Transcript Estimating quantities with confidence intervals

Confidence intervals
Kristin Tolksdorf
(based on previous EPIET material)
18th EPIET/EUPHEM Introductory course
01.10.2012
Inferential statistics
• Uses patterns in the sample data to draw
inferences about the population represented,
accounting for randomness.
• Two basic approaches:
– Hypothesis testing
– Estimation
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Criticism on significance testing
“Epidemiological application need more than a
decision as to whether chance alone could have
produced association.”
(Rothman et al. 2008)
→ Estimation of an effect measure (e.g. RR,
OR) rather than significance testing.
→ Estimation of a mean
→ Estimation of a proportion
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Why estimation?
Norovirus outbreak on a Greek island:
“The risk of illness was higher among people
who ate raw seafood (RR=21.5).”
How confident can we be in the result?
What is the precision of our point estimate?
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The epidemiologist needs measurements
rather than probabilities
2 is a test of association
OR, RR are measures of association on a continuous scale
infinite number of possible values
The best estimate = point estimate
Range of “most plausible” values, given the sample data
Confidence interval  precision of the point estimate
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Confidence interval (CI)
Range of values, on the basis of the sample
data, in which the population value (or true
value) may lie.
• Frequently used formulation:
„If the data collection and analysis could be
replicated many times, the CI should include the
true value of the measure 95% of the time .”
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Confidence interval (CI)
a = 5%
α/2
1-α
Lower limit
of 95% CI
α/2
upper limit
of 95% CI
s
95% CI = x – 1.96 SE up to x + 1.96 SE
Indicates the amount of random error in the estimate
Can be calculated for any „test statistic“, e.g.: means, proportions, ORs, RRs
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CI terminology
Point estimate
Confidence interval
RR = 1.45 (0.99 – 2.13)
Lower
confidence
limit
Upper
confidence
limit
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Width of confidence interval depends on …
• amount of variability in the data
• size of the sample
• level of confidence (usually 90%, 95%, 99%)
A common way to use CI regarding OR/RR is :
If 1.0 is included in CI  non significant
If 1.0 is not included in CI  significant
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Looking at the CI
A
B
RR = 1
Large RR
Study A, large sample, precise results, narrow CI – SIGNIFICANT
Study B, small size, large CI - NON SIGNIFICANT
Study A, effect close to NO EFFECT
Study B, no information about absence of large effect
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More studies are better or worse?
clinical or
biological
significance ?
20 studies with different
results...

1
RR
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Norovirus on a Greek island
• How confident can we be in the result?
• Relative risk = 21.5 (point estimate)
• 95% CI for the relative risk:
(8.9 - 51.8)
The probability that the CI from 8.9 to 51.8
includes the true relative risk is 95%.
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Norovirus on a Greek island
“The risk of illness was higher among people
who ate raw seafood (RR=21.5, 95% CI 8.9 to
51.8).”
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Example: Chlordiazopoxide use and congenital
heart disease (n=1 644)
C use
No C use
Cases
Controls
4
4
386
1 250
OR = (4 x 1250) / (4 x 386) = 3.2
p = 0.080 ; 95% CI = 0.6 - 17.5
From Rothman K
3.2
p=0.080
0.6 – 17.5
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Example: Chlordiazopoxide use and congenital
heart disease – large study (n=17 151)
C use
No C use
Cases
Controls
240
211
7 900
8 800
OR = (240 x 8800) / (211 x 7900) = 1.3
p = 0.013 ; 95% CI = 1.1 - 1.5
Precision and strength of association
Strength
Precision
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Confidence interval provides more
information than p value
• Magnitude of the effect
(strength of association)
• Direction of the effect
(RR > or < 1)
• Precision of the point estimate of the effect
(variability)
p value can not provide them !
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What we have to evaluate the study
2
Test of association, depends on sample size
p value
Probability that equal (or more extreme)
results can be observed by chance alone
OR, RR
Direction & strength of association
if > 1 risk factor
if < 1 protective factor
(independently from sample size)
CI
Magnitude and precision of effect
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Comments on p-values and CIs
• Presence of significance does not prove
clinical or biological relevance of an effect.
• A lack of significance is not necessarily a lack
of an effect:
“Absence of evidence is not evidence of
absence”.
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Comments on p-values and CIs
• A huge effect in a small sample or a small
effect in a large sample can result in identical
p values.
• A statistical test will always give a significant
result if the sample is big enough.
• p values and CIs do not provide any
information on the possibility that the
observed association is due to bias or
confounding.
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2 and Relative Risk
E
NE
Total
E
NE
Total
Cases
9
5
14
Cases
90
50
140
Non-cases
51
55
106
Non-cases
510
550
1060
Total
60
60
120
Total
600
600
1200
2 = 1.3
p = 0.13
RR = 1.8
95% CI [ 0.6 - 4.9 ]
2 = 12
p = 0.0002
RR = 1.8
95% CI [ 1.3-2.5 ]
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Common source outbreak suspected
Exposure
Yes
No
Total
Cases
15
50
65
23%
Non-cases
20
200
AR%
42.8%
20.0%
220
2
p
RR
95%CI
= 9.1
= 0.002
= 2.1
= 1.4 - 3.4
REMEMBER: These values do not provide any information on the
possibility that the observed association is due to a bias or confounding.
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The ultimative (eye) test
• Hypothesis testing: X²-Test
– Question: Is the proportion of facilitators wearing
glasses equal to the proportion of fellows wearing
glasses?
• Estimation of quantities: Proportion
– What is the proportion of fellows/facilitators
wearing glasses?
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The ultimative (eye) test
Glasses
among fellows :
Glasses
among facilitators :
Yes
No
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Yes
No
6
8
Total
38
Total
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Proportion = 11/38 = 0.29
SE
= 0.074
95%CI
= 0.14 - 0.44
Proportion = 6/14 = 0.43
SE
= 0.132
95%CI
= 0.17 - 0.69
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Recommendations
• Always look at the raw data (2x2-table). How
many cases can be explained by the exposure?
• Interpret with caution associations that
achieve statistical significance.
• Double caution if this statistical significance is
not expected.
• Use confidence intervals to describe your
results.
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Suggested reading
• KJ Rothman, S Greenland, TL Lash, Modern Epidemiology,
Lippincott Williams & Wilkins, Philadelphia, PA, 2008
• SN Goodman, R Royall, Evidence and Scientific Research,
AJPH 78, 1568, 1988
• SN Goodman, Toward Evidence-Based Medical Statistics.
1: The P Value Fallacy, Ann Intern Med. 130, 995, 1999
• C Poole, Low P-Values or Narrow Confidence Intervals:
Which are more Durable? Epidemiology 12, 291, 2001
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Previous lecturers
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Alain Moren
Paolo D’Ancona
Lisa King
Preben Aavitsland
Doris Radun
Manuel Dehnert
Ágnes Hajdu
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