Confidence Intervals Nancy D. Barker, M.S.. Statistical Inference.
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Transcript of Confidence Intervals Nancy D. Barker, M.S.. Statistical Inference.
![Page 1: Confidence Intervals Nancy D. Barker, M.S.. Statistical Inference.](https://reader030.fdocuments.net/reader030/viewer/2022032313/56649e695503460f94b6657d/html5/thumbnails/1.jpg)
Confidence Intervals
Nancy D. Barker, M.S.
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Statistical Inference
![Page 3: Confidence Intervals Nancy D. Barker, M.S.. Statistical Inference.](https://reader030.fdocuments.net/reader030/viewer/2022032313/56649e695503460f94b6657d/html5/thumbnails/3.jpg)
Statistical Inference
![Page 4: Confidence Intervals Nancy D. Barker, M.S.. Statistical Inference.](https://reader030.fdocuments.net/reader030/viewer/2022032313/56649e695503460f94b6657d/html5/thumbnails/4.jpg)
Statistical Inference
![Page 5: Confidence Intervals Nancy D. Barker, M.S.. Statistical Inference.](https://reader030.fdocuments.net/reader030/viewer/2022032313/56649e695503460f94b6657d/html5/thumbnails/5.jpg)
Statistical Inference
![Page 6: Confidence Intervals Nancy D. Barker, M.S.. Statistical Inference.](https://reader030.fdocuments.net/reader030/viewer/2022032313/56649e695503460f94b6657d/html5/thumbnails/6.jpg)
Statistical Inference
• Hypothesis Testing– Is there evidence that the population
parameter, e.g., RR, OR, IDR is different from the null value?
• Interval Estimation– How do we determine the precision of the
point estimate by accounting for sampling variability?
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Confidence Intervals
The goal:
Use sample information to compute two numbers, L and U, about which we can claim with a certain amount of confidence, say 95%, that they surround the true value of the parameter.
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General CI Formulas
• Arithmetic scale measures:
• Multiplicative scale measures:
*Note: The variance in this formula refers to the
variance [ln (point estimate)].
variancedeviate normal standard estimatepoint
*variance deviate normal standardestimate)nt exp[ln(poi
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Confidence Interval
• Mean:
n
szx
2
Confidence Level Z-value
90% 1.645
95% 1.960
99% 2.576
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Confidence Interval
• Mean:
Example: Calculate a 95% CI for the mean
Sample Mean: 26.2
Sample standard deviation: s=5.15
Sample size: n=32
n
szx
2
28.0 4.24 32
15.596.12.26
32
15.596.12.26
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Confidence Interval
Calculate a 90% CI for the mean
Sample Mean: 26.2
Sample standard deviation: s=5.15
Sample size: n=32
Calculate a 99% CI for the mean
Sample Mean: 26.2
Sample standard deviation: s=5.15
Sample size: n=32
27.7 7.24 32
15.5645.12.26
32
15.5645.12.26
28.6 9.23 32
15.5576.22.26
32
15.5576.22.26
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Confidence Interval
• Proportion:
Example: Calculate a 95% CI for the proportion
Sample proportion: 0.34
Sample size: n=400
n
ppzp
)1(
2
28.0 4.24 400
)66.0)(34.0(96.1.340
400
)66.0)(34.0(96.134.0
p
p
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95% Confidence Interval
• Difference between proportions:
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95% CI for difference between proportions
• Example
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Interpretation of CI
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Large Sample95% Confidence Interval for RR
• Risk Ratio (multiplicative scale)
Which is equivalent to:
Where,
*Uses a Taylor Series approximation for the variance
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Large Sample 95% Confidence Interval for RR
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Large Sample95% Confidence Interval for OR
• Odds Ratio (multiplicative scale)
Which is equivalent to
*Uses a Taylor Series approximation for the variance
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Large Sample 95% Confidence Interval for OR
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Large Sample95% Confidence Interval for IDR
• Incidence Density Ratio (Multiplicative scale)
Which is equivalent to
*Uses a Taylor Series approximation for the variance
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Large Sample95% Confidence Interval for IDR
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Properties of Confidence Intervals
• The wider the CI, the less precise the estimate.
• The more narrow the CI, the more precise the estimate.
• Note: The confidence interval does not address the issue of bias.
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What affects the Confidence Interval
• The level of confidence
• Sample Size
• Variation in the data
• For RR, OR, IDR, the strength of the association
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Confidence Interval vs. P-value
Similarities– Multiple formulas, (approximate and exact)– Neither account for bias– Statistically equivalent (Theoretically!)
Differences– CI provides same information as a statistical test, plus more– CI reminds reader of variability– CI provides range of compatible values (interval estimation)– CI more clearly shows influence of sample size
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Confidence Interval vs. P-Value
Study # Risk Ratio 95% CI P value
1 2.0 (1.2, 3.3) 0.007
2 7.0 (1.2, 40.8) 0.03
3 7.0 (0.8, 61.3) 0.08
4 0.98 (0.9, 1.07) 0.65
5 0.98 (0.97, 0.99) 0.0001
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Confidence Interval vs. P-Value
Study # Risk Ratio 95% CI P value
6 0.6 (0.5, 0.7) <<0.00001
7 4.0 (0.94, 17.0) 0.06
8 4.0 (0.99, 16.2) 0.052
9 4.0 (1.01, 15.8) 0.048
10 4.0 (2.0, 8.0) 0.00009