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SAMPLE SIZE

DETERMINATION

By- firoz qureshi

Dept. psychiatric nursing

OUTLINE

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• Our take home…………….

• What is sample size?

• What is sample size determination?

• How large a sample do I need?

• What are the methods of determining it?

• What are the factors that affect it?

• Mind my language

• How do you determine it?

• How do you use it?

• A final word………………..

OUR TAKE HOME

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At the end of this presentation, we should be able to;

Understand the significance of sample size.

Determine sample size.

Understand factors that may affect sample size

Use sample size in our research or study.

WHAT IS SAMPLE SIZE?

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This is the sub-population to be studied in order to

make an inference to a reference population(A

broader population to which the findings from a study

are to be generalized)

In census, the sample size is equal to the population

size. However, in research, because of time

constraint and budget, a representative sample are

normally used.

The larger the sample size the more accurate the

findings from a study.

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Availability of resources sets the upper limit of the

sample size.

While the required accuracy sets the lower limit of

sample size

Therefore, an optimum sample size is an

essential component of any research.

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WHAT IS SAMPLE SIZE

DETERMINATION

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Sample size determination is the mathematical

estimation of the number of subjects/units to be

included in a study.

When a representative sample is taken from a

population, the finding are generalized to the

population.

Optimum sample size determination is required

for the following reasons:

1. To allow for appropriate analysis

2. To provide the desired level of accuracy

3. To allow validity of significance test.

HOW LARGE A SAMPLE DO I

NEED?

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If the sample is too small:

1. Even a well conducted study may fail to answer

it research question

2. It may fail to detect important effect or

associations

3. It may associate this effect or association

imprecisely

CONVERSELY

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If the sample size is too large:

1. The study will be difficult and costly

2. Time constraint

3. Available cases e.g rare disease.

4. Loss of accuracy.

Hence, optimum sample size must be determined

before commencement of a study.

MIND MY LANGUAGE

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Random error

Systematic error

(bias)

Precision (reliability)

Accuracy (Validity)

Null hypothesis

Alternative hypothesis

Type I(a) error

Type II (b) error

Power (1-b)

Effect size

Design effect

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Random error: error that occur by chance. Sources are sample variability, subject to subject differences & measurement errors. It can be reduce by averaging, increase sample size, repeating the experiment.

Systematic error: deviations not due to chance alone. Several factors, e.g patient selection criteria may contribute. It can be reduce by good study design and conduct of the experiment.

Precision: the degree to which a variable has the same value when measured several times. It is a function of random error.

Accuracy: the degree to which a variable actually represent the true value. It is function of systematic error.

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Null hypothesis: It state that there is no

difference among groups or no association

between the predictor & the outcome variable.

This hypothesis need to be tested.

Alternative hypothesis: It contradict the null

hypothesis. If the alternative hypothesis cannot

be tested directly, it is accepted by exclusion if

the test of significance rejects the null

hypothesis. There are two types; one tail(one-

sided) or two tailed(two-sided)

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Type I(a) error: It occurs if an investigator rejects a null hypothesis that is actually true in the population. The probability of making (a) error is called as level of significance & considered as 0.05(5%). It is specified as Za in sample size computing. Za is a value from standard normal distribution ≡ a. Sample size is inversely proportional to type I error.

Type II(b) error: it occur if the investigator fails to reject a null hypothesis that is actually false in the population. It is specify in terms of Zb in sample size computing. Zb is a value from standard normal distribution ≡b

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Power(1-b): This is the probability that the test will correctly identify a significant difference, effect or association in the sample should one exist in the population. Sample size is directly proportional to the power of the study. The larger the sample size, the study will have greater power to detect significance difference, effect or association.

Effect size: is a measure of the strength of the relationship between two variables in a population. It is the magnitude of the effect under the alternative hypothesis. The bigger the size of the effect in the population, the easier it will be to find.

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Design effect: Geographic clustering is generally

used to make the study easier & cheaper to

perform.

The effect on the sample size depends on the

number of clusters & the variance between &

within the cluster.

In practice, this is determined from previous

studies and is expressed as a constant called

‘design effect’ often between 1.0 &2.0. The

sample sizes for simple random samples are

multiplied by the design effect to obtain the

sample size for the cluster sample.

odds ratio is a measure of effect size, describing

the strength of association or non-independence

between two binary data values.

relative risk (RR) is the risk of an event (or of

developing a disease) relative to exposure.

Relative risk is a ratio of the probability of the

event occurring in the exposed group versus a

non-exposed group.

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POWER ANALYSIS

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When the estimated sample size can not be included in a study, post-hoc power analysisshould be carried out.

The probability of correctly rejecting the null hypothesis is equal to 1 – b, which is calledpower. The power of a test refers to its ability to detect what it is looking for.

the power of a test is our probability of finding what we are looking for, given its size.

post-hoc power analysis is done after a study has been carried out to help to explain the results if a study which did not find any significant effects.

AT WHAT STAGE CAN SAMPLE

SIZE BE ADDRESSED?

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It can be addressed at two stages:

1. Calculate the optimum sample size required

during the planning stage, while designing the

study, using appropriate approach & information

on some parameters.

2. Or through post-hoc power analysis at the stage

of interpretation of the result.

APPROACH FOR ESTIMATING

SAMPLE SIZE/POWER ANALYSIS

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Approaches for estimating sample size and

performing power analysis depend primarily on:

1. The study design &

2. The main outcome measure of the study

There are distinct approaches for calculating

sample size for different study designs & different

outcome measures.

1. THE STUDY DESIGN

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There are many different approaches for calculating

the sample size for different study designs. Such as

case control design, cohort design, cross

sectional studies, clinical trials, diagnostic test

studies etc.

Within each study design there could be more sub-

designs and the sample size calculation will vary

accordingly.

Therefore, one must use the correct approach for

computing the sample size appropriate to the study

design & its subtype.

2.PRIMARY OUTCOME

MEASURE

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1⁰ outcome measure is usually reflected in the 1⁰ research question of the study & also depend on the study design.

For estimating the risk in control study, it will be the odds ratio, while for cohort study it will be the relative ratio.

For case control study, it could be the difference in means/proportions of exposure in case & controls, crude/adjusted odds ratio etc.

Hence, while calculating sample size, one of these 1⁰outcome measures has to be specified b/c there are distinct approach for calculating the sample size

statistical inference from the study

results

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In addition, there are also different procedure for

calculating sample size for two approaches of

drawing statistical inference from the study result i.e

1. Estimation (Confidence interval approach)

2. Hypothesis testing(Test of significance approach)

A researcher needs to select the appropriate

procedure for computing the sample size &

accordingly use the approach of drawing a statistical

inference subsequently.

NB: Test of significance: Chi-squared, T-test, Z-test, F-

test, P-value

ADDITIONAL PARAMETERS

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Depending upon the approach chosen for calculating the sample size, one also needs to specify some additional parameters such as;

Hypothesis

Precision

Type I error

Type II error

Power

Effect size

Design effect

PROCEDURE FOR CALCULATING

SAMPLE SIZE.

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There are four procedures that could be used for

calculating sample size:

1. Use of formulae

2. Ready made table

3. Nomograms

4. Computer software

USE OF FORMULAE FOR SAMPLE SIZE

CALCULATION & POWER ANALYSIS

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There are many formulae for calculating sample

size & power in different situations for different

study designs.

The appropriate sample size for population-based

study is determined largely by 3 factors

1. The estimated prevalence of the variable of

interest.

2. The desired level of confidence.

3. The acceptable margin of error.

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To calculate the minimum sample size required for accuracy, in estimating proportions, the following decisions must be taken:

1.Decide on a reasonable estimate of key proportions (p) to be measured in the study

2.Decide on the degree of accuracy (d) that is desired in the study. ~1%-5% or 0.01 and 0.05

3.Decide on the confidence level(Z) you want to use. Usually 95%≡1.96.

4.Determine the size (N) of the population that the sample is supposed to represent.

5.Decide on the minimum differences you expect to find statistical significance.

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For population >10,000.

n=Z2pq/d2

n= desired sample size(when the population>10,000)

Z=standard normal deviate; usually set at 1.96(or a~2), which

correspond to 95% confidence level.

p=proportion in the target population estimated to have a

particular characteristics. If there is no reasonable estimate,

use 50%(i.e 0.5)

q=1-p(proportion in the target population not having the

particular characteristics)

d= degree of accuracy required, usually set at 0.05 level(

occasionally at 2.0)

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E.g if the proportion of a target population with

certain characteristics is 0.50, Z statistics is 1.96

& we desire accuracy at 0.05 level, then the

sample size is

n=(1.962)(0.5)(0.5)/0.052

n=384.

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If study population is < 10,000

nf=n/1+(n)/(N)

nf= desired sample size, when study population <10,000

n= desired sample size, when the study population > 10,000

N= estimate of the population size

Example, if n were found to be 400 and if the population size were

estimated at 1000, then nf will be calculated as follows

nf= 400/1+400/1000

nf= 400/1.4

nf=286

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SAMPLE SIZE FORMULA FOR COMPARISON OF GROUPS

If we wish to test difference(d) between two sub-samples regarding a proportion & can assume an equal number of cases(n1=n2=n’) in two sub-samples, the formula for n’ is

n’=2z2pq/d2

E.g suppose we want to compare an experimental group against a control group with regards to women using contraception. If we expect p to be 40 & wish to conclude that an observed difference of 0.10 or more is significant at the 0.05 level, the sample size will be:

n’= 2(1.96)2(0.4)(0.6)/0.12

=184

Thus, 184 experimental subject & another 184 control subjects are required.

USE OF READYMADE TABLE FOR

SAMPLE SIZE CALCULATION

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How large a sample of patients should be followed up if an investigator wishes to estimate the incidence rate of a disease to within 10% of it’s true value with 95% confidence?

The table show that for e=0.10 & confidence level of 95%, a sample size of 385 would be needed.

This table can be used to calculate the sample size making the desired changes in the relative precision & confidence level .e.g if the level of confidence is reduce to 90%, then the sample size would be 271.

Such table that give ready made sample sizes are available for different designs & situation

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USE OF NOMOGRAM FOR

SAMPLE SIZE CALCULATION

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For use of nomogram to calculate the sample

size, one needs to specify the study(group 1)

& the control group(group 2). It could be

arbitrary or based on study design; the

nomogram will work either way.

The researcher should then decide the effect

size that is clinically important to detect. This

should be expressed in terms of % change in

the response rate compared with that of the

control group.

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E.g if 40% of patients treated with standard

therapy are cured and one wants to know

whether a new drug can cure 50%, one is looking

for a 25% increase in cure rate . (50%-

40%/40% = 25% )

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USE OF COMPUTER SOFTWARE FOR

SAMPLE SIZE CALCULATION & POWER

ANALYSIS

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The following software can be used for calculating

sample size & power;

Epi-info

nQuerry

Power & precision

Sample

STATA

SPSS

Epi-info for sample size

determination

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In STATCALC:

1 Select SAMPLE SIZE & POWER.

2 Select POPULATION SURVEY.

3 Enter the size of population (e.g. 15 000).

4 Enter the expected frequency (an estimate of

the true prevalence, e.g.80% ± your minimum

standard).

5 Enter the worst acceptable result (e.g. 75%) i.e

the margin of error is 5%

How to use sample size formulae

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Steps:

1st Formulate a research question

2nd Select appropriate study design, primary

outcome measure, statistical significance.

3rd use the appropriate formula to calculate the

sample size.

Finally

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Sample size determination is one of the most

essential component of every research/study.

The larger the sample size, the higher the degree

accuracy, but this is limit by the availability of

resources.

It can be determined using formulae, readymade

table, nomogram or computer software.

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STILL CONFUSED………………………..

Call a statistician•Sample selection

•Sample size determination

•Analysis of data

Smart people don’t do it alone…………………

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