流行病学通常被称为公共卫生的“基石”，它是一门研究疾病的分布和决定因素，健康状况，或人群间的活动和应用于控制健康问题的学科。由于流行病学与现实生活息息相关，并更好地评估公共卫生项目和政策，学生将理解流行病学的研究方法，通过这一门课所学到的理论知识应用到当今的公共健康问题。本课程通过流行病学的视框，探讨了心血管疾病和传染病等公共卫生问题，对地区情况和全球情况都进行了讨论。 翻译: Yi Zhou

Loading...

来自 The University of North Carolina at Chapel Hill 的课程

流行病学：基础公共卫生科学

1309 个评分

流行病学通常被称为公共卫生的“基石”，它是一门研究疾病的分布和决定因素，健康状况，或人群间的活动和应用于控制健康问题的学科。由于流行病学与现实生活息息相关，并更好地评估公共卫生项目和政策，学生将理解流行病学的研究方法，通过这一门课所学到的理论知识应用到当今的公共健康问题。本课程通过流行病学的视框，探讨了心血管疾病和传染病等公共卫生问题，对地区情况和全球情况都进行了讨论。 翻译: Yi Zhou

从本节课中

Measures of Association

This module introduces measures of association and confidence intervals.

- Dr. Karin YeattsClinical Associate Professor

Department of Epidemiology, UNC Gillings School of Global Public Health - Dr. Lorraine AlexanderClinical Associate Professor, Director of Distance Learning (North Carolina Institute for Public Health)

Department of Epidemiology, UNC Gillings School of Global Public Health

>> [MUSIC] In this segment, we're going to talk about confidence intervals.

Confidence intervals help us understand the range of variability or uncertainty in

either our measure of association or, our measure of disease occurrence.

After you, you have reviewed this segment, you should be able

to interpret both statistically significant and

non statistically significant measures of association.

Or measures of a disease occurrence and their confidence intervals.

Sometimes you might see a measure of disease occurrence

or a measure of association accompanied by a confidence interval.

What is a confidence interval?

Confidence intervals are a statistical construct

that provide us with information about a

range in which the true value lies with a certain degree of probability.

As well as information about the direction and strength of the effect.

Since we don't know the true value of say a risk ratio or an odds ratio.

We calculate their estimates.

Confidence intervals let us know how much

our estimates of these measures of association might vary.

We can answer the question, what is the range of uncertainty about our estimate?

If we perform an experiment 100 times,

and calculate an estimated risk ratio each time.

The 95% confidence interval is expected to contain the true value of the risk ratio

95 out of 100 times. 95 is a commonly used confidence interval.

However, sometimes you might also see 90 or a 99% confidence intervals.

A quick clarification on interpretation.

When interpreting the 95% confidence interval.

Is it correct to say that there is a

95% probability that the true value lies within the interval?

And the answer is, no. That is not correct.

A probability is relevant to a process, not a specific interval.

Here is the mathematical formula for 95% confidence interval.

The measure of association could be a risk

ratio, a prevalence odds ratio, a rate ratio, etcetera.

You take that estimate and then subtract 1.96 times the standard error of

the point estimate to get the lower 95% confidence bound.

To get the upper 95% confidence bound, you add 1.96

times the standard error.

Note that the 1.96 is specific to the 95% aspect of the confidence interval.

If you wanted to calculate the 99%

confidence interval, you would use the number 2.575.

And for a 90% confidence interval, you would use the number 1.645.

Since this is an introductory epidemiology MOOC, we are not

going to get into the details of how

you calculate the confidence intervals by hand, mathematically.

But it is possible to do so.

What does the confidence interval look like?

It has a lower bound and an upper bound. In this example the 95% confidence

interval is 1.9 to 4.1. In this example if you conducted the study

100 times, approximately 95% of those times the true value would

be contained between the interval of 1.9 and 4.1.

You might ask when looking at this diagram here, why is the estimate

not equal distance from the lower and

upper bounds of the 95% confidence interval?

The answer is that the confidence interval for ratio measures of

effect, such as the odd ratio, rate ratio or risk ratio.

Are computed using a logarithmic scale.

If you take the logarithm, you will see that the

point estimate is equidistant from the lower and upper bounds.

Here's an important point I would like you to remember.

The measure of association or point estimate ie, the risk ratio, odds

ratio, etcetera, will always be somewhere

between your upper and lower confidence interval.

If it isn't, this is a good indicator that something went wrong in your calculation.

For beginning epidemiology

students, there's free software available that you

can use to calculate 95% confidence intervals.

These include Open Epi and EpiSheet.

Using either of these spreadsheets, you can plug in the numbers

for each of the four cells and a 2 by 2 table.

These spreadsheets then calculate the standard

error and those related 95% confidence intervals.

So what is a p-value?

Study results are a combination of real effects and chance.

The p-value is a probability that tells you whether

the study results are consistent with being due to chance.

The p-value does not tell you if the study result was due to chance.

P-values alone do not let us to directly say anything about the

direction or size of a difference

or measure of association between different groups.

So what do the p-value and 95% confidence interval tell us?

Well first we know that the 95% confidence

interval has a relationship with the p-value.

And if the 95% confidence interval does not

include the null value, it is called statistically significant.

When a p-value is less than alpha, which is

usually chosen as 0.05, it may be called statistically significant.

When you have a statistically significant result, it means that

you can reject the null hypothesis, that there's no association

between the exposure and the health outcome.

Confidence intervals contain more information than a p-value.

A confidence interval also tells us the magnitude

of the association between the exposure and a disease.

And it also tells us about the precision of the estimate we obtained.

The narrower the confidence interval, the more precise the estimate.

A clear distinction must be made between statistical significance and clinical

relevance in epidemiologic studies.

The same numerical value for the results may be

statistically significant, if a large sample size was used.

And not significant if the sample size was smaller.

However, study results of clinical relevance are not

automatically unimportant just because there's no statistical significance.

So now let's look at these 95% confidence interval examples.

Which of these confidence intervals is or are, statistically significant?

Which is the most precise?

And another question to think about.

Are narrower confidence intervals more significant?

So, if the confidence interval does not cross the null value, in this case, 1.0.

Because we're talking about a ratio measure.

Then the confidence interval is statistically significant.

Of the examples listed, a, b, and c. C is statistically significant.

C is is statistically significant because it does not cross the null value.

A and B do.

B is a more precise confidence interval compared with A and C.

Why is that?

Well, B is more precise because the confidence interval

is more narrow or smaller compared with A and C.

As

A is the widest interval of these three examples.

It is the least precise confidence interval.

As I pointed out in one of the previous slides, statistical significance of

the confidence interval, depends on whether

the confidence interval includes the null value.

So while B is a more precise estimate, it is not statistically significant.

Because it includes the null value of one.

So here's a quick example for you to test your understanding.

In this example is the risk ratio estimate 2.8 with its 95% confidence intervals

of 1.9 to 4.1, statistically significant? And the answer is yes.

This risk ratio estimate is statistically significant as the 95% confidence

interval of 1.9 to 4.1 does not include the null value of 1.0.

This concludes the segment on confidence intervals.

The most important things to take away from this segment.

And understanding how to interpret

confidence intervals and their statistical significance.

Is if the confidence interval does not include

the null value, then it is statistically significant.

And so for the null value for ratio measures is one.

And for difference measures it's 0.

So if for example if a rate ratio confidence interval

does not include the value one, then it is statistically significant.

And if we were talking about a

difference measure, measure, such as a risk difference.

And the confidence interval around the risk difference did not include

the null value of 0 then it would be statistically significant.

So hopefully you can use this information

when you're reading articles or reading a newspaper

articles that it, that actually include a confidence interval.

And it will help you understand the uncertainty around

that measure of association or measure of disease occurrence.