Research methodology from association to causation

1. Association and Causation
Lt Col Ayon Gupta
MBBS, MD (CM), AFMC,
PhD Epidemiology (AIIMS-ND)
Dept of Community Medicine,
Armed Forces Medical College, Pune

2. Outline of presentation
• Association
▫ Definition
▫ Types of association
▫ Proving association
• Causation
▫ Criteria for causation

3. Association- definition
• Synonyms: correlation, statistical dependence,
relationship
• Statistical dependence between two or more
events, characteristics or variables.
• An association is present if probability of
occurrence of a variable depends upon one or
more variable.
(A dictionary of Epidemiology by John M. Last)

4. Examples
• Thunder versus Lightning
• Cholera and Source of Water
• Childbed fever and clinics by doctors/midwives

5. 5

6. Births, deaths, and mortality rates (%) for all
patients at the two clinics 1841-1846
First clinic Second clinic
Births Deaths Rate Births Deaths Rate
20042 1989 9.92 17791 691 3.38

7. Association - types
• Positive/Negative
• Direct/Indirect
• Causal/Non causal

8. Association- types
• Positive – occurrence of higher value of a
variable is associated with the occurrence of
higher value of another variable.
▫ Ex – education and suicide
• Negative – occurrence of higher value of a
variable is associated with lower value of
another variable.
▫ Ex – female literacy and IMR

9. Association types
• Direct – directly associated i.e. not via a known
third variable
 Direct
 Indirect
Salt intake Hypertension CAD
Salt intake Hypertension

10. Example of a complex causal model

11. Association- types
• Causal – independent variable must cause
change in dependent variable.
▫ Ex – salt intake and hypertension
• Non-causal – non-directional association
between two variables.
▫ Ex – alcohol use and smoking
▫ Oral Pill use and number of sex partners

12. Causal and non-causal associations
Can we think of examples of non-causal association shown above

13. Why do we measure association/
causation?
• If causal –
▫ Identifying people at risk – risk prediction models.
▫ Use the term risk factors
▫ Interventions can be identified for prevention
• If non-causal – markers of risk
▫ Could be non-specific (CRP), or mediator or
byproduct of the disease
▫ Schizophrenia, Alzheimer's Disease

14. Let us list some
questions/hypothesis of
associations

15. List

16. Steps in proving an association
• Choose appropriate study design
• Measure variables addressing bias and
confounding
• Statistical analysis & significance testing

17. What study design to choose?
• Descriptive/analytical/interventional
• Cross-sectional/longitudinal
• Case-control/Cohort
Examples

18. 18

19. Algorithm for studying Association
Could it be due to bias?
Could it be due to
Confounding?
Could it be a result of
Chance?
Could it be causal?
OBSERVED
ASSOCIATION
NO
Probably not
Apply Hill’s
guidelines
True Association
Significance testing
Multi-variate analysis
Association Persists
Critical review
Causal Association
NO
Probably Yes

20. Measuring association
• Depends on
• Type of variables
▫ Continuous - Correlation coefficient, Z or t Test
▫ Categorical - Chi square test/ p value
▫ Non-normal distribution- Non-parametric tests
• Type of Study Design
▫ Case Control - Odds ratio
▫ Cohort - Relative risk/Risk ratio/Risk Difference

21. Correlation Co-efficient
Salt intake
Blood Pressure
Correlation coefficient (r) = 0.85
Negative association
•Pearson’s Correlation Coefficient
•The correlation coefficient
ranges from −1 to 1.
•A value of 1 implies that a linear
equation describes the
relationship between X and Y
perfectly.
•A value of −1 implies that all
data points lie on a line for which
Y decreases as X increases.
•A value of 0 implies that there is
no linear correlation between the
variables.

22. Chi-square test
40 80
60 20
Obese Non-obese
Heart
disease
Present
Absent
100 100
80
120
200
Chi square = 33.33, p value < 0.001

23. Other tests of association
• Comparing means using Z test or t Test
• Nonparametric tests
▫ Spearman rank correlation
▫ Kendall’s Tau

24. Measures of Association - Odds Ratio
40 80
60 20
Obese Non-obese
Heart
disease
Present
Absent
100 100
80
120
200
Odds ratio = ad/bc
= 60*80/40*20 = 48/8
=6.00 (95% CI - 3.05-11.9)

25. Measures of Association - Risk Ratio
•The table shows results
of a study examining
the risk of wound
infections when an
incidental
appendectomy was
done during a staging
laparotomy for
Hodgkin's disease.
Appende
ctomy
Wound Infection Total Incidenc
e (%)
Yes No
Yes 7 124 131 7/132 =
5.34
No 1 78 79 1/79 =
1.27
Risk Ratio = 5.34/1.27 = 4.2
95% CI = 0.53 – 95.0

26. Role of Chance ( p –value)
• Probability of a finding occurring by chance.
• Conventionally kept at 0.05 or 5%.
• If the probability is low ( <5%) it is considered
sufficiently unlikely to have occurred by chance
to justify the designation “statistically
significant.”
• Or if 95% CI does not cross
▫ 1 for OR/RR
▫ 0 for correlation coefficient

27. Determinants of Significance
• Sample Size
▫ Formula for Difference in proportions or means
▫ Large sample size everything can be significant
▫ Power of a test or Beta error
• Clinically significant difference
▫ Hypothesis formulation

28. Handling multiple causation
• Examples
▫ Maternal mortality – age and parity
▫ Coronary Heart Disease – age, smoking, PA…
• Handling during analysis
▫ Stratified analysis
▫ Multi-variate analysis

29. Rule out Confounding
• Two criteria
▫ Independent risk factor or its determinant for disease
▫ Associated with factor under investigation
• Examples
▫ Oral pills and cervical cancer – number of sex partners
▫ Alcohol & CAD - smoking
• Handling Confounding
▫ Restriction of the study population
▫ Matching
▫ Stratification
▫ Multivariable analysis

30. 30
Schematic representation of
confounder
Association
of interest
Confounder
Exposure Exposure
Association of
interest
Outcome
Factor A
Outcome

31. 31
Properties of confounder
• Causally associated with the outcome
• Can be causally or non-causally associated with
the exposure
• Is not an intermediate variable in the causal
pathway between exposure and outcome

32. Algorithm for studying Association
Could it be due to bias?
Could it be due to
Confounding?
Could it be a result of
Chance?
Could it be causal?
OBSERVED
ASSOCIATION
NO
Probably not
Apply Hill’s
guidelines
True Association
Significance testing
Multi-variate analysis
Association Persists
Critical review
Causal Association
NO
Probably Yes

33. Rule out Bias
• A Systematic deviation from truth
• Occurs due to limitations in methodology
• Usually of two types
▫ Selection Bias
▫ Measurement Bias
• Examples
▫ Recall bias in a case-control study of congenital defects
▫ Comparison of absenteeism between participants and
non-participants of workplace intervention

34. 34
Bias
• Any systematic error in a study resulting in
incorrect estimate of association
• Evaluation of role of bias as an alternative
explanation is necessary before interpreting
study results
• When evaluating a study for presence of bias,
▫ Identify its source
▫ Estimate its magnitude: small, moderate or large
▫ Assess its direction: Either towards or away from
the null

35. 35
Types of Bias
• Selection bias:
▫ Occurs when identification of subjects for study is
influenced by some other axis of interest
▫ More important in case-control and retrospective
cohort studies
• Observation/Information bias:
▫ Occurs due to systematic differences in data collection
procedure between different study groups
▫ Affects all study designs equally

36. 36
Types…
Selection Bias
• Control selection bias
• Differential surveillance
errors , Diagnosis errors
or Errors in referral of
individuals into study
• Self-selection bias
• Loss to follow up
• Healthy worker effect
Information Bias
• Recall bias
• Interviewer bias
• Misclassification bias
▫ Differential
▫ Non-differential

38. Would you agree that Catholics commit less suicide than protestants?

39. When does association
become causation

40. Evidence for causal relation
Koch’s postulates
 organism is always found with
the disease
 organism must be isolated and
grown in pure culture
 organism must cause a specific
disease when inoculated into an
animal.
 organism can be recovered from
lesions in the animal and
identified.

41. Guidelines for judging whether an
association is causal
1. Strength of Association
2. Biological gradient (dose-response)
3. Temporality
4. Consistency
5. Specificity
6. Biological plausibility
7. Effect of removing the exposure
8. Extent to which alternate explanations have
been considered

42. Strength of Association
• Measures of the association
▫ Relative risk; Odds ratio
• „
Stronger association is more likely to be
causal, but a weak association can also be
causal
• Examples
▫ RR for lung cancer and cigarette smoking from
various studies are around 10
▫ RR for breast cancer and cigarette smoking from
various studies are between 1–1.5
▫ Environmental risk factors

43. Biological Gradient
• If risk increases with increasing exposure, it supports
the notion of a causal association.
• However, the absence of dose-response does not
preclude causal association
▫ There is almost always a dose below which no
response occurs or can be measured
▫ There is also a dose above which any further
increases in the dose will not result in any
increased effect
• For some substances, some dose levels may be
beneficial “The right dose differentiates a poison
from a remedy” (Paracelsus)

44. Temporality
• Exposure precedes outcome
• If factor "A" is believed to cause a disease, then it
is clear that factor "A" must necessarily
always precede the occurrence of the disease.
• Depression and Alcohol Use?
• This is the only absolutely essential criterion.
• Does not automatically mean it causes it.
• Not possible in Case-control studies

45. Consistency
• It is supportive of causal association if the same
finding can be replicated in/by
▫ different populations
▫ using various study designs
▫ different researchers
▫ different places and times

46. Specificity
• Specificity of the association suggests that one exposure
is specific to one disease
• This criterion is not applicable to all exposure-disease
associations because a disease may be caused by several
exposures, and an exposure may cause several diseases.
• An exposure is likely to have a deleterious effect on a
specific mechanism (at a cellular or molecular level) that
may then lead to one or more diseases).
• Example: tobacco use causes many diseases other than
lung cancer and lung cancer is caused by many
substances other than tobacco.

47. Biological Plausibility
• Not always possible.
• May actually follow the discovery
• Not in conflict with existing theories

48. Others
• Alternate Explanations
▫ Extent to which alternate explanations have been
considered including confounders
• Coherence
▫ This implies that a cause-and-effect interpretation for
an association does not conflict with what is known of
the natural history and biology of the disease.
▫ If we claim that a newly introduced exposure of high
prevalence greatly increased the incidence of a disease,
there should be an increased incidence of that disease
in the population at large.

49. Effect of removing the exposure
• Experimental studies or natural studies
• Similar to the dose-response relationship, the
presence of this criterion supports the notion of
causal association. However, the absence does
not preclude it.
• Example: after quitting smoking, the amount of
specific-DNA adducts decreases in blood
• Vaccine Probe Studies

51. Guidelines of Causality: Lung Cancer and
Smoking

52. Algorithm for studying Association
Could it be due to bias?
Could it be due to
Confounding?
Could it be a result of
Chance?
Could it be causal?
OBSERVED
ASSOCIATION
NO
Probably not
Apply Hill’s
guidelines
True Association
Significance testing
Multi-variate analysis
Association Persists
Critical review
Causal Association
NO
Probably Yes

53. Examples

54. Example
• The effects of prenatal exposure to diethylstilbestrol were studied by
a prospective cohort investigation of 110 exposed and 82 unexposed
females.
• The general health characteristics of mothers and daughters in both
groups were similar.
• Among the exposed, there were striking benign alterations of the
genital tract, which included transverse ridges (22 per cent),
abnormal vaginal mucosa (56 per cent), and biopsy-proved adenosis
(35 per cent). Among the unexposed there were no ridges and one
case of vaginal mucosal abnormality including adenosis (p < 0.0001).
• Abnormal cervical epithelium occurred in almost all exposed
subjects but in only half the unexposed (p < 0.0001).
• The incidence of vaginal adenosis was highest when
diethylstilbestrol was begun in early pregnancy. It was not detected
when treatment was initiated in the 18th week or later.
• Oral contraceptive use and prior pregnancy were associated with less
adenosis and erosion, respectively (p <0.05). No cases of cancer
were observed.
N Engl J Med 292:334–339, 1975)

55. Summary
• Make an hypothesis for association – choose
appropriate design and sample size
• Test for Hypothesis by ruling out chance
• Rule out the possibility of bias and confounding.
• Apply criteria of causation to judge causality
• “All that glitters are not gold”. Not all
associations are causal.

56. THANKS