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Unit 5: Evaluating Associations

Unit 5: Evaluating Associations. Unit 5 Learning Objectives: Understand the concepts of “valid” and “causal” statistical associations. Understand the possible explanations for statistical associations: --- Chance --- Bias --- Confounding

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Unit 5: Evaluating Associations

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  1. Unit 5: Evaluating Associations

  2. Unit 5 Learning Objectives: • Understand the concepts of “valid” and “causal” statistical associations. • Understand the possible explanations for statistical associations: • --- Chance • --- Bias • --- Confounding • Distinguish between the major types of bias in epidemiologic studies. • Distinguish between “internal validity” and “external validity.”

  3. Unit 5 Learning Objectives (cont.): • Understand the concept of confounding and methods to assess its presence. • Understand the concept of causality, including necessary and sufficient causes. • Understand pros and cons of guidelines used to evaluate causal associations in epidemiology.

  4. Assigned Readings: Textbook (Gordis): Chapter 14, pages 206-222 (from association to causation) Chapter 15, pages 224-230 (bias and confounding)

  5. Evaluating Associations If we observe an exposure/disease association, we must consider: 1. Is the association valid? (do the study findings reflect the true relationship between the exposure and disease?) 2. Is the association causal? (Is there sufficient evidence to infer that a causal association exists between the exposure and the disease?)

  6. Evaluating Associations EVALUATING THE VALIDITY OF AN ASSOCATION: In any epidemiologic study, there are at least 3 possible explanations for the observed results: 1. CHANCE 2. BIAS 3. CONFOUNDING These explanations are not mutually exclusive -- more than one can be present in the same study

  7. CHANCE The “luck of the draw” 1. Rarely can we study an entire population, so inference is attempted from a sample of the population 2. There will always be random variation from sample to sample 3. In general, smaller samples have less precision, reliability, and statistical power (more sampling variability)

  8. CHANCE 4. A test of “statistical significance” is performed to assess the degree to which the data are compatible with the null hypothesis of no association 5. The “p-value” reflects the probability that the test statistic (e.g. t-statistic or chi- square statistic) produced from the data would be greater than or equal to the observed value under the null hypothesis of no association

  9. CHANCE 6. By convention, if p < 0.05, then the association between the exposure and disease is considered to be “statistically significant.” (e.g. we reject the null hypothesis (H0) and accept the alternative hypothesis (H1)) What does p < 0.05 mean? Indirectly, it means that we suspect that the magnitude of effect observed (e.g. risk ratio) is not due to chance alone (in the absence of biased data collection or analysis)

  10. CHANCE Example: Possible biased coin Coin Toss - 10 Times:ObservedExpected Heads 7 5 Tails 3 5 Odds H:T 7:3 = 2.33 Excess Heads = O - E = 7 - 5 = 2 p-value: > 0.05 (computation not shown) The observed excess of heads to tails is not much greater than that which might be expected by chance

  11. CHANCE Example: Possible biased coin Coin Toss – 1,000 Times:ObservedExpected Heads 700 500 Tails 300 500 Odds H:T 700:300 = 2.33 Excess Heads = O - E = 700 - 500 = 200 p-value: < 0.05 (computation not shown) The observed excess of heads to tails is much greater than that which might be expected by chance

  12. CHANCE BEWARE: The p-value reflects both the magnitude of the difference between the study groups AND the sample size So, a related, but more informative measure known as the confidence interval (CI) is also calculated. CI = a range of values within which the true population value falls, with a certain degree of assurance (probability).

  13. CHANCE HYPOTHETICAL EXAMPLE OF 95% CONFIDENCE INTERVAL Exposure: Caffeine intake (high versus low) Outcome: Incidence of breast cancer Risk Ratio: 1.32 (point estimate) p-value: 0.14 (not statistically significant) 95% C.I.: 0.87 - 1.98 _____________________________________________ 0.0 0.5 1.0 1.5 2.0 (null value) 95% confidence interval

  14. CHANCE INTERPRETATION: Our best estimate is that women with high caffeine intake are 1.32 times (or 32%) more likely to develop breast cancer compared to women with low caffeine intake. However, we are 95% confident that the true value (risk) of the population lies between 0.87 and 1.98 (assuming an unbiased study). _____________________________________________ 0.0 0.5 1.0 1.5 2.0 (null value) 95% confidence interval

  15. CHANCE So, if the 95% confidence interval does NOT include the null value of 1.0 (p < 0.05), then we declare a “statistically significant” association. Question: How do we declare a “valid” statistical association? Answer: By ruling out, to the extent possible, bias or confounding

  16. CHANCE Note: Although we have initially reviewed the role of chance, the conventional sequence would be to first assess (rule out) the presence of systematic bias. In other words, it makes no sense to evaluate chance as a possible explanation when a study is biased from the start (i.e. the “experiment” we have set up is flawed, and hence, we should discard it).

  17. BIAS • BIAS: Systematic error in the design, conduct, or analysis of a study that results in a mistaken estimate of an exposure/disease relationship • SELECTION BIAS • INFORMATION BIAS • * Interviewer • * Recall Bias • * Reporting Bias • * Surveillance Bias

  18. BIAS SELECTION BIAS: Any systematic error that arises in the process (mechanism) of identifying the study populations (i.e. the two 2 study groups to be compared) • Occurs when selection of study subjects (whether by exposure or disease status) is based on different criteria related to exposure or disease • Results in the study groups being non-comparable, unless some type of statistical adjustment can be made

  19. SELECTION BIAS EXAMPLE:Case Control Study Outcome: Hemorrhagic stroke Exposure: Appetite suppressant products that contain Phenylpropanolamine (PPA) Cases: Persons who experienced a stroke Controls: Persons in the community without stroke Bias: Control subjects were recruited by random-digit dialing from 9:00 AM to 5:00 PM. This resulted in over- representation of unemployed persons who may not represent the study base in terms of use of appetite suppressant products.

  20. SELECTION BIAS EXAMPLE:Retrospective Cohort Study Outcome: COPD Exposure: Employment in tire manufacturing Exposed: Plant assembly line workers Non-exposed: Plant administrative personnel Bias: The exposed were contacted (selected) at a local pub while watching Monday night football; the non-exposed were identified through review of plant personnel files. Exposed persons may have been more likely to be smokers (related to COPD)

  21. SELECTION BIAS EXAMPLE:Non-Response • If refusal or non-response is related to exposure, the estimate of effect (exposure/disease) may be biased. For example, if controls are selected by use of a household survey, non-response may be related to demographic and lifestyle factors associated with employment. • Responders often differ systematically from persons who do not respond.

  22. SELECTION BIAS NOTE: • Restrictive sampling alone, so long as different criteria are not used between study groups, does not confer selection bias • It merely means that the study results may not generalize to the larger population (external validity)

  23. INFORMATION BIAS Definition: Systematic differences in the way in which data on exposure and outcome are obtained from the various study groups. Some Types/Sources of Information Bias: • Bias in abstracting records • Bias in interviewing • Bias from surrogate interviews • Surveillance bias • Reporting and recall bias

  24. INTERVIEWER BIAS DEFINITION: Systematic difference in the soliciting, recording, or interpretation of information from study participants • Can affect every type of epidemiologic study • May occur when interviewers are not “blinded” to exposure or outcome status of participants.

  25. INTERVIEWER BIAS • Interviewer’s knowledge of subjects’ disease status may result in differential probing of exposure history • Similarly, interviewer’s knowledge of subjects’ exposure history may result in differential probing and recording of the outcome under examination • Placebo control is one method used to maintain observer blindness in randomized trials.

  26. RECALL BIAS DEFINITION: Study group participants systematically differ in the way data on exposure or outcome are recalled • Particularly problematic in case-control studies • Individuals who have experienced a disease or adverse health outcome may tend to think about possible “causes” of the outcome. This can lead to differential recall

  27. RECALL BIAS EXAMPLE:Case-Control Study Outcome: Cleft palate Exposure: Systemic infection during pregnancy Cases: Mothers giving birth to children with cleft palate Controls: Mothers giving birth to children free of cleft palate Bias: Mothers who have given birth to a child with cleft palate may recall more thoroughly colds and other infections experienced during pregnancy

  28. REPORTING BIAS DEFINITION: Selective suppression or revealing of information such as past history of sexually transmitted disease. • Often occurs because subject reluctance to report an exposure due to attitudes, beliefs, and perceptions • “Wish bias” may occur among subjects who have developed a disease and seek to show that the disease “is not their fault.”

  29. SURVEILLANCE BIAS • If a population is monitored over a period of time, disease ascertainment may be better in the monitored population than in the general population (“surveillance bias”). • May lead to biased estimate of exposure/disease relationship.

  30. MISCLASSIFICATION DEFINITION: Erroneous classification of the exposure or disease status of an individual into a category to which it should not be assigned Example: --- Cases incorrectly classified as controls --- Controls incorrectly classified as cases --- Exposed incorrectly classified as non- exposed --- Non-exposed incorrectly classified as exposed

  31. MISCLASSIFICATION Non-differential misclassification: The proportion of subjects misclassified on exposure does not depend on disease status OR The proportion of subjects misclassified on disease does not depend on exposure status

  32. MISCLASSIFICATION Non-differential misclassification: • Tends to make the exposure or disease groups more similar than they really are • Some non-differential misclassification is inevitable • Almost always results in bias towards the null • In interpretation, researcher must consider what real effect might have been obscured

  33. MISCLASSIFICATION Differential misclassification: Classification error of exposure status occurs more frequently among the diseased or non-diseased OR Classification error of disease status occurs more frequently among the exposed or non-exposed

  34. MISCLASSIFICATION Differential misclassification: • Results in relatively unpredictable effects • Can exaggerate or underestimate the true exposure/disease relationship • By chance (infrequently), can also result is estimate that is the same as the true exposure/disease relationship.

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