1. Lecture 1: Fundamentals of epidemiologic study design and analysis
Jeffrey E. Korte, PhD
BMTRY 747: Foundations of Epidemiology II
Department of Public Health Sciences
Medical University of South Carolina
Spring 2015

2. Basic study designs Ecologic Cohort Case-control
Cross-sectional
Longitudinal
Cohort
Case-control
Randomized controlled trial

3. Other study designs
Case-cohort
Nested case-control
Case-crossover

4. Ecologic studies Unit of observation: geographical area
No individual information available
Analyze correlations between:
Mean value of exposure of interest
Rate of disease of interest
Vulnerable to “ecologic fallacy”

5. Ecologic fallacy “Marginal” information known
Individual information not known
Exp
Not exp
Dis ? 50
No dis
950
200
800
1000

6. Ecologic fallacy (exposure appears related to disease)
Not exp
Dis ? 50
No dis
950
200
800
1000
Exp
Not exp
Dis ?
150
No dis
850
500
1000
Region 1
Region 2

7. Ecologic fallacy unmasked (there is actually no association)
Exp
Not exp
Dis 10 40 50
No dis
190
760
950
200
800
1000
Exp
Not exp
Dis 75
150
No dis
425
850
500
1000
Region 1
Region 2

8. Ecologic fallacy Scenario 2: example from Szklo book, page 16
Are poor people bad drivers???
(see next slide)

10. Ecologic studies Great for generating hypotheses
Aggregate measures (mean across people)
Environmental (physical exposures)
Global measures (sociopolitical, etc.)
e.g. dietary fat intake/breast cancer; vitamin D/prostate cancer
Mixed individual-ecologic study
Some variables are measured using an ecologic criterion (neighborhood characteristics, etc.)

11. Cross-sectional studies
Single timepoint
May be baseline data from a cohort study
Assess association between exposure and disease of interest
Limited to prevalent disease outcomes
Reflects incidence rate and duration/survival
Exposure data more vulnerable to recall bias
Less time-consuming, less expensive

12. Cross-sectional studies
Disadvantage: concurrent exposure and disease information restricts causal inference
May be able to collect historical exposure data in questionnaire (vulnerable to recall bias)

13. Cohort studies Assemble individuals without disease
Assess exposure status
Follow individuals over time
Observe incident disease events
Avoid recall bias
More expensive, time-consuming

14. Cohort studies Can estimate disease risk, disease rate
Can estimate proportion exposed (if population-based sample)
Can evaluate numerous outcomes
Can evaluate numerous exposures
Cohort study can be basis for more efficient study designs (case-cohort, nested case-control)

15. Cohort studies Occupational exposures:
Can use occupational cohorts to evaluate specific exposures (e.g. chemicals, radiation) at high doses in humans
Not representative of general population
Vulnerable to healthy worker survival effect
Exposed group and comparison group may have comparability problems

16. Cohort studies Retrospective cohort studies
Historical exposure data is available
Medical records are available through time
Cohort is assembled and followed through historical time to simulate a prospective cohort study
Often used in occupational studies
Less expensive and time-consuming

17. Case-control studies
Individuals recruited into study based on disease status
Case definition can be critical
Historical exposure information obtained
Exposure compared between cases and controls
Vulnerable to recall bias

18. Case-control studies Selection of control group is critical
Population-based, hospital-based?
Matching?
Controls should be representative of the population from which the cases occurred (people who would have been recruited as cases if they had had the disease of interest)

19. Case-control studies See Figure 1-19, Szklo page 26: survival bias
(see next slide)

21. Case-cohort study Based in cohort study
Sub-cohort is identified: subset of participants at baseline
Sub-cohort may include eventual cases
Individuals who become cases are compared to sub-cohort

22. Case-cohort study Advantages:
less expensive than cohort study, if lab tests are done on selected stored samples instead of all samples
Exposures assessed before incident disease (no recall bias)
Sub-cohort can be comparison group for more than one case group (e.g. different disease)

23. Case-cohort study
See Figure 1-21, Szklo page 28
(see next slide)

25. Nested case-control study
Based in cohort study
As cases arise, one or more (matched) controls are selected
Individuals may serve as a control at one timepoint, then serve as a case at a later timepoint
Advantages similar to case-cohort design

26. Nested case-control study
See Figure 1-20, page 27 Szklo
(see next slide)

28. Case-crossover design
All individuals have the disease of interest
Exposure for each individual is compared at one timepoint (e.g. just before diagnosis) versus another timepoint (e.g. one year earlier)
Useful for acute effects of exposure (e.g. environmental, psychological, physical)

29. Randomized controlled trials
Experiment
May test medical, behavioral, social intervention
Compare outcomes between groups
Randomization should eliminate the possibility of bias or confounding, even from unknown confounders
Findings may not be generalizable, depending on sampling strategy/recruitment into study

30. Measures and associations
Strength of association
Risk ratio, odds ratio, hazard ratio, rate ratio
1.0 denotes no association (i.e. the exposure groups have the same risk)
Statistical significance / p value
Chi-square, t-test, multivariable regression
p<0.05 denotes statistical significance
Confidence intervals
Show precision of estimate and statistical significance

31. Measures and Associations (continuous outcome)
Mean
Median
Percentiles
Difference between means
Can calculate risk ratio for 1-unit increase, 10-unit increase, etc. (association is assumed to be constant over the exposure range)

32. Measures and Associations (categorical outcome)
Prevalence
Incidence
Risk
Odds
Relative risk / risk ratio
Hazard ratio
Odds ratio
Rate ratio

33. Prevalence
People currently living with a health outcome of interest (e.g. 72%, 137/100,000, etc.)
Prevalence is a reflection of several factors
Incidence rate
Cure rate
Progression rate
Death rate
Explanatory factors (e.g. age, causal exposures, medical care)

34. Prevalence
Point prevalence
Period prevalence
Lifetime prevalence

35. Incidence New cases of disease (risk or rate)
Occur over time, during study follow-up or during public health surveillance
Reflects:
Changes in diagnostic standards
Screening bias (early detection)
Latent period (undetected/undetectable)
(these factors also affect prevalence)

36. Risk Same as “proportion”
Assumes all individuals have the same follow-up time
Risk ratio: disease risk in exposed group, divided by risk in unexposed group

37. Rate Individuals do not need to have the same follow-up time
Denominator is person-years of follow-up in each group
Sum of individual follow-up times
Rate ratio: disease rate in exposed group, divided by rate in unexposed group

38. Rate (example) Deaths Person-yrs Rate Group 1 45 13,739 32.8/10,000
15,180
21.1/10,000
relative rate = 1.6

39. Risk Ratio vs. Rate Ratio
Example: compare two samples followed over 5 years.
Group 1: 20% develop cancer, all within the first year
Group 2: 20% develop cancer, all during Year 5
What is the risk ratio?
What about the rate ratio?

40. Odds
Probability of having disease (or exposure), divided by probability of not having disease (or exposure)
Useful for case-control study
Odds ratio is a good estimate of risk ratio for rare diseases

41. Odds ratio Exp No exp Dis 45 5 50 No dis 10 40 55 100
Odds of exposure in cases: 45/5
Odds of exposure in controls: 10/40
Exposure odds ratio = (45/5)/(10/40) = 36
Disease odds ratio also equals 36

42. Risk ratio Exp No exp Dis 45 5 50 No dis 10 40 55 100
Risk of disease in exposed: 45/55
Risk of disease in unexposed: 5/45
Risk ratio = (45/55)/(5/45) = 7.4

43. Risk ratio Exp No exp Dis 9 1 10 No dis 250 740 990 259 741 1000
Risk of disease in exposed: 9/259
Risk of disease in unexposed: 1/741
Risk ratio = (9/259)/(1/741) = 25.7

44. Hazard ratio
Used in survival analysis (Cox proportional hazards model) of cohort studies
“Time-to-event” information is the outcome of interest
When each case arises, all other noncensored, healthy individuals serve as controls for that case at that timepoint.
The model assumes that the hazard ratio (exposed/unexposed) stays constant over time.

45. Rate ratio Can be estimated in Poisson regression
Another generalized linear model
Uses “count” data, typically in cohort studies
The model assumes that independent risk factors result in multiplicative risks.

46. Risk difference Can be estimated in linear regression
Outcome is assumed to be continuous, normally distributed
Exposures can be continuous, ordinal, or categorical
The model assumes that independent risk factors result in additive risks.

47. The Golden Rule
Thou shalt design and analyze epidemiologic studies in such a way as to allow you to answer the scientific question of interest.
Corollary: methods are a means to an end.
Implication: do not adapt the question to fit the methods.