’s Dictionary 1 : a line diagonal to the grain of a fabric; especially : a line at a 45° angle to the selvage often utilized in the cutting of garments for smoother fit 2 a : a peculiarity in the shape of a bowl that causes it to swerve when rolled on the green b : the tendency of a bowl to swerve; also : the impulse causing this tendency c : the swerve of the bowl 3 a : bent or tendency b : an inclination of temperament or outlook; especially : a personal and sometimes unreasoned judgment : prejudice c : an instance of such prejudice d (1) : deviation of the expected value of a statistical estimate from the quantity it estimates (2) : systematic error introduced into sampling or testing 4 a : a voltage applied to a device (as a transistor control electrode) to establish a reference level for operation b : a high-frequency voltage combined with an audio signal to reduce distortion in tape recording

18. Bias of Priene (600 - 540 BC) One of the 7 sages of classical antiquity Consulted by Croesus, king of Lydia, about the best way to deploy warships against the Ionians Bias wished to avoid bloodshed, so he misled Croesus, falsely advising him that the Ionians were buying horses Bias later confessed to Croesus that he had lied and that the Ionians were also building warships. Croesus was pleased with the way that he had been deceived by Bias and made peace with the Ionians. Bias = deviation from truth BMJ 2002;324:1071

19. Classification Schemes for Error Szklo and Nieto –Bias Selection Bias Information/Measurement Bias –Confounding –Chance Other Common Approach –Bias Selection Bias Information/Measurement Bias Confounding Bias –Chance

21. Selection Bias Technical definition –Bias that is caused when individuals have different probabilities of being included in the study according to relevant study characteristics: namely, the exposure and the outcome of interest Plain definition –Bias that is caused by some kind of problem in the process of selecting subjects initially or - in a longitudinal study - in the process that determines how long subjects stay in the study

22. Selection Bias in a Descriptive Study Pre-election surveys re: 1948 Presidential Election –various methods used to find subjects –largest % favored Dewey General election results –Truman beat Dewey Ushered in realization of the importance of representative (random) sampling

23. N= 894 sample Actual vote Yes 4,717,006 (55%) No 3,809,090 (45%) The San Francisco Chronicle Should Gov. Davis be recalled? Based on a survey conducted in English and Spanish among random samples of people likely to vote in California’s Oct. 7 recall election

24. Leukemia Incidence Among Observers of a Nuclear Bomb Test Caldwell et al. JAMA 1980 Smoky Atomic Test in Nevada Outcome of 76% of troops at site was later found; occurrence of leukemia determined 82% contacted by the investigators 18% contacted the investigators on their own 4.4 greater risk of leukemia than those contacted by the investigators

25. SOURCE POPULATION STUDY SAMPLE Descriptive Study: Unbiased Sampling An even dispersion of evenly weighted arrows

26. SOURCE POPULATION STUDY SAMPLE Descriptive Study: Selection Bias Uneven dispersion of unevenly weighted arrows

27. Disease Exposure + - +-+- SOURCE POPULATION STUDY SAMPLE Analytic Study: Unbiased Sampling

28. Diseased Exposed + - +-+- SOURCE POPULATION STUDY SAMPLE Analytic Study: Selection Bias

29. Selection Bias in Case-Control Studies Coffee and cancer of the pancreas MacMahon et al. N Eng J Med 1981; 304:630-3 Cases: patients with histologic diagnosis of pancreatic cancer in any of 11 large hospitals in Boston and Rhode Island between October 1974 and August 1979 What study base gave rise to these cases? How should controls be selected?

30. Selection Bias in a Case-Control Study Coffee and cancer of the pancreas MacMahon et al. N Eng J Med 1981; 304:630-3 Controls: Other patients under the care of the same physician of the cases with pancreatic cancer. Patients with diseases known to be associated with smoking or alcohol consumption were excluded

31. 207275 932 CaseControl Coffee: > 1 cup day No coffee OR= (207/9) / (275/32) = 2.7 (95% CI, 1.2-6.5) Coffee and cancer of the pancreas MacMahon et al., (N Eng J Med 1981; 304:630-3) 216 307

32. Relative to the true study base that gave rise to the cases, the: Controls were: Other patients under the care of the same physician at the time of an interview with a patient with pancreatic cancer Most of the MDs were gastroenterologists whose other patients were likely advised to stop using coffee Patients with diseases known to be associated with smoking or alcohol consumption were excluded Smoking and alcohol use are correlated with coffee use; therefore, sample is relatively depleted of coffee users

33. Cancer No cancer coffee no coffee SOURCE POPULATION STUDY SAMPLE Case-control Study of Coffee and Pancreatic Cancer: Selection Bias

34. 1410 8284 CaseControl Coffee: > 1 cup day No coffee OR= (84/10) / (82/14) = 1.4 (95% CI, 0.55 - 3.8) Coffee and cancer of the pancreas: Use of population-based controls Gold et al. Cancer 1985

35. Selection Bias in a Cross-sectional Study Inclusion of prevalent disease causes all sorts of problems Finding a diseased person in a cross-sectional study requires 2 things: –the disease occurred in the first place –the case survived long enough to be sampled Any factor associated with a prevalent case of disease might be associated with disease development, survival with disease, or both Assuming goal is to find factors associated with disease development, bias in prevalence ratio occurs any time that exposure under study is associated with survival with disease

36. Cross-Sectional Study Design

37. Selection Bias in a Cross-sectional Study e.g., Smoking and emphysema Smoking is a cause of emphysema, but persons with emphysema who continue to smoke have shorter survival Hence, in any cross-section of persons with emphysema, those who smoke less are apt to be more greatly represented (because of the survival disadvantage of those who continue to smoke) Therefore, cross-sectional study of current smoking and emphysema will result in a prevalence ratio that underestimates the entity you are presumably interested in: the incidence ratio

38. Emphysema Smoke + - +-+- SOURCE POPULATION STUDY SAMPLE Cross-sectional study of smoking and emphysema

39. Selection Bias in a Cross-Sectional Study Is glutathione S-transferase class  deletion (GSTM1-null) polymorphism associated with increased risk of breast cancer? With prevalent breast cancer cases, an association with GSTM1-null is seen depending upon the number of years since diagnosis But not with incident cases Kelsey et al. Canc Epi Bio Prev 1997

40. Selection Bias: Cohort Studies/RCTs Among initially selected subjects, selection bias less likely to occur compared to case-control or cross-sectional studies –Reason: study participants (exposed or unexposed; treatment vs placebo) are selected (theoretically) before the outcome occurs

41. Disease Exposure + - +-+- SOURCE POPULATION STUDY SAMPLE Cohort Study/RCT Since disease has not occurred yet among initially selected subjects, there is no opportunity for disproportionate sampling with respect to exposure and disease

42. Disease Exposure + - +-+- SOURCE POPULATION STUDY SAMPLE Cohort Study/RCT All that is sampled is exposure status Even if disproportionate sampling of exposed or unexposed groups occurs, it will not result in selection bias when forming measures of association

43. Selection Bias: Cohort Studies Selection bias can occur on the “front-end” of the cohort if diseased individuals are unknowingly entered into the cohort e.g.: –Consider a cohort study of effect of exercise on all-cause mortality in persons initially thought to be completely healthy. –If some participants were enrolled had undiagnosed cardiovascular disease and as a consequence were more likely to exercise less, what would the effect be on the measure of association?

44. Death No death exercise no exercise SOURCE POPULATION STUDY SAMPLE Cohort Study of Exercise and Survival Selection bias will lead to spurious protective effect of exercise (assuming truly no effect)

45. Selection Bias: Cohort Studies/RCTs Most common form of selection bias does not occur with the process of initial selection of subjects Instead, selection bias most commonly caused by forces that determine length of participation (who ultimately stays in the analysis) i.e. loss to follow-up –When those lost to follow-up have a different probability of the outcome than those who remain (i.e. informative censoring) AND –Frequency of lost to follow-up is different across exposure groups (degree of informative censoring differs across exposure groups) (i.e. when loss to follow-up is associated with both outcome and exposure) –selection bias results

46. Selection Bias: Cohort Studies/RCTs e.g., Cohort study of progression to AIDS: IDU vs homosexual men In general, getting sicker is a common reason for loss to follow-up Therefore, persons who are lost to follow-up have different AIDS incidence than those who remain (i.e., informative censoring) In general, IDU more likely to become lost to follow-up - at any given level of feeling sick Therefore, the frequency of informative censoring differs across exposure groups (IDU vs homosexual men) Results in selection bias: underestimates the incidence of AIDS in IDU relative to homosexual men

47. Effect of Selection Bias in a Cohort Study Survival assuming no informative censoring and no difference between IDU and homosexual men Effect of informative censoring in IDU group Effect of informative censoring in homosexual male group Time Probability of being AIDS-free

48. AIDS No AIDS IDU Homo- sexual men SOURCE POPULATION STUDY SAMPLE Cohort Study of HIV Risk Group and AIDS Progression Selection bias will lead to spurious underestimation of AIDS incidence in both exposure groups, more so in IDU group

49. Selection Bias in a Randomized Clinical Trial If randomization is performed correctly, then selection bias on the “front-end” of the study (i.e., differential inclusion of diseased individuals between arms) is not possible –even if diseased individuals are unknowingly included, randomization ensures that this occurs evenly across treatment groups However, beware of imposters to randomization, such as “every other participant was assigned”, that can be deciphered by participants or staff –can lead to differential distribution of diseased or high-risk participants across treatment groups

50. Selection Bias in a Clinical Trial Losses to follow-up are the big unknown in clinical trials and the major potential for selection bias e.g., Assume that: –a symptomatic side effect of a drug is more common in persons “sick” from disease –occurrence of the side effect is associated with more losses to follow-up Then: –drug treatment group would be selectively depleted of the sickest persons (i.e., informative censoring) –drug treatment group looks better overall

51. Effect of Selection Bias in an RCT Survival assuming no informative censoring and no difference between drug and placebo Effect of informative censoring in drug group Time Probability of non- disease

52. Managing Selection Bias Prevention and avoidance are critical: Study design is critical Unlike confounding where there are solutions in the analysis of the data, once the subjects are selected, there are usually no fixes for selection bias In case-control studies: –Follow the study base principle In cross-sectional studies: –Be aware of how exposure in question affects disease survival In longitudinal studies (cohorts/RCTs): –Screen for occult disease at baseline –Avoid losses to follow-up –Consider worst-case scenario sensitivity analyses

53. Extra Slides

54. Diseased Exposed + - +-+- REFERENCE/ TARGET/ SOURCE POPULATION STUDY SAMPLE ? INTERNAL VALIDITY OTHER POPULATIONS ? EXTERNAL VALIDITY (generalizability) STUDY POPULATION

55. Preventing Losses in Longitudinal Studies Select those most willing to participate (internal validity before generalizability) Obtain comprehensive contact information –SSN (critical for death index), DOB –Middle initial, father’s surname –Address –Friends and family members Engage participants while in follow-up When losses occur, contact: –postal service for change of address –DMV –National death index