Epidemiologic Methods- Fall 2002

Bias in Clinical Research: Selection and Measurement Bias Framework for threats to validity (bias) Selection bias –by study design: descriptive case-control cross-sectional longitudinal studies (cohort or experimental) Measurement bias –exposure vs. outcome –non-differential vs. differential

Internal vs External Validity Validity –before, for measurements: accuracy of evaluation of individual traits or characteristics –today, for entire studies: accuracy of inferences about populations Internal validity –Do the results obtained from the actual subjects accurately represent the target population? External validity (aka generalizability) –Do the results obtained from the actual subjects pertain to persons outside of the target population? –Internal validity is a prereq for external validity

Diseased Exposed REFERENCE/ TARGET/ SOURCE POPULATION STUDY SAMPLE INTERNAL VALIDITY OTHER POPULATIONS EXTERNAL VALIDITY

The goal of any study is to find the truth Ways of missing the truth (getting the wrong answer): –Bias Any systematic process that results in incorrect estimate of: –measure of disease (or exposure) occurrence in a descriptive study –measure of association between exposure and disease in an analytic study –Chance Random error –type I –type II Threats to Validity in Clinical Research

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 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.

“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 by selecting or encouraging one outcome or answer over others 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

Classification Schemes for “Ways of Getting the Wrong Answer” Szklo and Nieto –Bias Selection Bias Information/Measurement Bias –Confounding –Chance Other Common Approach –Bias Selection Bias Information/Measurement Bias Confounding Bias –Chance

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 participate in the study

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

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

REFERENCE/ TARGET/ SOURCE POPULATION STUDY SAMPLE Descriptive Study: Unbiased Sampling

REFERENCE/ TARGET/ SOURCE POPULATION STUDY SAMPLE Descriptive Study: Selection Bias

Diseased Exposed REFERENCE POPULATION STUDY SAMPLE Analytic Study: Unbiased Sampling

Diseased Exposed REFERENCE POPULATION STUDY SAMPLE Analytic Study: Selection Bias

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 the Boston and Rhode Island between October 1974 and August 1979 What study base gave rise to these cases? How should controls be selected?

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

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

Relative to the study base that gave rise to the cases, the: Controls: 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

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

Selection Bias in a Cross-sectional Study Inclusion of prevalent cases 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

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 really interested in: the incidence ratio

Emphysema Smoke REFERENCE/ TARGET POPULATION STUDY SAMPLE Cross-sectional study of smoking and emphysema

Selection Bias: Cohort Studies/RCTs Among initially selected subjects, selection bias much 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

Diseased Exposed REFERENCE 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 E_EE_E

Diseased Exposed REFERENCE POPULATION STUDY SAMPLE Cohort Study/RCT All that is sampled is exposure status Even if disproportionate sampling occurs, it will not result in selection bias when forming measures of association E_EE_E

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 the effects of exercise on all-cause mortality among 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?

Death No death exercise no exercise REFERENCE POPULATION STUDY SAMPLE Cohort Study of Exercise and Survival Selection bias will lead to spurious protective effect of exercise

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 –this probability is different across exposure groups –selection bias results

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 loss to follow-up - at any given level of feeling sick Therefore, the degree 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

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

AIDS No AIDS IDU Homo- sexual men REFERENCE POPULATION STUDY SAMPLE Cohort Study of HIV Risk Group and AIDS Progression Selection bias will lead to spurious underestimation of AIDS incidence in IDU group

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, 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

Measurement Bias Definition –bias that is caused when the information collected about or from subjects is inaccurate (invalid; erroneous) any type of variable: exposure, outcome, or confounder –aka: misclassification bias; information bias (text); identification bias misclassification is the immediate result

Definition of Terms Related to Measurement Accuracy Sensitivity –the ability of a test (measurement) to identify correctly those who have the characteristic (disease or exposure) of interest. Specificity –the ability of a test (measurement) to identify correctly those who do NOT have the characteristic of interest

Causes for Misclassification Participant recall Ambiguous questions Under or overzealous interviewers Problems in biological specimen question Faulty instruments Data management problems

Diseased Exposed REFERENCE/ TARGET POPULATION STUDY SAMPLE Non-Differential Misclassification of Exposure Problems with sensitivity - independent of disease status Problems with specificity - independent of disease status

Non-differential Misclassification of Exposure Truth: No misclassification (100% sensitivity/specificity) ExposureCasesControls Yes5020 No5080 OR= (50/50)/(20/80) = 4.0 Presence of 70% sensitivity in exposure classification ExposureCasesControls Yes50-15=3520-6=14 No50+15=6580+6=86 OR= (35/65)/(14/86) = 3.3 Effect of non-differential misclassification of 2 exposure categories: Bias the OR toward the null value of 1.0

Diseased Exposed REFERENCE/ TARGET POPULATION STUDY SAMPLE Non-Differential Misclassification of Exposure: Imperfect Sensitivity Problems with sensitivity

Diseased Exposed REFERENCE/ TARGET POPULATION STUDY SAMPLE Non-Differential Misclassification of Exposure Problems with sensitivity - independent of disease status Problems with specificity - independent of disease status

Non-Differential Misclassification of Exposure: Imperfect Sensitivity and Specificity Exposure Cases Controls Yes5020 No5080 True OR = (50/50) / (20/80) = 4.0 True Cases Controls Distribution exp unexp exp unexp (gold standard) Study distribution: Cases Controls Exposed Unexposed sensitivity or specificity Exposure Cases Controls Yes5534 No4566 Observed OR = (55/45) / (34/66) =2.4 REFERENCE/ TARGET POPULATION Study Sample

Non-differential Misclassification of Exposure: Magnitude of Bias on the Odds Ratio Assume True OR= Observed ORPrev of Exp in controls SpecificitySensitivity

Diseased Exposed REFERENCE/ TARGET POPULATION STUDY SAMPLE Non-Differential Misclassification of Outcome Problems with sensitivity - independent of exposure status Problems with specificity - independent of exposure status

Non-differential Misclassification of Outcome: Magnitude of Bias on the Odds Ratio Assume True OR= Observed ORPrev of Exp in controls SpecificitySensitivity

Special Situation In a Cohort or Cross-sectional Study Misclassification of outcome If specificity of outcome measurement is 100% Any degree of imperfect sensitivity, if non-differential, will not bias the risk ratio or prevalence ratio e.g. Worth knowing about when choosing cutoff for continuous variables on ROC curves: choose most specific cutoff Truth 70% sensitivity

Differential Misclassification of Exposure Weinstock et al. AJE 1991 Nested case-control study with Nurses Health Study Cases: women with new melanoma diagnoses Controls: women w/out melanoma - by incidence density sampling Measurements: questionnaire about “tanning ability”; administered shortly after melanoma development

Question asked after diagnosis Question asked before diagnosis (NHS baseline)

Diseased Exposed REFERENCE/ TARGET POPULATION STUDY SAMPLE “Tanning Ability” and Melanoma Imperfect specificity - mostly in cases

Differential Misclassification of Exposure: Magnitude of Bias on the Odds Ratio Assume True OR=3.9

Misclassification: Summary of Effects Dichotomous exposure and outcome Multi-level exposure and/or outcome –more complicated and less predictable –e.g. non-differential misclassification can lead to bias away from null

Poor Reproducibility Poor Validity Good Reproducibility Good Validity Managing Measurement Bias Prevention and avoidance are critical If true sensitivity/specificity are known, complex back-calculation techniques exist that can be used in the analysis phase Optimize the reproducibility/validity of your measurements!

Selection Bias in a Clinical Trial Losses to follow-up are the big unknown in clinical trials and the major potential for selection bias If: – 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 –drug overall looks better