Sampling and Sample Size DeterminSampling and Sample Size Determinationation
Power and sample size. Objectives Explain why sample size is important Explain what makes up a...
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Transcript of Power and sample size. Objectives Explain why sample size is important Explain what makes up a...
![Page 1: Power and sample size. Objectives Explain why sample size is important Explain what makes up a sample size calculation Demonstrate sample size calculations.](https://reader036.fdocuments.net/reader036/viewer/2022081505/551aa6cf550346856e8b488a/html5/thumbnails/1.jpg)
Power and sample size
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Objectives
• Explain why sample size is important• Explain what makes up a sample size
calculation• Demonstrate sample size calculations• Explain the level of understanding that you
need for the FCEM• Hope that you retain some/any of this
information once you leave the room
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FCEM – what do you need?
• Any comparative or diagnostic study needs a power calculation
• The number of study participants must reach this number
• If it doesn’t ask yourself why not– incorrect assumptions?– untoward events?
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Altman
“...a trial should be big enough to have a high chance of detecting, as statistically
significant, a worthwhile effect if it exists and thus be reasonably sure that no benefit
exists if it is not found..”
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Why is sample size important?
1. Need to get an answer
2. Need to get the right answer
3. Need to be sure we get the right answer
4. Avoid exposing too many participants than is necessary to get the answer
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Study process
Planning
Data collection and processing
Data analysis and presentation
Data presentation
Data interpretation
Publication
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Hypotheses
• Research looks to answer a hypothesis
• Hypotheses are (statistically) easier to “prove” if they start from a null hypothesis:– “there is no difference between treatment A and
treatment B in treating X”– “there is no difference between test C and test
D in diagnosing Y”
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Answers
• Four ways to get an answer:– Correct answer for the correct reason– Correct answer for the wrong reason– Incorrect answer for the correct reason – Incorrect answer for the wrong reason
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Truth table
Truth
There is a difference
There is no difference
Null Hypothesis
Rejectedi.e. a
difference was found
Type I error
Accepted i.e. no
difference was found
Type II error
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Truth table
Truth
There is a difference
There is no difference
Null Hypothesis
Rejectedi.e. a
difference was found
Type I error
Accepted i.e. no
difference was found
Type II error
![Page 11: Power and sample size. Objectives Explain why sample size is important Explain what makes up a sample size calculation Demonstrate sample size calculations.](https://reader036.fdocuments.net/reader036/viewer/2022081505/551aa6cf550346856e8b488a/html5/thumbnails/11.jpg)
Generic sample size equation
n ~ 2(z1-α/2 + z1-β)2 σ2
Δ2
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Sample size
n ~ 2(z1-α/2 + z1-β)2 σ2
Δ2
• n is the sample size
• α is the significance level - often set at 0.05 so we accept a 5% chance of making a type I error
• 1-β is the power – often set at 0.8 so we accept an 80% chance of avoiding a type II error
• Δ is the effect size
• σ is the variance within the population
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Factors affecting sample size
• The precision and variance of measures within any sample
• Magnitude of a clinically significant difference
• How certain we want to be of avoiding a type I error
• The type of statistical test we are performing
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Precision and variance
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Clinically significant difference
• Very small differences require very precise estimates of the true population values
• But is it clinically important?
• At great effort we could demonstrate a 2mmHg difference in blood pressure between two drugs
• But is it clinically important?
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Standardised difference
• Based upon the ratio of the difference of interest to the standard deviation of those observations
• Calculated in a different way depending on whether the data is continuous or categorical
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Continuous data
• So if we were assessing an antihypertensive and wanted a 10mm difference between the drugs and the population standard deviation was 20mm then the standardised difference would be 0.5
Standardised difference = difference between the means population standard deviation
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Categorical data
• P1 is the baseline mortality
• P2 is the “new” mortality we expect
• P is 0.5(P1 + P2 )
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Standardised difference and power
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Gore and Altman nomogram
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Gore and Altman nomogram
0.01
0.05
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Diagnostic studies - sensitivity
TP = true positive rate, FN = false negative rate,
SN = sensitivity, P = prevalence
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Diagnostic studies - specificity
FP = false positive rate, TN = true negative rate
SP = specificity, P = prevalence
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Questions?
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FCEM – what do you need?
• Any comparative or diagnostic study needs a power calculation
• The number of study participants must reach this number
• If it doesn’t ask yourself why not– incorrect assumptions?– untoward events?