Use of statistical tools in epidemiology

1. Use of statistical tools in epidemiology Hamisu Salihu, MD, PhD PGY3 Preventive Medicine Residency Emory University

2. Causal association • Strength of association • Consistency • Dose-response relationship • Biological plausibility • Temporal relationship • Coherence • Specificity

3. Threats to Epidemiologic Studies

4. “CBC” of research evaluation • Could the findings be due to Chance? • Could the findings be due to Bias? • Could the findings be due to Confounding?

5. Could the Findings be due to Chance?

6. Chance • Association was a fluke due to random variability • P value: likelihood that the observed results could have occurred by chance alone ( probability of type 1 error) • P <0.05 conventionally used; probability of <5% or 1 in 20 of obtaining observed result by chance • Recall: Type 1 and Type 2 error

7. Chance • Results of statistical tests for the role of chance are dependent upon: • Sample size (type 2 error) • Variability in the population • The number of comparisons

8. Chance • Multiple analyses of subgroups within the same data set may produce spurious associations, since p value indicates the opportunity of findings arising by chance • Probability of finding by chance if the alpha level is 0.05 is 1- (1-alpha)k where k = number of analyses

9. Could the Findings be due to Confounding?

10. Confounding • A distortion of the true relationship between an exposure and a given outcome, resulting from a mutual relationship with one or more extraneous factors • Confounder: variable that is related to both the exposure and the outcome and that serves as a bridge to meditate an apparent association between exposure and outcome where no real association exists (or it exists at a smaller magnitude); or, alternatively it may mask a real association* Morton, Habel and Mc Carter

11. Confounding CONFOUNDING FACTOR EXPOSURE OF INTEREST OUTCOME OF INTEREST

12. Criteria for Confounding (need both) • The potential confounder must be associated with the outcome of interest • The confounder is an actual risk factor for the outcome • The confounder affects the likelihood of recognizing the outcome • The potential confounder must be associated with the exposure of interest, but not be a result of the exposure (should not be an intermediary in the causal pathway)

13. Confounding CONFOUNDING FACTOR EXPOSURE OF INTEREST OUTCOME OF INTEREST Example: Postmenopausal estrogen use and hip fracture. Is age a confounder? Yes. Age is associated with postmenopausal estrogen use (the older less likely). Age is also a risk factor for hip fractures. Adjust for age.

14. Confounding CONFOUNDING FACTOR EXPOSURE OF INTEREST OUTCOME OF INTEREST Example: Lung cancer and coffee drinking. Is smoking a confounder? Yes. Smoking is associated with coffee intake (smokers are more likely to drink coffee). Smoking is also a risk factor for lung cancer. Adjust for smoking.

15. When is a Factor Not a Confounder? • If the individual’s status regarding the confounder is a result of the exposure under study, or the confounder is an intermediary in the causal pathway • If the individual’s status regarding the confounder is a result of the disease under study • If the confounder is essentially measuring the same thing as the exposure • If the association between the confounder and the outcome of interest is thought to be due to chance

16. Not Confounding CONFOUNDING FACTOR Confounder and outcome not associated EXPOSURE OF INTEREST OUTCOME OF INTEREST Example: Postmenopausal estrogen use and hip fracture. Is breast self exam (BSE) a confounder? No. BSE may be associated with postmenopausal estrogen use (estrogen users may be more likely to do BSE), but BSE does not cause/prevent a hip fracture.

17. Not Confounding CONFOUNDING FACTOR Confounder and exposure not associated EXPOSURE OF INTEREST OUTCOME OF INTEREST Example: HTN and heart disease. Is history of rheumatic heart dx (RHD) a confounder? No. RHD may be a risk factor for ht dx but it is not associated with HTN.

18. Not Confounding CONFOUNDING FACTOR Confounder causes the exposure EXPOSURE OF INTEREST OUTCOME OF INTEREST Example: Hyperplasia and breast cancer. Is estrogen use a confounder? No. Hyperplasia may be a result of the estrogen.

19. Not Confounding CONFOUNDING FACTOR Exposure causes the confounder EXPOSURE OF INTEREST OUTCOME OF INTEREST Example: Low fat diet and heart disease. Is cholesterol a confounder? No. A low fat diet usually results in a lower cholesterol level.

20. Not Confounding CONFOUNDING FACTOR Outcome causes the confounder EXPOSURE OF INTEREST OUTCOME OF INTEREST Example: Smoking and lung cancer Is weight loss a confounder? No. Lung cancer often results in weight loss. Outcome causes the confounder

21. How do you Detect Confounding? • Determine if the potential confounder is associated with both exposure and outcome • Adjust for the potential confounder in analyzing the data • If there is a difference between the adjusted and the unadjusted estimate of the effect of the exposure, then the potential confounder is a true confounder

22. How do You Control for Confounding? Design stage: • Restriction: study only subjects in a given category • Matching: match individuals for comparison on the basis of their status regarding the confounder • Use a RCT design: randomly assign participants to exposed and unexposed groups

23. How do You Control for Confounding? Analysis stage: • Report stratum-specific estimates: list the effect of the exposure on the outcome for each level of the confounder • Statistical methods: • rate-adjustment • Mantel-Hanzel methods • regression analysis (modeling)

24. Effect Modification • Effect modifier: a variable that changes/alters the association between two other variables (exposure and outcome)* • Not a confounder, since it is not associated with the exposure • How to tell the difference between the two: • OR or RR will change when adjusted for a confounder but will not change when adjusted for an effect modifier • There will be different levels of risk for each level of the modifier Morton, Hable and McCarter

25. Effect Modification • RR for cobalt exposure and development of cardiac disease (cardiomyopathy) is 3.0 • When adjusted for gender (M/F), RR remains the same (3.0) • When RRs are computed separately for gender: • RR men = 3.8 • RR women = 2.2 • Gender is therefore an effect modifier of the association between cobalt exposure and cardiomyopathy

26. In Summary… CONFOUNDING Confounding Factor EXPOSURE OF INTEREST OUTCOME OF INTEREST EFFECT MODIFICATION Effect Modifier EXPOSURE OF INTEREST OUTCOME OF INTEREST

27. Could the Findings be Due to Bias?

28. Bias • Anything that produces systematic error in a research finding* • Leads to an incorrect estimate of an association • Threatens internal and external validity • No study is immune to bias; some are more susceptible than others • Selection, information and misclassification bias Vogt 1993

29. Selection Bias Selection of subjects based on other axis of interest (exposure outcome) • Case-control studies: selection of cases and controls based on likelihood that they may have been exposed e.g., looking for COPD cases among charts of known smokers • Cohort studies: selection of exposed and unexposed subjects based on likelihood that they may develop outcome of interest

30. Selection Bias • Non-respondent bias (volunteer bias, self selection bias): characteristics differ between respondents and non-respondents • Losses to follow up: subjects who drop out often differ from those who complete the study • Membership bias: members healthier than general population e.g., finding that mortality of active workers is less than that of the population as a whole was due to a self-selection bias (“healthy worker effect”) Fox and Collier, 1976

31. Information Bias Systematic error in the collection of data in the study • Recall bias: subjects with outcome (or exposure) recall events in greater detail or amplify their frequency or importance. • Example: A study is performed asking mothers who have delivered children with congenital abnormalities about exposure to x-rays. Their responses are compared to those of other women of the same age

32. Information Bias • Interviewer bias: knowledge of study hypothesis may lead to differing structure or presentation of questions • Observer bias: preconceived ideas as to results or outcome of examination

33. Information Bias • Surveillance bias: exposed (or diseased) participants may be more closely followed than referents/controls/unexposed • Hawthorne effect: those being watched or studied may modify their behaviors or act differently as a consequence of the observation process

34. Differential Misclassification • When the proportion of subjects misclassified on exposure depends on disease status or when the proportion of subjects misclassified on disease depends on exposure • i.e., subjects are misclassified based on their exposure/outcome • Can bias in either direction

35. Non-differential Misclassification • When the proportion of subjects misclassified on exposure does not depend on disease status or when the proportion of subjects misclassified on disease does not depend on exposure • i.e., subjects are misclassified at random • Direction of bias is usually toward the null

36. Direction of Bias

37. Differential Misclassification • Cohort study to compare incident rates of emphysema among smokers and non-smokers. If smokers seek medical attention to a greater degree than nonsmokers then emphysema might be diagnosed more frequently among smokers than nonsmokers • In a retrospective study of anti-nausea medication use and the risk of congenital malformations, mothers of infants with deformities and mothers of normal infants were interviewed within 24 hours of delivery regarding medications taken during the first semester. Mothers with congenitally malformed babies will be more likely to recall anti-nausea medication use

38. Non-differential Misclassification • In a retrospective study of mothers’ childhood vitamin A consumption and the risk of congenital malformations, mothers of infants with deformities and mothers of normal infants were interviewed about their childhood nutritional intake. There is an equal likelihood mothers in both groups will not correctly recall childhood Vitamin A intake.

39. Control of Bias In study design • Choice of study groups • Use second control group • Data collection • Objective, standardized, close ended questions • Use memory aids • Trained interviewers • Blind interviewers to subjects’ diseases status • Persons determining diagnosis should be blind to exposure status

40. Control of Bias In analysis/manuscript • Difficult to adjust for these types of biases • Can model inputted data to determine possible extent of some types of biases • Describe the likely direction of the bias • Collect information on non-respondents and those lost to follow up

41. Control of Bias in RCTs • Proper randomization: eliminates selection bias • Exclusion bias: analyze data by intention to treat • Double or triple blindprotocol: blinding eliminates observation (detection) bias • Include placebo group

42. THANKS