3 Causal Models Part I: Sufficient Causes

1. 3 Causal Models Part I: Sufficient Causes Matthew Fox Advanced Epidemiology

3. Review of This Morning • “Modern” epidemiology • Goal of etiologic research • Valid and precise estimate of the effect of exposure on disease • Why not everything is as we were taught • Statistics • Review of study designs, measure of effect • Odds ratios and case-control sampling • Hopefully changed a few minds (and set the tone)

4. This Session • Why are we in this business? • What is the goal of epi investigations? • Things may have missed in intro epi • How causal models and causal inference helps clarify what we do and how we do it • Sufficient Causes Model • Rothman’s Model (Sufficient Cases Model)

5. I turn on a light switch and the light doesn’t go on. The globe (bulb?) is burned out. What prevented the light from going on?

6. I turn on a light switch and the light does go on. What caused the light to go on?

7. Is there any disease for which all cases are attributable to one and only one cause?

8. Smoking causes lung cancer. So why doesn’t every smoker get lung cancer?

9. Is the occurrence of disease deterministic or random within what we know? Is a coin flip random?

10. What is a “strong” risk factor? What determines strength?

11. Why study causal models? • Helps understand why we do what we do • What do we mean when we say that smoking causes lung cancer? • Gives meaning to our measures of effect • Theoretical and practical • Helps clarify important epidemiologic concepts

12. A definition of a cause • An antecedent event, characteristic, or condition that was necessary for the occurrence of disease at the time it occurred all other things being fixed • Antecedent • Necessary • At the time it occurred • Other things fixed

13. The Sufficient Cause Model Ken Rothman

14. History • Goes back to John Leslie Mackie (1964) • Necessary causes: • If x is a necessary cause of y, then y necessarily implies x • x does not imply y will occur • Sufficient causes: • If x is a sufficient cause of y, then x necessarily implies y • Since other things can cause y, y does not imply x • Typical use of cause refers to: • Insufficient and non-redundant parts of unnecessary but sufficient causes (INUS)

15. The Sufficient Cause Model • Lots of ways to get a disease • Think of each way as a pie • Called a sufficient cause • Mechanisms exist independent of us • But we’re susceptible to them if weacquire the components • Go through life picking up exposures and filling in pies

16. The Sufficient Cause Model • Person is susceptible to multiple diseases • Diseases have multiple sufficient causes • Each sufficient cause hasmultiple component causes • Each component causehas attributes • Shared components between sufficient causes • It is theoretically possible every case of outcome has a unique pie

17. Take home message 1:All disease causation is multifactoral and mechanisms are complex

18. Sufficient Causes • Minimally sufficient • Each sufficient cause has a unique set of components and none is extraneous • Necessary cause • Component cause appearing in all sufficient causes for a disease • Poole proposes “universally necessary” • Complementary component causes • Set of component causes required to complete a sufficient cause, aside from one (exposure)

19. How much do we know? • U is often largest piece of a sufficient cause • We understand poorly disease causation • But if we could specify the mechanisms perfectly, could we predict all disease? • Deterministic • Might be infinite combinations • In risk factor epidemiology, we focus on one component and ignore the complement

20. So what might HIV infection look like? • One SCM (pie) might be: • Exposed to HIV through sex • Unvaccinated (OK for now), no natural immunity • No condom use • Why doesn’t everyone exposed through sex get HIV? U • Circumcision? STDs? Genetic factors? No use of microbicide? • Is each component necessary? • If so, take away one and you prevent disease • Must be more causes than just the HIV virus • Other SCMs exist • Mother to child, transfusion, needle stick, etc.

21. The Sufficient Cause Model • Components can be positive or negative • Lack of vaccination • Component causes should be specific • Can be identical except for timing • Don’t need to understand entire pie to prevent • Removing one piecerenders the pie incomplete

22. Disease and Causation • The sufficient cause acts when all of the component causes have been gathered • Disease occurs at completion of temporally last component cause • Model is deterministic • Each disease occurrence has a latency • Time between its occurrence and its detection

23. Component Causes (Exposures) • Component causes (i.e. exposures we might want to study) have attributes • Dose • Duration • Induction period (NOT LATENCY) • Specifying the attributes improves the resolving power of the study

24. Does smoking cause lung cancer? • Smoking is too imprecise • How much for how long? What type? • Starting at what age? • Infinite combinations • Each might have a different risk • Ignoring the attributes means we are lump all exposures (from 1 lifetime cigarette to a 10 packs a day) together as exposed • This biases towards no effect!

25. Dose attributes • Time weighted average dose • Grams of fat per day • Maximum dose • Highest adult body weight • Body weight or surface area scaled • Grams of alcohol per kilogram body weight • Cumulative dose • Pack years of cigarettes

26. Duration attributes • Total time of exposure • years employed • Biologically relevant time of exposure • Smoking before first pregnancy • Time of exposure beyond a minimum • Years of driving after age 25 • Time of exposure after gathering another component cause • HIV infection after HPV infection

27. Take home message 2: To study effects of exposures, the exposures must be precisely defined

28. Induction period attributes • Induction period is the time between completion of a COMPONENT cause (i.e. the exposure of interest) and completion of the SUFFICIENT cause (i.e. disease occurrence) • Induction period doesn’t characterize disease • Characterizes component cause-disease pair • Every disease has a component cause with zero induction time • Failure to exclude induction time from person time biases towards the null

29. Take home message 3: Diseases don’t have induction times

30. Induction Time Example Diethylstilbestrol, adenocarcinoma of the vagina A synthetic non-steroidal estrogen, given to pregnant women to prevent miscarriage (’40s-’70s) Exposure is known to have occurred during gestation Cancer occurs in the offspring between 15-30 years of age Other processes assumed to occur in the interim Other components in the causal pie still occur Adolescent hormonal activity may be one If outcome can’t occur before 10 years, don’t include 1st 10 years of person-time Similar to immortal person-time 30

31. Catalyst of diseases Anything that speeds up (or slows down) the occurrence of a disease that would occur anyway Are they causes of disease? Remember the “at the time it occurred” part of the definition of a cause Does it matter to you? What about promoters or catalysts?

32. Applications of the sufficient cause model • The effect of the index condition, relative to the reference condition: • The number of completed sufficient causes among those with index condition • Minus number of completed sufficient causes among those with reference condition • Interaction between component causes • Arises when one or more sufficient causes contains both component causes

33. Take home message 4: Interaction is ubiquitous

34. Strength is Determined by Complements • Strength of a risk factor • Typically measured on the relative scale • Is determined by the relative prevalence in the population of the causal complements • Also affected by the competing risks of other sufficient causes for the same disease

35. G U E Imagine a gene-environment interaction U Phenylketonuria (PKU) - a genetic disorder characterized by a deficiency in the enzyme to metabolize the amino acid phenylalanine. Untreated, it can cause problems with brain development. However, PKU is a rare genetic diseases that can be controlled by diet, one low in phenylalanine.

36. Imagine a gene-environment interaction • Imagine a population where: • 10% get disease through U no matter what • 60% of the population has U and G completed • If we randomly assigned the exposure (diet), would the relative risk be high or low?

37. Imagine a gene-environment interaction Randomized to get E Randomized to not get E U U Exposed 70% get disease (10% U + 60% U/G/E) Unexposed 10% get disease (10% U) G G G G U U U U E E G G G G U U U U E E G G G G U U U U E E RR = 70%/10% = 7

38. Imagine a gene-environment interaction • Imagine a population where: • 10% get disease through U no matter what • Same as 1st example • 10% of the population has U and G completed • If we randomly assigned the exposure, would the relative risk be high or low?

39. Imagine a gene-environment interaction Randomized to get E Randomized to not get E U U Exposed 20% get disease (10% U + 10% U/G/E) Unexposed 10% get disease (10% U) G G U U E RR = 20%/10% = 2.0

40. Imagine a gene-environment interaction • Imagine a population where: • 40% get disease through U no matter what • 10% of the population has U and G completed • Same as last • If we randomly assigned the exposure, would the relative risk be high or low?

41. Imagine a gene-environment interaction Randomized to get E Randomized to not get E U U U U Exposed 50% get disease (10% U + 40% U/G/E) Unexposed 40% get disease (40% U) U U U U G G U U E RR = 50%/40% = 1.25

42. Another example • Vaccines are usually extremely protective • What we often call a strong protective effect • But what if we tested our vaccine in a population where everyone had natural immunity? • Lack of natural immunity is a piece in the pie (causal complement) • When it is common, the effect appears strong • When it is rare, the effect appears weak

43. Take home message 5: “Strength” of an exposure’s effect is a function of:1) prevalence of causal complements (what we usually ignore) and 2) the % of all disease that goes through mechanisms without the exposure (U for short)

44. Illustrates arbitrariness of effects: Effect of folic acid on: Spina bifida Assume same samplesize (N=100) RD = 0.2 A B C

45. Illustrates arbitrariness of effects: Effect of FA on: Neural tube closure RD = 0.2 Same A B C

46. Take home message 6: We are often best studying rare occurrences when possible

47. Proportion of disease caused by… • What % of all cases are caused by genes? • What % of are caused by environment (dietary phenalynine)? G U E Phenketunuria - 100% of all cases

48. Take home message 7:The % of all cases of a disease attributable to different causes can (and will) sum to > 100%

49. Advantages of the SCM • Applies to disease mechanisms in individuals • Tells us about how disease occurs, not just what caused it • Illustrates: • How component causes act together • Attributes of component causes • How strength is function of complements • The ubiquity of interaction • Process of causation, although not complete

50. Disadvantages of the SCM • Cannot easily apply to populations • Though our examples show can be used this way • Deterministic in nature • Assumes no randomness to disease causation • Doesn’t easily deal with continuous variables • Obscures importance of reference group definition • Comparison is between those with component cause and those without the component cause under study