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Epidemiologic Methods - Fall 2009. Bias in Clinical Research: General Aspects and Focus on Selection Bias Framework for understanding error in clinical.

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Presentation on theme: "Epidemiologic Methods - Fall 2009. Bias in Clinical Research: General Aspects and Focus on Selection Bias Framework for understanding error in clinical."— Presentation transcript:

1 Epidemiologic Methods - Fall 2009

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3 Bias in Clinical Research: General Aspects and Focus on Selection Bias Framework for understanding error in clinical research –systematic error: threats to internal validity (bias) –random error: sampling error (chance) Selection bias (a type of systematic error) –by study design: descriptive case-control cross-sectional longitudinal 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 –All we can do is make educated guesses about the soundness of our inferences –Those who are more educated will make better guesses

5 Anyone can get an answer The challenge is to tell if it is correct

6 Disease Exposure + - +-+- REFERENCE/ TARGET/ SOURCE POPULATION aka STUDY BASE STUDY SAMPLE OTHER POPULATIONS Two types of inferences

7 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

8 Disease Exposure + - +-+- REFERENCE/ TARGET/ SOURCE POPULATION aka STUDY BASE STUDY SAMPLE Most important inference is the first one Without an accurate first inference, there is little point considering the second inference Attempts in study design to enhance the second inference are often in conflict with goal of making a sound first inference

9 The goal of any study is make an accurate (true) inference, 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 or sampling error occurs because we cannot study everyone (we must sample) direction is random and not predictable captured in the precision of the inference (e.g., SE and CI) Error in Clinical Research

10 Good Validity Good Precision Poor Validity Poor Precision Validity and Precision: Each Shot at Target Represents a Study Sample of a Given Sample Size

11 Validity and Precision Poor Validity Good Precision Good Validity Poor Precision

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

13 Performing an Actual Study: You Only Have One Shot Field of “statistics” can tell you the random error (precision) with formulae for confidence intervals Only judgment can tell you about systematic error (validity) Judgment requires substantive and methodologic knowledge

14 Disease Exposure + - +-+- REFERENCE/ TARGET/ SOURCE POPULATION ? INTERNAL VALIDITY OTHER POPULATIONS ? EXTERNAL VALIDITY (generalizability) STUDY SAMPLE Two Types of Inferences Correspond to Two Types of Validity

15 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 –“Threat to validity” = threat to internal validity –Identifying threats to validity is a critical aspect of research

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17 The goal of any study is make an accurate (true) inference, 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 a 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 or sampling error occurs because we cannot study everyone (we must sample) direction is random and not predictable captured in the precision of the inference (e.g., SE and CI) Error in Clinical Research

18 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.

19 “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

20 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. 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

21 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 “BIG 4”

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23 Emerging Terminology: “Causal Research” Goal: Identify causal relationships 6 ways a statistical association can occur 1.Chance 2.Selection bias 3.Measurement bias 4.Confounding 5.Reverse causation 6.True causal relationship Process of causal research: rule out the first 5

24 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 Easier definition –Bias that is caused by some kind of systematic problem in the process of selecting subjects initially or - in a longitudinal study - in the process that determines which subjects drop out of the study Problem caused by: –Investigators: Faulty study design –Participants: By choosing not to participate/ending participation –(or both)

25 Selection Bias in a Descriptive Study Surveys re: 1948 Presidential election –various methods used to find subjects –largest % favored Dewey General election results –Truman beat Dewey Fault: Bad Study Design Ushered in realization of the importance of representative (random) sampling

26 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 Election polls provide rare opportunity to later look at truth

27 SOURCE POPULATION STUDY SAMPLE Descriptive Study: Unbiased Sampling No Selection Bias Even dispersion of arrows

28 SOURCE POPULATION STUDY SAMPLE Descriptive Study: Biased Sampling Presence of Selection Bias Uneven dispersion of arrows e.g., Dewey backers were over- represented

29 Leukemia 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 incidence of leukemia than those contacted by the investigators Fault: Study design (look back studies are inherently limited) + the participants (especially who chose not to participate)

30 Mortality following initiation of antiretroviral therapy in Uganda In the presence of 39% loss to follow-up at 3 years Geng et al. JAMA 2008

31 Mortality following initiation of antiretroviral therapy in Uganda Accounting for losses to follow-up by tracking down vital status of a sample of the lost in the community Naive estimate Corrected estimate Selection bias

32 Disease Exposure + - +-+- SOURCE POPULATION STUDY SAMPLE Analytic Study: Unbiased Sampling No Selection Bias Given that a person resides in one of the 4 cells in the source population, the selection probability is the probability he/she will be represented in that cell in the study sample. For no selection bias to occur, selection probabilities cannot differ according to both exposure and disease

33 Diseased Exposed + - +-+- SOURCE POPULATION STUDY SAMPLE Analytic Study: Biased Sampling Presence of Selection Bias Unequal selection probability isolated to one cell: Underestimate of Exposure Effect

34 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?

35 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 without pancreatic cancer 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

36 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 Biased?

37 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 Conclusion: Controls vastly depleted of coffee users compared to true study base Fault: Investigators (Poor study design)

38 Cancer No cancer coffee no coffee SOURCE POPULATION STUDY SAMPLE Case-control Study of Coffee and Pancreatic Cancer: Selection Bias Bias: overestimate effect of coffee in causing cancer

39 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

40 Disease Exposure + - +-+- SOURCE POPULATION STUDY SAMPLE Equal selection probability in all 4 cells: No Selection Bias Selection Bias in a Cross-sectional Study: Presence of exposure and disease at outset invites selection bias

41 Disease Exposure + - +-+- SOURCE POPULATION STUDY SAMPLE Unequal selection probability: Overestimate of Effect Selection Bias in a Cross-sectional Study: Presence of exposure and disease at outset invites selection bias

42 Disease Exposure + - +-+- SOURCE POPULATION STUDY SAMPLE Unequal selection probability: Underestimate of Effect Selection Bias in a Cross-sectional Study: Presence of exposure and disease at outset invites selection bias

43 Disease Exposure + - +-+- SOURCE POPULATION STUDY SAMPLE Unequal selection probability: Overestimate of Effect Selection Bias in a Cross-sectional Study: Presence of exposure and disease at outset invites selection bias

44 Disease Exposure + - +-+- SOURCE POPULATION STUDY SAMPLE Unequal selection probability: Underestimate of Effect Selection Bias in a Cross-sectional Study: Presence of exposure and disease at outset invites selection bias

45 Disease Exposure + - +-+- SOURCE POPULATION STUDY SAMPLE Typically you don’t know the selection probabilities Selection Bias in a Cross-sectional Study: Presence of exposure and disease at outset invites selection bias ? ? ? ?

46 History of Heart Attack Hyper- lipidemia + - +-+- SOURCE POPULATION STUDY SAMPLE Selection Bias in a Cross-sectional Study: Effect of Non-Responders Austin, AJE 1981 Survey of S. California adults OR observed = 3.6 25347 452312 Overall 82% Response ? ? ? ?

47 History of Heart Attack Hyper- lipidemia + - +-+- SOURCE POPULATION Investigators made the extra effort to track down and question the initial non- responders Selection Bias in a Cross-sectional Study: Effect of Non-Responders Austin, AJE 1981 Survey of S. California adults OR true = 3.3 2807 100% 63 30 100% 401 100% CORRECTED STUDY SAMPLE Selection probability

48 History of Heart Attack Hyper- lipidemia + - +-+- SOURCE POPULATION STUDY SAMPLE Investigators made the extra effort to track down and question the initial non- responders Selection Bias in a Cross-sectional Study: Effect of Non-Responders 83%87% 83%72% Austin, AJE 1981 Survey of S. California adults OR biased = 3.6 OR true = 3.3 25347 452312 2807 100% 63 30 100% 401 100% CORRECTED STUDY SAMPLE Response %Selection bias

49 Effect of unequal response probabilities in a cross-sectional study Group ExposureOutcome Bias in OR due to non- response MenFamily h/o MIHeart failure+63% HypertensionStroke-32% WomenFamily h/o strokeStroke+59% Family h/o diabetesStroke-34% Austin, AJE 1981 Survey of S. California adults Fault: The Participants (Study design is fine)

50 Another Mechanism for Selection Bias in Cross-sectional Studies Finding a diseased person in a cross-sectional study requires 2 things: –the disease occurred in the first place –person survived long enough to be sampled Any factor found 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 (etiologic research), bias in prevalence ratio occurs any time that exposure under study is associated with survival with disease

51 Cross-Sectional Study Design

52 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, an association with GSTM1-null is seen depending upon the number of years since diagnosis But not with brand new incident diagnoses Kelsey et al. Canc Epi Bio Prev 1997

53 Breast Cancer GSTM1 + - null SOURCE POPULATION STUDY SAMPLE Cross-sectional study of GSTM1 polymorphism and breast cancer pos. Bias: overestimate effect of GSTM-1 null polymorphism in causing breast cancer Fault: Study design

54 Selection Bias: Cohort Studies/RCTs Among initially selected subjects, selection bias “on the front end” 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

55 Disease Exposure + - +-+- SOURCE POPULATION STUDY SAMPLE Cohort Study/RCT At the outset, since disease has not occurred yet among initially selected subjects, there is typically no opportunity for disproportionate sampling with respect to exposure and disease. (We cannot yet draw the 4 arrows)

56 Disease Exposure + - +-+- SOURCE POPULATION STUDY SAMPLE Cohort Study/RCT All that is sampled is exposure status (the “margins”) Even if disproportionate sampling of exposed or unexposed groups occurs, it will not result in selection bias when forming measures of association A + B C + D a + b c + d

57 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 happen to the measure of association?

58 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)

59 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; losses) –When those lost to follow-up have a different probability of the outcome than those who remain (i.e. informative censoring) in at least one of the exposure groups AND –Rate of informative censoring differs across exposure groups Selection bias results

60 Selection Bias: Cohort Studies e.g., Cohort study of progression to AIDS: IDU vs homosexual men All the ingredients are present: Informative censoring is present –getting sick is a common reason for loss to follow-up –persons who are lost to follow-up have greater AIDS incidence than those who remain (i.e., informative censoring) Informative censoring is differential across exposure groups –IDU more likely to become lost to follow-up - at any level of feeling sick –i.e., the magnitude of informative censoring differs across exposure groups (IDU vs homosexual men) Result: selection bias -- underestimates the incidence of AIDS in IDU relative to homosexual men

61 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 men group Time Probability of being AIDS-free Selection bias

62 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 Fault: The Participants (Study design is fine)

63 Effect of losses to follow-up in a cohort study Bisson, PLoSOne, 2008 Naively Ignoring Losses Tracking Down Vital Status on Losses Determinants of survival after initiation of antiretroviral therapy in Africa

64 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 (other than by chance) –even if diseased individuals are unknowingly included, randomization typically ensures that this occurs evenly across treatment groups

65 Selection Bias in a Clinical Trial Losses to follow-up are the big unknown in clinical trials and the major potential cause of selection bias e.g., Assume that: –a symptom-causing side effect of a drug is more common in persons “sick” from the disease under study –occurrence of the side effect is associated with more losses to follow-up Then: –Compared to placebo, drug treatment group would be selectively depleted of the sickest persons (i.e., informative censoring) –Would make drug treatment group appear better

66 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

67 Managing Selection Bias Prevention and avoidance are critical –Unlike confounding where there are solutions in the analysis of the data, once the subjects are selected and their follow-up occurs, there are usually no easy fixes for selection bias In case-control studies: –Follow the study base principle In cross-sectional studies: –Strive for high response percentages –Be aware of how exposure in question affects disease survival In longitudinal studies (cohorts/RCTs): –Screen for occult disease/precursors at baseline –Avoid losses to follow-up –Consider approaches to tracking down the lost


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