3/2/2011 Case-control studies 1 Study designs: Case- control studies Victor J. Schoenbach, PhD home page Department of Epidemiology Gillings School of Global Public Health University of North Carolina at Chapel Hill www.unc.edu/epid600/ Principles of Epidemiology for Public Health (EPID600)
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3/2/2011Case-control studies1 Study designs: Case-control studies Victor J. Schoenbach, PhD home page Department of Epidemiology Gillings School of Global.
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3/2/2011 Case-control studies 1
Study designs: Case-control studies
Victor J. Schoenbach, PhD home page
Department of EpidemiologyGillings School of Global Public Health
University of North Carolina at Chapel Hill
www.unc.edu/epid600/
Principles of Epidemiology for Public Health (EPID600)
10/7/2008 Case-control studies 2
From my uncleAre you the weakest link?
Below are four (4) questions.
You have to answer them instantly.
You can't take your time, answer all of them immediately. OK?
Let's find out just how clever you really are.
Ready?
10/7/2008 Case-control studies 3
Question 1
You are participating in a race.
You overtake the second person.
Question: What position are you in?
10/7/2008 Case-control studies 4
Question 1 – answer
If you answer that you are first, then you are absolutely wrong!
Answer: If you overtake the second person and you take his place, you are second!
10/7/2008 Case-control studies 5
On to question 2
Try not to screw up on the next question.
To answer the second question, don't take as much time as you took for the first question.
10/7/2008 Case-control studies 6
Question 2
Question: If you overtake the last person, then you are …?
10/7/2008 Case-control studies 7
Question 2 – answer
Answer: If you answered that you are second to last, then you are wrong again.
Tell me, how can you overtake the LAST person?! …?
You're not very good at this are you?
10/7/2008 Case-control studies 8
On to question 3
The third question is very tricky math!
Note: This must be done in your head only.
Do NOT use paper and pencil or a calculator.
Try it.
10/7/2008 Case-control studies 9
Question 3 – what is the total?
Take 1000 and add 40 to it.
Now add another 1000.
Now add 30.
Add another 1000.
Now add 20.
Now add another 1000.
Now add 10.
10/7/2008 Case-control studies 10
Question 3 – answer
Did you get 5,000? The correct answer is actually 4,100.
Don't believe it? Check with your calculator!
10/7/2008 Case-control studies 11
On to question 4
Today is definitely not your day.
Maybe you will get the last question right?
10/7/2008 Case-control studies 12
Question 4
Mary's father has five daughters: 1. Nana, 2. Nene, 3. Nini, 4. Nono.
Question: What is the name of the fifth daughter?
10/7/2008 Case-control studies 13
Question 4 – answer
Answer: Nunu? NO! Of course not. Her name is Mary. Read again.
(“Mary's father has five daughters.…”)
You ARE the WEAKEST LINK!!!!!! Good-bye!!!
(With Love, Your Uncle)
10/7/2008 Case-control studies 14
Plan for this lecture
• Confidence intervals and significance tests (read only)
• Incidence density and cumulative incidence (brief)
• Attributable risk (brief)
• Theoretical overview of case-control studies as a complement to the traditional perspective
10/7/2008 Case-control studies 15
Confidence intervals & significance tests
• Everything you’ve been told so far about confidence intervals and statistical significance is probably misleading, including this statement.
• I am not licensed to teach statistics, so what I say on this topic mustn’t leave this room!
10/7/2008 Case-control studies 16
Confidence intervals
• “a plausible range of values for the unknown population parameter”
Michael Oakes, Statistical inference, p.52
• Exact interpretation is problematic
• We are more confident that a 95% interval covers the parameter than a 90% interval, but the 95% interval is wider (provides a less precise estimate)
10/7/2008 Case-control studies 17
Significance tests
“It might be argued that the significance test, if properly understood, does no harm. This is, perhaps, fair comment, but anyone who appreciates the force of the case presented in this chapter will realize that equally, it does very little good.”
Michael Oakes, Statistical inference, p.72
10/7/2008 Case-control studies 18
Incidence rate and incidence proportion
[incidence density and cumulative incidence]
10/7/2008 Case-control studies 19
100% 0%
T0 Time T1
Population at risk
ID = - slope (relative to height)
IR (ID) and IP in a closed cohort
} CI
}1 – CI
T0 T1
10/7/2008 Case-control studies 20
Attributable risk
10/7/2008 Case-control studies 21
Attributable risk
Assume that we know a causal factor for a disease.
Conceptually, the “attributable risk” for that factor is:
1. difference in risk or incidence between exposed and unexposed people or
2. difference in risk or incidence between total population and unexposed people
10/7/2008 Case-control studies 22
Attributable risk
Attributable risk can be presented as:
1. an “absolute” number, e.g., “80,000, or 20 per 100 cases/year of stroke are attributable to smoking”
2. a “relative” number, e.g., “20% of stroke cases are attributable to smoking”.
(analogy: a wage increase in a part-time job: $ increase, % increase in wage, % increase in income)
10/7/2008 Case-control studies 23
For relative measures, think of % of cases
R1 R1 – R0 =
"Attributable
R0 R0 n0 R0 n1
risk"
n0 n1
People
Incidence rate or proportions
10/7/2008 Case-control studies 24
For relative measures, think of % of cases
R1 R1 – R0 =
"Attributable
R0 R0 n0 R0 n1
risk"
n0 n1
Substitute population
Caseload
10/7/2008 Case-control studies 25
For relative measures, think of % of cases
R1 R1 – R0 =
"Attributable
R0 R0 n0 R0 n1
risk"
n0 n1
Caseload
10/7/2008 Case-control studies 26
Case-control studies
10/8/2001 Case-control studies 27
Case-control studies• Traditional view: compare
- people who get the disease - people who do not get the disease
• “Controls” a misnomer, derived from faulty analogy to controls in experiment
• Modern conceptualization: controls are a “window” into the “study base”
10/7/2008 Case-control studies 28
Case-control studies
Cases Controls
10/7/2008 Case-control studies 29
Population at risk (N=200)
10/7/2008 Case-control studies 30
O
Week 1
O
10/7/2008 Case-control studies 31
O
Week 2
O
O
OO
10/7/2008 Case-control studies 32
O
OO
Week 3
O
O
O
O
10/7/2008 Case-control studies 33
Incidence rate(“incidence density”)
Number of new casesIR = –––––––––––––––––––
Population time
6/23/2002 Case-control studies 34
Incidence rate(“incidence density”)
Number of new cases 7IR = ––––––––––––––––––– = ––––––
Population time ?
6/23/2002 Case-control studies 35
Incidence rate(“incidence density”)
Population time at risk:
200 people for 3 weeks = 600 person-wks
But 2 people became cases in 1st week
3 people became cases in 2nd week
2 people became cases in 3rd week
Only 193 people at risk for 3 weeks
6/23/2002 Case-control studies 36
Incidence rate(“incidence density”)
Assume that:
2 people who became cases in 1st week were at risk for 0.5 weeks each = 2 @ 0.5 = 1.0
3 people who became cases in 2nd week were at risk for 1.5 weeks each = 3 @ 1.5 = 4.5
2 people who became cases in 3rd week were at risk for 2.5 weeks each = 2 @ 2.5 = 5.0
6/23/2002 Case-control studies 37
Incidence rate(“incidence density”)
Total population-time =
Cases occuring during week 1: 1.0 p-w
Cases occuring during week 2: 4.5 p-w
Cases occuring during week 3: 5.0 p-w
Non-cases: 193 x 3 = 579.0 p-w
589.5 p-w
6/23/2002 Case-control studies 38
Incidence rate(“incidence density”)
7 IR = –––––– = 0.0119 cases / person-wk 589.5
average over 3 weeks
Number of new casesIR = –––––––––––––––––––
Population time
3/2/2011 Case-control studies 39
Incidence proportion(“cumulative incidence”)
Number of new casesCI = –––––––––––––––––––
Population at risk
10/7/2008 Case-control studies 40
Incidence proportion(“cumulative incidence”)
73-week CI = –––– = 0.035 200
Number of new casesCI = –––––––––––––––––––
Population at risk
10/7/2008 Case-control studies 41
Can estimate incidence in people who are “exposed”
O
Week 1
10/7/2008 Case-control studies 42
Can estimate incidence in people who are “exposed”
O
Week 2
OO
10/7/2008 Case-control studies 43
Can estimate incidence in people who are “exposed”
O
Week 3
OO
O
10/7/2008 Case-control studies 44
Can estimate incidence in people who are “unexposed”
O
Week 1
10/7/2008 Case-control studies 45
Can estimate incidence in people who are “unexposed”
O
Week 2
O
10/7/2008 Case-control studies 46
Can estimate incidence in people who are “unexposed”