1. Study Designs-Review Epidemiology 200A Dr. Jørn Olsen
November 24, 2009
Fall 2009

2. Causation 1st Axiom: Diseases exist as a function of preceding causes
causes exist
causes (some) have a social or environmental history
2nd Axiom: Diseases are distributed as a function of susceptibility and exposures
Diseases follow sufficient causal fields
Host
Exposure
Environment

3. Causation
3rd Axiom: The manifestation of diseases is a function of treatment and natural defense mechanism.
4th Axiom: Diseases are not distributed at random. They are not unavoidable events.
Some causes are man made.
Therefore, some diseases are preventable.

4. Hume :
We may define a cause to be an object followed by another and where all the objects similar to the first are followed by objects similar to the second.
E D

5. Not many examples of E D associations in medicine
Not many examples of E D associations in medicine. Causes in the ”strong-Hume” sense could be seen as necessary and sufficient causes
A necessary cause
E D
several examples in medicine, but they are usually man-made
tuberculosis
post-partum depression
AIDS, etc.
A sufficient cause E D

6. Sexual debut is a risk indicator, maybe a risk factor or a cause (sexual activity is a necessary cause). E D

7. Causes act conditionally upon other causes and in combination they become a sufficient causal field.
Each component cause is only sufficient given the other component causes exist.
I II III A B B E A A D D C F
70% 20 % 10%
Causes are only necessary for a subdomain of diseases (I, II or III) and they are only sufficient given the other causes are in place.
The strength of a given association depends upon the prevalence of the other component causes.

8. A B air p sex C bronchial response treatment asthma E D
DAG
A B air p sex C bronchial response treatment asthma E D
arc A B adjacent > nodes
A-C-D directed (causal) E-C-D not directed
Ancestor – descendant
Acyclic if no directed path forms a closed loop

9. Measures of disease occurrence, measure of associations

10. Breast cancer screening trial 5year follow-up
Screening N Obs. Time Death
Yes , ,
Yes , ,
CI+, R /25,000 =
CI-, R /25,000 =
IR /118,000 =
IR /117,500 =

11. If disease is rare… R~ IR ~ disease-odds=
(180/25,000) ~ (180/118,000) x 5 ~ (180/25,000) / (24820/25,000)
~ = ~

12. From incidence rates to incidence proportions Kaplan-Meir or exponential formula:
Conditions:
Closed population
No competing risks
Stable IR over Δt and IR is small
∑IRkΔtk k
1-e

13. Associations RR = R+ / R- = 0.857 IRR = IR+ / IR- = 0.853
OR = Disease odds + / Disease odds - = 0.857

14. Attributable fractions
Screening a preventive measure (for mortality). Treat lack of screening as ‘a causal exposure’.
210 die in the non-screened group. Had they had the same risk as the screened we would expect
(180/25,000) x 25,000 = 180 deaths
30 deaths prevented out of 210 = 30/210 x 100 = 14%

15. Population or ( R- - R+) / R- = 1-RR
( ) / = =0.14
Preventive fraction-Vaccine efficiency in infectious disease epidemiology

16. 210+180 died in the population
died in the population. 30 could have been prevented = 30/390 = 7.7%
Formal procedure
R- x N- - R+ N-
R- x N- + R+ x N+

19. Prevalence and incidence
Ix D = P/(N – P) in steady state population with no migration.
I(N-P)Δt
I1PΔt or
(1/D)PΔt
I1 = incidence rate of
leaving the
prevalence pool
I (N-P) Δt = (1/D) P Δt
I (N-P) = 1/D P
I x D = P/(N-P)
P/(N-P) prevalence odds; if P is small
I x D ≈ P/N

20. Is the obesity epidemic leading to an epidemic of diabetes?
The prevalence of diabetes is increasing but ?

23. Randomized Controlled Trial (RCT)
Experimental studies: The researcher manipulates the exposure in order to study the effect of the exposure.
The unit of observation is often individuals, but can be regions.
Requires longitudinal data collection.
Relies upon primary data or a combination of primary and secondary data.
Is well suited to estimate the expected therapeutic efficacy in quantitative terms under ideal circumstances (efficacy). Does not usually provide evidence of unexpected effects (or side effects). Does not provide evidence of effects in routine circumstances (effectiveness).

24. Three key elements in a classic RCT
Randomization: comparability of populations
Placebo: comparability of circumstances
Blinding: comparability of information

25. Basic structure of the RCT
Set up a specific hypothesis
Select a sampling frame among suitable patients
Define inclusion/exclusion criteria
Obtain informed consent
Randomize
Follow compliance
Measure outcome
Analyse according to intention to treat or according to protocol

26. Sequence Candidate patients
Define source population / study base incl./excl. Criteria
Informed consent randomize
Compliance Loss to follow-up Effect
Analyse: Intention to treat or According to protocol
Candidate patients

27. Reporting from the trial should at least provide the information requested by the CONSORT group (Lancet 2001; 357: ).
CONSORT: Consolidated Standards of Reporting Trials
22-item checklist
Detailed explanation of CONSORT in Altman et al. Annals of Internal Medicine 2001; 134:

28. Screening for colorectal cancer Kronborg et al
Screening for colorectal cancer Kronborg et al. Lancet 1996; 348:
Figure 1: Study profile

29. Screening for colorectal cancer Kronborg et al
Screening for colorectal cancer Kronborg et al. Lancet 1996; 348:
Table 2. Compliance during repeated screening
                                                           
Screening round
Number of people invited for screening
Number of people screened
30,762 20,113 18,236 16,746 15,279
20,672 (67%) 18,781 (93%) 17,279 (94%) 15,845 (94%) 14,203 (92%)

30. Screening for colorectal cancer Kronborg et al
Screening for colorectal cancer Kronborg et al. Lancet 1996; 348:

31. Classification of designs
Unit of observation
= individual or clusters of individual macroepidemiology / microepidemiology
Allocation of exposure
= experimental / non-experimental
Longitudinal recording
= survey / case-control or follow-up
Selection of exposure
or outcome
= follow-up / case-control
Source population
= case-control; primary, secondary
Data
= primary / secondary
Data collection
= prospectively / retrospectively 31

32. Source: Kunzli et al. The Semi-individual Study in Air Pollution Epidemiology; A Valid Design as Compared to Ecologic Studies. EHP 1997, 105 (10).