A2.5 Sample size calculations for cross-sectional studies (or surveys)

A2.5 PRACTICAL: R

Example of estimating sample size for a hypothesis in a cross-sectional study

You have been asked to help with a power calculation for a cross-sectional study, to estimate the point prevalence of obesity within a population. A study five years ago in this population found that 30% of people were obese, but the government thinks this has increased by 10% (to a point prevalence of 40%). Estimate the sample size needed for this study, assuming that the previous point prevalence of 30% is your `null hypothesis’. You want 80% power.

You are calculating a sample size for one proportion here.

The command is now ‘pwr.p.test’:

> power8<-pwr.p.test(h=ES.h(p1=0.3, p2=0.4), power=0.8, sig.level=0.05)
> power8

proportion power calculation for binomial distribution (arcsine transformation)

h = 0.2101589
n = 177.7096
sig.level = 0.05
power = 0.8
alternative = two.sided

You need about 178 participants in your study to estimate this prevalence.

Question A2.5: One researcher has suggested that the proportion of the population who is obese may actually have decreased by 10% in the last five years (i.e. to 20%). How would this change your estimate for the sample size needed?

Answer

You are calculating a sample size for one proportion here.

> power9<-pwr.p.test(h=ES.h(p1=0.3, p2=0.2), power=0.8, sig.level=0.05)
> power9

proportion power calculation for binomial distribution (arcsine transformation)

h = 0.2319843
n = 145.8443
sig.level = 0.05
power = 0.8
alternative = two.sided

The estimated sample size has now reduced slightly, to 146.

Based the outputs above, we can conclude that more data are needed to detect a change in proportion from 0.3 to 0.4 than from 0.3 to 0.2. For a fixed absolute difference (here the absolute difference in proportions is 0.1), larger sample sizes are needed to obtain a given level of power as the proportions approach 0.5. This relationship is symmetrical around 0.5, as shown below:

> power9<-pwr.p.test(h=ES.h(p1=0.1, p2=0.2), power=0.8, sig.level=0.05)
> power9

proportion power calculation for binomial distribution (arcsine transformation)

h = 0.2837941
n = 97.45404
sig.level = 0.05
power = 0.8
alternative = two.sided

> power10<-pwr.p.test(h=ES.h(p1=0.9, p2=0.8), power=0.8, sig.level=0.05)
> power10

proportion power calculation for binomial distribution (arcsine transformation)

h = 0.2837941
n = 97.45404
sig.level = 0.05
power = 0.8
alternative = two.sided

Recall that the standard error (se) of the sampling distribution of p is $LaTeX: \sqrt{\frac{p\left(1-p\right)}{n}}$ .

As p gets closer to 0.5, the amount of variability increases (se is largest when p=0.5) and, therefore, more data are needed to detect a change in proportions of 0.1.

A2.5 PRACTICAL: Stata

Example of estimating sample size for a hypothesis in a cross-sectional study

You have been asked to help with a power calculation for a cross-sectional study, to estimate the point prevalence of obesity within a population. A study five years ago in this population found that 30% of people were obese, but the government thinks this has increased by 10% (to a point prevalence of 40%). Estimate the sample size needed for this study, assuming that the previous point prevalence of 30% is your `null hypothesis’.

You are calculating a sample size for one proportion here.

The command is:

power oneproportion 0.3, diff(0.1)

This could also be calculated using:

power oneproportion 0.3 0.4, power(0.8)

*–Estimated sample size: N = 172

Question A2.5: One researcher has suggested that the proportion of the population who is obese may actually have decreased by 10% in the last five years (i.e. to 20%). How would this change your estimate for the sample size needed?

Answer

You are calculating a sample size for one proportion here.

power oneproportion 0.3, diff(-0.1)

Or alternatively:

power oneproportion 0.3 0.2

*– Estimated sample size: N = 153

The estimated sample size has now reduced slightly, to 153.

Based the outputs above, we can conclude that more data are needed to detect a change in proportion from 0.3 to 0.4 than from 0.3 to 0.2. For a fixed absolute difference (here the absolute difference in proportions is 0.1), larger sample sizes are needed to obtain a given level of power as the proportions approach 0.5. This relationship is symmetrical around 0.5, as shown below:

power oneproportion 0.1 0.2

*– Estimated sample size: N = 86

power oneproportion 0.9 0.8

*– Estimated sample size: N = 86

Recall that the standard error (se) of the sampling distribution of p is $LaTeX: \sqrt{\frac{p\left(1-p\right)}{n}}$ . As p gets closer to 0.5, the amount of variability increases (se is largest when p=0.5) and, therefore, more data are needed to detect a change in proportions of 0.1.

A2.5 PRACTICAL: SPSS

Example of estimating sample size for a hypothesis in a cross-sectional study

You have been asked to help with a power calculation for a cross-sectional study, to estimate the point prevalence of obesity within a population. A study five years ago in this population found that 30% of people were obese, but the government thinks this has increased by 10% (to a point prevalence of 40%). Estimate the sample size needed for this study, assuming that the previous point prevalence of 30% is your `null hypothesis’.

Select

Analyze >> Power Analysis >> Proportions >> One Sample Binomial Test

Input the values form the scenario into the Power Analysis window as before. In this example the Population proportion is the predicted value of 40% (0.4) and the null value is the previous prevalence of 30% (0.3).

One researcher has suggested that the proportion of the population who is obese may actually have decreased by 10% in the last five years (i.e. to 20%). How would this change your estimate for the sample size needed?

Answer

For the first part of the question, when the hypothesis is that we will see a 40% prevalence, the output table will look like this.

The estimated sample size we need to have a power of 80% is 172.

In the second part of the question, when the hypothesis is that we will see a 20% prevalence, the output table will look like this.

The estimated sample size has now reduced slightly, to 153.

Based the outputs above, we can conclude that more data are needed to detect a change in proportion from 0.3 to 0.4 than from 0.3 to 0.2. For a fixed absolute difference (here the absolute difference in proportions is 0.1), larger sample sizes are needed to obtain a given level of power as the proportions approach 0.5. This relationship is symmetrical around 0.5, as shown below:

Recall that the standard error (se) of the sampling distribution of p is $LaTeX: \sqrt{\frac{p\left(1-p\right)}{n}}$ . As p gets closer to 0.5, the amount of variability increases (se is largest when p=0.5) and, therefore, more data are needed to detect a change in proportions of 0.1

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A2.5 Sample size calculations for cross-sectional studies (or surveys)

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Example of estimating sample size for a hypothesis in a cross-sectional study

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