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Epidemiologic Methods - Fall 2009

Epidemiologic Methods - Fall 2009. 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)

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Epidemiologic Methods - Fall 2009

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  1. Epidemiologic Methods - Fall 2009

  2. 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)

  3. 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

  4. Anyone can get an answer • The challenge is to tell if it is correct

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

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

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

  8. Error in Clinical Research • 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)

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

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

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

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

  13. ? EXTERNAL VALIDITY (generalizability) OTHER POPULATIONS Inference Disease + - + - Inference ? INTERNAL VALIDITY Exposure REFERENCE/ TARGET/ SOURCE POPULATION Two Types of InferencesCorrespond to Two Types of Validity STUDY SAMPLE

  14. Two Types of InferencesCorrespond to Two Types of 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 • “Threat to validity” = threat to internal validity • Identifying threats to validity is a critical aspect of research

  15. Error in Clinical Research • 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)

  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 fit2 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 bowl3 a: bent or tendencyb: 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 bestway to deploy warships against the Ionians • Bias wished to avoid bloodshed, so he misled Croesus, falselyadvising him that the Ionians were buying horses • Bias later confessed to Croesusthat he had lied. • Croesus was pleased with the way that he had been deceived byBias 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 “BIG 4”

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

  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 • 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)

  22. 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

  23. N= 894 sample Actual vote The San Francisco Chronicle Should Gov. Davis be recalled? Yes 4,717,006 (55%) No 3,809,090 (45%) Election polls provide rare opportunity to later look at truth 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. Descriptive Study: Unbiased Sampling No Selection Bias Even dispersion of arrows SOURCE POPULATION STUDY SAMPLE

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

  26. 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)

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

  28. 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 Corrected estimate Selection bias Naive estimate

  29. Analytic Study: Unbiased Sampling No Selection Bias Disease 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. + - + - Exposure SOURCE POPULATION For no selection bias to occur, selection probabilities cannot differ according to both exposure and disease STUDY SAMPLE

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

  31. 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?

  32. 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

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

  34. 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)

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

  36. 84 82 10 14 • Coffee and cancer of the pancreas: • Use of population-based controls • Gold et al. Cancer 1985 Case Control Coffee: > 1 cup day No coffee OR= (84/10) / (82/14) = 1.4 (95% CI, 0.55 - 3.8)

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

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

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

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

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

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

  43. Selection Bias in a Cross-sectional Study:Effect of Non-Responders History of Heart Attack + - + - Hyper-lipidemia ? ? SOURCE POPULATION ? 25 347 ? Overall 82% Response 45 2312 Austin, AJE 1981 Survey of S. California adults OR observed = 3.6 STUDY SAMPLE

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

  45. Selection Bias in a Cross-sectional Study:Effect of Non-Responders History of Heart Attack Investigators made the extra effort to track down and question the initial non-responders + - + - 30 401 Hyper-lipidemia 100% 100% 63 2807 100% 100% CORRECTED STUDY SAMPLE SOURCE POPULATION OR true = 3.3 25 347 83% 87% Response % Selection bias 45 2312 Austin, AJE 1981 Survey of S. California adults 72% 83% OR biased = 3.6 STUDY SAMPLE

  46. Effect of unequal response probabilities in a cross-sectional study Fault: The Participants (Study design is fine) Austin, AJE 1981 Survey of S. California adults

  47. 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

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