sample size determination€¦ · • calculate the sample size for the detectable difference of...
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![Page 1: Sample Size Determination€¦ · • Calculate the sample size for the detectable difference of prevalence 20%. It means P 0 = 27% and P 1 = 47%. 90 students in each group. • Calculate](https://reader034.vdocuments.us/reader034/viewer/2022050112/5f499c1f7a875058690ac4b0/html5/thumbnails/1.jpg)
Assoc Prof Dr Sarimah binti Abdullah
Unit of Biostatistics & Research Methodology
Universiti Sains Malaysia
Sample Size Determination
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Why do we need to calculate
the sample size?
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Sample Size Calculation
A) for Estimation
i. a mean
ii. a proportion
B) for Hypothesis Testing
i. compare means
ii. compare proportions
iii. correlation
C) Diagnostic test
D) Validation
SINGLE MEAN FORMULA
SINGLE PROPORTION FORMULA
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Sample Size Calculation
for Estimation
1. to estimate the duration of exercise among
adult in Kg X.
2. to estimate the prevalence of obesity
in Kg X.
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Estimating a mean
• A study is planned to estimate the duration
of exercise among adult in Kampong X.
• The result should be reported as "mean
duration of exercise (DEx.) and its 95% CI".
e.g. mean DEx. 16.5 mins/day (95% CI: 15.5, 17.5)
The value of a study can be judged by the width
of Confidence Interval.
Wide CI means .. a poor study.
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If we plan for 95% confidence (5% error), so Z = 1.96;And SD (σ ) is estimated as 4.3 (Duration of Exercise) (either by previous study or a pilot study; if previous study, state
the reference)
To estimate duration of exercise
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If we plan for 95% confidence (5% error), so Z = 1.96;And SD (σ ) is estimated as 4.3 (Duration of Exercise) (either by previous study or a pilot study; if previous study, state
the reference)
Impossible to check for
normality assumption
Now, it is the researcher decision to select which sample size
will be appropriate for the study.
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How to report? (in Methodology)
• Sample size was determined as follows.
• The following formula (Daniel, 1999) is used to
calculate the sample size.
Z = 1.96 for 95% confidence
σ = SD of DEx. = 4.3 (Brian, 2002??)
∆ = Precision = 1 min/day
• We need 72 people in order to estimate the mean DEx. with
the precision of 1 min/day.
• We decided to take 90 people (additional 20%) for
anticipated non-response cases.
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Sample Size Calculation
for Estimation
1. to estimate the duration of exercise among
adult in Kg X.
2. to estimate the prevalence of obesity
in Kg X.
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Estimating a Proportion
• A study is planned to estimate the prevalence of
obesity in Kampong X.
• The result should be reported as Prevalence
(Proportion) of obesity and its 95% CI".
In our example data,
we get 37% (95% CI: 27%, 47%).
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If we plan for 95% confidence (5% error), Z = 1.96,
and P is estimated as 40% (Prevalence of Obesity) (Literature or Pilot study).
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(1) Generally, smaller precision is better.
(2) However, commonly, researchers are limited with
the availability of resources .
(3) It may depend on previous studies:
- Previous studies have reported with a certain width of CIs
in their studies. Somehow, if we want to repeat the study,
we should come out with a better width of CI (added value).
Setting the level of confidence is conventional at 95% (Z = 1.96).
P or SD is estimated by the literature or pilot study.
The remaining question is "HOW TO DECIDE THE PRECISION?".
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If we plan for 95% confidence (5% error), Z = 1.96,
and P is estimated as 40% (Prevalence of Obesity) (Literature or Pilot study).
Now, it is the
researcher
decision to
select which
sample size will
be appropriate
for the study.
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Relationship between P & Sample Size
0
100
200
300
400
500
600
700
800
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
P
Sa
mp
le S
ize
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Sample Size Calculation
for Hypothesis Testing
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Sample Size Calculation
- For Hypothesis Testing
3. to compare duration of exercise between male
and female
4. to compare prevalence of obesity between male
and female
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Important Concepts
1. Type I (α error)
2. Type II (β error) / Power of the Study (1-β)
3. Detectable Difference (Detectable
Alternative)
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Test
DecisionCorrectType I
Type IICorrect
FalseTrue
Null Hypothesis (Ho)
Reject Ho
Do not reject Ho
Power of the study
"Power to reject the false null
hypothesis"
0.05
0.2
80%
Important Concepts
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Detectable Difference
1. What is Detectable Difference?
2. How to decide the Detectable
Difference?
Important Concepts
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What is Detectable Difference (Detectable Alternative)?
• The “minimum size of the difference between groups”
that the study could detect !!!
Important Concepts
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60.0 Kg 60.1 Kg≈
60.0 Kg 60.5 Kg<
What is Detectable Difference (Detectable Alternative)?
Important Concepts
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What is Detectable Difference (Detectable Alternative)?
• The “minimum size of the difference between groups”
that the study could detect !!!
• "The study could detect" means ...
➢ Let's say, you are comparing means of 2 groups, and in
reality, the 2 means are truly different.
➢ And also at the end of the study, you get the result as "two
group means are significantly different" (one is more than
the other).
➢ It means that "you detect the difference".
➢ Let's say, you get the result "the difference is not
significant" ... meaning that "you fail to detect it".
Important Concepts
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How to decide Detectable Difference?
It should reflect the “Clinically Significant
Difference” (CSD).
We should be able to detect the “CSD”.
In other words, we should design a study to
detect CSD.
Comparing means of two (2) POPULATIONS
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• Comparing 2 means
• Comparing 2 proportions
Using PS software ….
http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/PowerSampleSize
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• Comparing 2 means
Using PS software ….
Researchers want to compare duration of exercise
between male and female college students.
Objective: To compare mean duration of exercise
between male and female students
( )2
2
βα/2
2 ZZ2σn
Δ
+=
Alpha (0.05)
Power (start with 80%)σ = SD (within group SD of duration of
exercise)
∆ = Detectable Difference (Clinically important
difference)
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1
2
3
Detectable Difference
SD from other study or pilot study
Ratio between 2 groups (m=1 means 1:1)
4. Fill all 5 inputs5
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Detectable Difference
SD from other study or pilot study
Ratio between 2 groups (m=1 means 1:1)
4. Fill all 5 inputs
With the
sample size
33 in each
group, we
will achieve
80% power
to detect the
difference of
3 mins/day
(DEx.) with
the Alpha at
0.05.
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With the sample size 33 in each group,
we will achieve 80% power
to detect the difference of 3 mins/day (DEx.)
with the Alpha at 0.05.
Example:
Say, in really, the difference is 5 mins/day between male
and female.
With this sample size, you have at least 80% chance to
get the ‘significant’ or ‘positive’ result. (You have at least
80% power to reject the Null).
Say, if the difference is 1 mins/day. So, this sample size
will fail to detect this difference. But it’s OK, we don’t
want to detect this small size. It is not clinically/
practically important.
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IN SUMMARY, for comparing 2 means
We need to decide ….
Alpha (0.05; consensus – 0.05)
Power (80%=0.8)
SD (variable of interest – from previous study or pilot study)
Detectable Difference (should reflect clinical/practical importance)
Ratio of sample size between 2 groups (m = 1 “1:1”; m=2 “2:1”)
--------------------------------------------------------------------------
How to report?We use PS software (Dupont & Plummer, 1997) to calculate the
sample size based on comparing 2 means.
To detect the difference of 3 mins/day (Duration of Exercise) with
80% power and alpha 0.05, we need 33 students in each study
group (SD was estimated as 4.3, reference?).
We have decided to take 40 male and 40 female students
(additional 20%) with the anticipation of some non-responses.
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Exercise
• Researchers want to compare the SBP between treated and
untreated hypertension patients.
• A recent study revealed that the SD of SBP among hypertension
patients was 10 mmHg (state ref.??).
• The researchers feel that it is important to detect the difference
of 5 mmHg between 2 study groups.
• They plan to take equal sample size (1:1) for 2 study groups
(m=1).
• Set alpha at 0.05 as usual.
• Calculate the sample size to achieve the power of 80%.
64 in
each
group
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• Researchers want to compare the SBP between treated and
untreated hypertension patients.
• SD was 10 mmHg (state reference??).
• DD sets at 5 mmHg.
• They plan to take 1:2 ratio for untreated: treated (m=2) (because
difficult to find ‘untreated’).
• Set alpha at 0.05 as usual.
• Calculate the sample size to achieve the power of 80%.
48 untreated
and 96 treated
hypertensive
patients
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• Comparing 2 proportions
Using PS software ….
Researchers want to compare prevalence of obesity
between male and female students.
Objective: To compare the prevalence of obesity
between male and female students.
Remember … We have to set Alpha, Power and
Detectable Difference.
Alpha = 0.05
Power = 80% (0.8)
Detectable Difference = ???
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• Comparing 2 proportions
Using PS software ….
Researchers want to compare prevalence of obesity
between male and female students.
Alpha (0.05)
Power (80% = 0.8)
∆ = Detectable Difference (Clinically important difference) (P1-P0)
P0 = Prevalence of obesity among male (Get from literature)
P1 = Prevalence of obesity among female (Set based on
desired DD)
m = 1 (equal ratio between male and female)
Objective: To compare the prevalence of obesity
between male and female students.
![Page 34: Sample Size Determination€¦ · • Calculate the sample size for the detectable difference of prevalence 20%. It means P 0 = 27% and P 1 = 47%. 90 students in each group. • Calculate](https://reader034.vdocuments.us/reader034/viewer/2022050112/5f499c1f7a875058690ac4b0/html5/thumbnails/34.jpg)
• Comparing 2 proportions
Using PS software ….
Researchers want to compare prevalence of obesity
between male and female students.
Alpha (0.05)
Power (80% = 0.8)
∆ = Detectable Difference (Clinically important difference) (P1-P0)
P0 = 0.27 (Say, we get from literature)
P1 = 0.37 (This is our decision based of DD. Here, we put
0.37. It means that we are setting the DD in this study as 0.10
or 10%)
m = 1 (equal ratio between male and female)
Objective: To compare the prevalence of obesity
between male and female students.
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1
2
(P1-P0) is Detectable Difference.
Ratio between 2 groups (m=1 means 1:1)
4. Fill all 5 inputs
5
P0 – from previous or pilot study
3
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1
2
(P1-P0) is Detectable Difference.
Ratio between 2 groups (m=1 means 1:1)
4. Fill all 5 inputs
5
P0 – from previous or pilot study
3
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IN SUMMARY, for comparing 2 proportions
We need to decide ….
Alpha (0.05; consensus – 0.05)
Power (80% = 0.8)
Po (Prevalence of obesity among male, 27% (reference?)
Detectable difference (should reflect clinical/practical importance – in this
example, 10% difference is decided. Therefore, P1 = 37%)
Ratio between 2 groups (m=1 “1:1”; m=2 “2:1”)
--------------------------------------------------------------------------
How to report?
We use PS software (Dupont & Plummer, 1997) to calculate the sample size
based on comparing 2 proportions.
To detect the difference of 10% in prevalence of obesity (P0 27% versus P1
37%) between the 2 study groups with 80% power and alpha 0.05, we need
340 male and 340 female students. (Po, the prevalence of obesity among
male was estimated as 27%, ref?) (You may add some e.g. 10%)
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Exercise!• Calculate the sample size for the detectable
difference of prevalence 20%. It means P0 = 27% and
P1 = 47%.90 students in
each group.
• Calculate the sample size for the detectable
difference of prevalence 20% (as above).
• And male: female ratio as 2:1 (m=2).
66 female & 132
male students.
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Comparing 3 means
Using G power….
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Correlation…using online http://www.medic.usm.my/biostat/
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C) DIAGNOSTIC TESTS –eg :sensitivity and specificity
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http://www.medic.usm.my/biostat/C) DIAGNOSTIC TESTS –eg :kappa aggrement
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http://www.medic.usm.my/biostat/C) DIAGNOSTIC TESTS –eg :kappa aggrement
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http://www.medic.usm.my/biostat/D) Validation
–eg :Cronbach alpha
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http://www.medic.usm.my/biostat/D) Validation
–eg :intraclass correlation
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• For each specific objective, should calculate the
sample size.
• Sometimes, in one objective, more than one variables
of interest (multiple linear regression).
• In this case, we need to calculate for each
variable of interest.
• Then, the biggest sample size will be “the sample size
of the study”.
• We need to add-up 10-20% because we may get non-
response, loss of follow up, or any other loss.
Final COMMENTS
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Acknowledgement
Special thank to :
Assoc Prof Dr Mohd Ayub Saddiq
Thank You.