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Unit 7: Effect Measure Modification And Intervention Studies

Unit 7: Effect Measure Modification And Intervention Studies. Unit 7 Learning Objectives: Understand the concept of “effect measure modification”. Employ methods to investigate effect measure modification on both additive and multiplicative scales.

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Unit 7: Effect Measure Modification And Intervention Studies

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  1. Unit 7: Effect Measure Modification And Intervention Studies

  2. Unit 7 Learning Objectives: • Understand the concept of “effect measure modification”. • Employ methods to investigate effect measure modification on both additive and multiplicative scales. • Recognize the differences between observational and experimental studies. • Distinguish between therapeutic and preventive intervention studies. • Understand design features of randomized clinical trials.

  3. Unit 7 Learning Objectives: • Recognize ethical issues in clinical trials. • Recognize the role of Institutional Review Boards in clinical trials. • Understand the use of random allocation, factorial designs, and cross-over designs in experimental epidemiology. • Understand the use of blinding (masking) in the conduct of experimental studies. • Understand the impact of non-participation, compliance, and attrition of subjects in experimental studies.

  4. Assigned Readings: Textbook (Gordis): Chapter 15, pages 233-238 (Interaction) Chapter 7, Randomized Trials Chapter 8, Randomized Trials: some further issues Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. New England Journal of Medicine 2002; 346:393-403.

  5. Introduction to Effect Measure Modification

  6. Effect Measure Modification Effect Measure Modification: The magnitude or direction of an association varies according to levels of a third factor. Also called: • “Effect Modification” • “Interaction” Note: Unlike confounding, effect measure modification should be described and reported, rather than controlled.

  7. Effect Measure Modification Hypothesis:High alcohol consumption is associated with larynx cancer (cohort study) RR = (30 / 200) / (15 / 315) RR = 3.15 •Persons with high alcohol consumption appear to be at 3.15 times higher risk of developing larynx cancer than persons without high alcohol consumption. However, is this elevated risk similar among smokers and non-smokers?

  8. Effect Measure Modification NON-SMOKERS SMOKERS RR = (4 / 53) / (6 / 156) RR = 1.96 RR = (26 / 147) / (9 / 159) RR = 3.12 Does smoking modify the relationship between alcohol consumption and larynx cancer?

  9. Effect Measure Modification CRUDE RRCA = 3.15 STRATA 1 RRNS = 1.96 STRATA 2 RRSM = 3.12 Unlike the assessment of confounding, the crude estimate is NOT USED to evaluate the presence of effect measure modification. Instead, the stratum-specific estimates are compared directly to see if they are different (heterogeneous). This example suggests “risk-ratio heterogeneity.”

  10. Effect Measure Modification Keep in mind that the presence of “effect measure modification” depends on which measure of effect is evaluated (e.g. risk difference, risk ratio, etc.). The RD is on an additive scale. The RR is on a multiplicative scale. Let’s look at RD and RR separately.

  11. Effect Measure Modification Expected additive = (0.075 + 0.057) – 0.038 = 0.094 Expected multiplicative = 1.96 x 1.47 = 2.89

  12. Effect Measure Modification In this example, it appears that smoking modifies (increases) both the risk difference and risk ratio between alcohol consumption and larynx cancer.

  13. Effect Measure Modification Hypothesis:Female gender is associated with depression (cohort study) RR = (100 / 280) / (18 / 233) RR = 4.62 • Females appear to be at 4.62 times higher risk of depression than males. However, is this elevated risk similar among young persons and older persons?

  14. Effect Measure Modification YOUNG OLD RD = RD = RR = RR =

  15. Effect Measure Modification YOUNG OLD RD = (6 / 54) - (6 / 150) RD = 0.111 – 0.040 = 0.071 RD = (94 / 226) - (12 / 83) RD = 0.416 – 0.145 = 0.271 RR = (6 / 54) / (6 / 150) RR = 0.111 / 0.040 = 2.78 RR = (94 / 226) / (12 / 83) RR = 0.416 / 0.145 = 2.88

  16. Effect Measure Modification

  17. Effect Measure Modification Expected additive = Expected multiplicative =

  18. Effect Measure Modification Expected additive = (0.111 + 0.145) – 0.040 = 0.216 Expected multiplicative = 2.78 x 3.61 = 10.04

  19. Effect Measure Modification In this example, older age modifies (increases) the risk difference between gender and depression. However, the risk ratio is not modified by older age (no risk ratio heterogeneity).

  20. Effect Measure Modification Hypothesis:Depression is associated with risk of hip fracture (cohort study) RR = (40 / 220) / (30 / 245) RR = 1.48 •Depressed persons appear to be at 1.48 times higher risk of hip fracture than non-depressed persons. However, is this elevated risk similar among persons with low and high body mass index (BMI)?

  21. Effect Measure Modification LOW BMI HIGH BMI RD = RD = RR = RR =

  22. Effect Measure Modification LOW BMI HIGH BMI RD = (6 / 56) - (6 / 150) RD = 0.107 – 0.040 = 0.067 RD = (34 / 164) - (24 / 95) RD = 0.207 – 0.253 = -0.045 RR = (6 / 56) / (6 / 150) RR = 0.107 / 0.040 = 2.68 RR = (34 / 164) / (24 / 95) RR = 0.207 / 0.253 = 0.82

  23. Effect Measure Modification

  24. Effect Measure Modification Expected additive = Expected multiplicative =

  25. Effect Measure Modification Expected additive = (0.107 + 0.253) – 0.040 = 0.320 Expected multiplicative = 2.68 x 6.32 = 16.92

  26. Effect Measure Modification In this example, it appears that high BMI modifies (decreases) both the risk difference and risk ratio between depression and risk of hip fracture.

  27. Effect Measure Modification Axioms: 1. The presence of effect measure modification should be assessed by “eyeballing” the stratum-specific estimates to see if they differ. 2. Unlike confounding, which is a nuisance effect, effect measure modification represents useful information that should be explored and reported. 3. In the presence of effect measure modification, calculation and reporting of an overall (crude) effect is of dubious value, and is potentially misleading.

  28. Effect Measure Modification Axioms: 4. Statistical tests of homogeneity of the stratum- specific estimates can be performed, but these tests are often underpowered – “eyeballing” the stratum-specific estimates is a better approach. 5. Be careful in the number of subgroups in which effect measure modification is investigated – each additional investigation increases the likelihood of a type I error (chance finding in which the null hypothesis is erroneously rejected).

  29. Effect Measure Modification Axioms: 6. Although some authors define effect measure modification (interaction) as any effect greater than additive, this is inappropriate since the stratum-specific estimates can differ in a non-additive fashion. 7. Any third variable has the potential to be: a) Confounder and effect modifier b) Confounder and not an effect modifier c) Not a confounder and an effect modifier Thus, there is no relationship between confounding and effect measure modification.

  30. Intervention Studies

  31. Observational vs. Experimental Studies REVIEW

  32. Observational Studies • Investigator observes the natural course of events. • Documents who is exposed or non- exposed • Documents who has or has not developed the outcome of interest

  33. Experimental (Intervention) Studies • Investigator allocates the exposure • Therapeutic (Secondary Prevention) • Prevention (Primary Prevention) • Follow subjects to document subsequent development of disease

  34. Experimental (Intervention) Studies • Therapeutic Trials - almost always conducted among individuals (e.g. clinical trial) • Prevention Studies - may be conducted among individuals (e.g. field trial) or among entire populations (community trial)

  35. Therapeutic Clinical Trial • Participants have a disease or condition • Therapies are tested for safety and effectiveness (secondary prevention)

  36. Preventive Field Trial • Participants (individuals) are free from the condition of interest • Potential preventive treatments are tested -- can include healthy individuals at usual risk, or persons recognized to be at high risk (primary prevention)

  37. Preventive Community Trial • Entire communities are randomly allocated to treatments of interest • Example: Newburgh-Kingston dental caries study

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