Module 11

Question 1

What is one of the primary purposes of epidemiology?

Answer 1

To find the causes of dz
-The discovery of risk and protective factors associated with health outcomes statistically
-Which cause or prevent the outcomes

Question 2

What branch of epidemiology can help find the etiology of a dz?

Answer 2

Analytic epidemiology
-Tries to test “a priori” hypotheses (theoretical ideas) about the causes of determinants of health in populations

Question 3

What questions do analytic studies answer?

Answer 3

Is there a statistical association between exposure and dz?
Is the association “real”, i.e., causal?
This is to r/o the 1st of the possible explanations for the results: That the results were entirely d/t chance or random occurrences.

Question 4

Parameter definition

Answer 4

Actual indicator in the entire source pop or pop of interest

Question 5

Statistic definition

Answer 5

Indicator that the investigator found in the sample or study pop

Question 6

What are the major sources of error in epidemiologic research?

Answer 6

Random (or chance) errors (reliability)
Systematic errors (bias)

Question 7

What is random error?

Answer 7

Is another way of saying there is lack of reliability in the study

Question 8

What does high random error indicate?

Answer 8

Low reliability

Question 9

What does low random error indicate?

Answer 9

High reliability

Question 10

Imprecise results are __________

Answer 10

Unreliable results

Question 11

How are imprecise results unreliable results?

Answer 11

If the results are imprecise, the possibility of random error is also high.

Question 12

How do imprecise results occur?

Answer 12

When the factor being measured is not measured sharply
Analogous to aiming a rifle at a target that is not in focus

Question 13

How can precision be increased?

Answer 13

By increasing sample size or the number of measurements

Question 14

In what ways do random errors reflect fluctuations occurring by chance around a true value of a parameter?

Answer 14

Sampling error
Variability in the data itself
Imprecision in measurement

Question 15

Reliability relates to ______

Answer 15

Repeatability

Question 16

When does sampling error occur?

Answer 16

Brief reminder: epidemiologic studies draw inferences about the experiences of an entire pop based on an eval of only a sample
Occurs when the sample selected is not representative of the source pop for reasons other than systematic bias in the way ppl were recruited.

Question 17

How is sample size related to chance?

Answer 17

Characteristics of participants in a sample may vary from sample to sample. As a result, an association between an exposure and outcome, or lack thereof, may vary by chance.
Sample size is directly related to chance. The larger the sample size, the more pop variability it can reflect.
To minimize sampling error d/t chance, increase the sample size.

Question 18

How does variability affect error?

Answer 18

If there is a lot of variability in the data, there is more possibility of random error than if the data are less variable.
If the pop contains a lot of variability, the chances of drawing an unrepresentative sample go up.

Question 19

Imprecision in measurement details

Answer 19

The lack of agreement in results from time to time reflects random error inherent in the type of measurement procedure employed.

Question 20

What is an example of imprecision in measurement?

Answer 20

Two readings of BP of the same persons using the same instrument may be different

Question 21

How do we decrease random error d/t imprecision in measurement?

Answer 21

Take more measurements and average them

Question 22

When is it difficult to get a statistically significant result?

Answer 22

When the sample size is small, or measurements have wide variation and/or are imprecise

Question 23

What makes it harder to reject the null hypothesis?

Answer 23

Increasing random error

Question 24

What do statistically insignificant results not represent?

Answer 24

A study limitation, unless the study was statistically underpowered (sample size was too small) or measurements were too imprecise

Question 25

Definition of hypothesis

Answer 25

A tentative suggestion that certain associations exist

Question 26

How is hypothesis used in epi?

Answer 26

To eval suggestions about cause-effect relationships

Question 27

On what are hypotheses based?

Answer 27

On learned and scientific observation from which theories or predictions are made Development of hypotheses is based on scientific approaches, using facts in the analyses and in a manner that makes common sense, based on rational scientific knowledge.

Question 28

What type of reasoning does hypothesis generation involve?

Answer 28

Inductive reasoning No "a priori hypothesis" Often based on descriptive or preliminary data Leads to the creation of a tentative hypothesis

Question 29

What type of reasoning does hypothesis testing involve?

Answer 29

Deductive reasoning There is an "a priori" hypothesis Confirmed (or not) in an analytic study

Question 30

Seven steps in hypothesis testing for assessing an association

Answer 30

1. Formulate the null hypothesis 2. Formulate the alternative (a priori, research) hypothesis 3. Set the significance level (0.05) and sample size 4. Recruit the sample. 5. Collect the data 6. Analyze the data (odds ratio, relative risk, etc.) and calculate the test statistic (p-value or confidence interval) 7. Either reject or fail to reject the null hypothesis and draw conclusions from the results.

Question 31

Null hypothesis definition

Answer 31

There is no difference among the groups being compared There is no relationship between the exposure and the dz

Question 32

How is the research question expressed?

Answer 32

The alternative hypothesis expressed in question form

Question 33

Calculating significance tests

Answer 33

In order to decide whether or not to reject or fail to reject the null hypothesis, a test statistic is computed and compared with a critical value obtained from a set of statistical tables. However, calculating them is only the first step. Is our odds ratio statistically significant?

Question 34

Significance level: α

Answer 34

This is arbitrarily set. Usually is 0.05, but could be 0.01, 0.001, or 0.1 The significance level is the chance of rejecting the null hypothesis when, in fact, it is true (type I error)

Question 35

The P-value, what is it?

Answer 35

All tests of statistical significance lead to a probability statement, which is usually expressed as a p-value.

Question 36

P-value definition

Answer 36

The p-value indicates the probability that findings at least as extreme as those observed were likely to have occurred by chance alone, given there is truly no relationship between exposure and dz.

Question 37

What does a p-value really tell you?

Answer 37

In other words, if the null hypothesis is true, and there really is NO relationship between the exposure and the dz, what is the probability of getting the result that you did by chance?

Question 38

What does a p-value of 0.05 mean?

Answer 38

Means that 5% of the time we would get a result as extreme or greater than the one we got 95 times out of 100 we would get a result that is less extreme Only 5 times out of 100 it would be more extreme If the exposure is NOT related to the dz

Question 39

Rejecting the null hypothesis

Answer 39

If p <0.05, we conclude that chance is an unlikely explanation for the finding. -These results would occur by chance only very rarely The null hypothesis is rejected and the statistical association is said to be significant.

Question 40

Failing to reject the null hypothesis

Answer 40

If p ≥ 0.05, we conclude that chance cannot be excluded as an explanation for the finding -And we fail to reject the null hypothesis -That is: --These results are likely to occur by chance at least 5% of the time.

Question 41

Statistical significance and chance

Answer 41

A statistically significant finding does not mean that the results DID NOT occur by chance. -Only that it is unlikely that the findings did occur by chance A non-significant finding does not mean that there is not an association -Only that the probability that our results are due to chance is too high for us to feel comfortable enough to reject the null.

Question 42

In statistics, nothing is ______

Answer 42

Absolute No p-value, however small, completely excludes chance. No p-value, however large, completely mandates (ensures) chance

Question 43

Definitions of confidence interval

Answer 43

A range of values that one can say, with a scientific degree of confidence, contain the true pop value (the parameter) A computed interval of values that, with a given probability, contain the true value of the population parameter Confidence interval indicates the range within which the true magnitude of effect (parameter) lies with a certain degree of assurance

Question 44

Format of confidence interval

Answer 44

The degree of confidence is usually stated as a percentage Commonly the 95% confidence interval is used. This corresponds to an alpha of 0.05

Question 45

What two things does confidence interval tell us?

Answer 45

If the statistic (called the point estimate) is statistically significant compared to a predetermined cut-off point (same as a p-value) How precise the statistic (or point estimate) is

Question 46

What is the null value?

Answer 46

The value indicating no association For all ratios, such as relative risks, odds ratios, prevalence ratios, standardized mortality rates: null = 1.00 For correlation coefficients or other proportions: null = 0.00

Question 47

Rejecting the null hypothesis if the null value is not included inside the confidence interval

Answer 47

For ratios, this means that 1.00 is NOT in the confidence interval Then p < the value of alpha that we choose We say the results are statistically significant

Question 48

Failing to reject the null hypothesis if the null value is included in the confidence interval

Answer 48

For ratios, this means that 1.000 IS in the confidence interval Then, the corresponding p-value is, by definition, > alpha. We cannot reject the null hypothesis, and we say the results are not statistically significant.

Question 49

Confidence intervals give all of the information of a p-value plus...

Answer 49

The expected range of effect sizes What our odds ratios or relative risks might have been if we had a different sample of ppl

Question 50

What is the width of the confidence interval influenced by?

Answer 50

The variability of the data, sample size, the precision of your measurements, and the percentage of the confidence interval that you choose.

Question 51

Narrow vs wide confidence intervals

Answer 51

A narrow confidence interval is very precise. A wide one lacks precision. Confidence intervals get narrower if your data is more reliable, meaning there is less random error. -Confidence intervals get narrower if there are more ppl in your sample -Confidence intervals get narrower (more precise or more certain) if the underlying data have less variation/scatter -Confidence intervals get narrower if measurements are precise

Question 52

Meaning of a 90% confidence interval

Answer 52

If we drew 100 samples and repeated the study 100 times.... And it is a 90% confidence interval, 90 times out of 100, our odds ratio would fall within this confidence interval -10 times out of 100, our results would be outside the original confidence interval -We can be 90% sure that the true value (parameter) is within this confidence interval (given there is no bias) -alpha = 0.1

Question 53

What to take into account with confidence intervals that have amounts that are below 1

Answer 53

They appear more narrow than they actually are

Question 54

Are statistical and clinical (or practical) significance the same?

Answer 54

No In very large samples, while small differences in dz frequency or low magnitudes of relative risk may be statistically significant, they may have no clinical or practical significance. Conversely, with small sample sizes, large differences or measures of effect may be clinically important, even though they may not be statistically significant.

Question 55

Statistical significance depends partly on what?

Answer 55

Sample size A small difference (or weak odds ratio, relative risk, correlation coefficient, etc.) may achieve statistical significance if the sample size is large. A large difference (or strong odds ratio, relative risk, correlation coefficient, etc.) may not achieve statistical significance if the sample size is too small. Insufficient statistical power.

Question 56

What is the meaning of statistically significant?

Answer 56

A statistically significant association (usually p<0.05 or 95% CI that does not include 1.00) means that the likelihood of getting the results that we got purely by chance is low So, we reject the null hypothesis, and say our results were unlikely to have been d/t chance, and were driven by something, but we still do not conclude they were causal, or always important

Question 57

What does statistical significance indicate?

Answer 57

We have jumped the first hurdle in hypothesis testing. We can then say that there appears to be an association between exposure and outcome, and that this association is not likely to be d/t chance or random error We cannot yet determine cause

Question 58

What do statistical tests not tell you?

Answer 58

If the study design is valid If the results are clinically, biologically, or sociologically meaningful If the association is causal

Question 59

Definition of attributable risk

Answer 59

The quantity of dz incidence (or mortality) that can be attributed to a specific exposure

Question 60

Question and purpose of relative risk

Answer 60

Is there an association between an exposure and a dz? Valuable in etiologic studies

Question 61

Questions and purpose of attributable risk

Answer 61

Given that the exposure is a cause of the dz, how much of the dz can be attributed to the exposure? What is the public health impact of this exposure? How much of the risk (incidence) of dz can we hope to prevent if we are able to eliminate exposure to the agent in question? Valuable in the making of clinical or policy decisions and evaluating the impact of a prevention program

Question 62

What is the purpose of risk difference and attributable risk among the exposed?

Answer 62

Used to guide clinical advice

Question 63

What is another name for attributable risk among the exposed?

Answer 63

Etiologic fraction

Question 64

What is the purpose of population risk difference and population attribute risk?

Answer 64

Used to guide policy decisions or to help decide whether to implement a given intervention

Question 65

What is background risk?

Answer 65

A certain amount of dz occurs in the not exposed. This is the background risk The attributable risk uncovers how much is attributable to the exposure, apart from the background risk.

Question 66

What is incidence in the exposed a combination of?

Answer 66

Incidence not due to the exposure (background incidence) + incidence due to the exposure

Question 67

Risk difference formula

Answer 67

Incidence in the exposed group - incidence in the not exposed group or Ie- Ine

Question 68

Interpretation sentence for risk difference

Answer 68

_____ per _____ of the incident cases of ______ among ______ are attributable to their _______.

Question 69

Attributable risk among the exposed formula

Answer 69

Risk difference/incidence in the exposed group or (Ie - Ine)/ Ie

Question 70

Interpretation of attributable risk among the exposed

Answer 70

____% of incident cases of _____ among _____ are attributable to their _____.

Question 71

What questions are asked in terms of population attributable risk?

Answer 71

What would the impact on the entire pop be if _____ were eliminate? How much (or what proportion) of a dz in the entire pop could be prevented if we could eliminate this exposure? By how much would the overall dz rate decrease? What is the impact of an entirely successful health promotion program to eliminate an exposure?

Question 72

Definition of population attributable risk

Answer 72

The proportion of the rate of the dz in the pop that is d/t the exposure.

Question 73

Impact of removing an exposure from a population: prevalence of exposure

Answer 73

If a higher proportion of ppl have the exposure, the impact on the population of eliminating it will be higher.

Question 74

Impact of removing an exposure from a pop: relative risk

Answer 74

If the association between exposure and dz is stronger, the impact on the pop of eliminating it will be higher.

Question 75

Impact of removing an exposure from a pop: incidence rate of the dz

Answer 75

The higher the incidence rate of the dz, the greater the number (not proportion) of cases that could be prevented

Question 76

Influence of the prevalence of the exposure on the population attributable risk

Answer 76

If the exposure to be eliminated is smoking, and 100% of the pop smokes, the population attributable risk - the attributable risk among the exposed (all are exposed). If 0% of the population smokes, the population attributable risk = 0 (none are exposed). If 1-99% of the pop smokes, the impact lies between 0 and the attributable risk among the exposed

Question 77

Importance of the prevalence of exposure to the population attributable risk

Answer 77

If the prevalence of exposure in that pop is low, regardless of a high relative risk, the impact of eliminating that exposure will also be low. If the prevalence of exposure in that pop is high, regardless of a moderate relative risk, the impact of eliminating that exposure can also be high.

Question 78

Of what is incidence in the total population a combination of?

Answer 78

Incidence due to all other causes and Incidence d/t the exposure of interest

Question 79

Definition of risk difference

Answer 79

The absolute amount of incidence of dz in the exposed that is attributable to the exposure

Question 80

Population risk difference definition

Answer 80

The absolute amount of dz incidence in the total pop attributable to the exposure

Question 81

Population risk difference formula

Answer 81

Incidence of the dz in the entire pop - incidence of dz in the not exposed or Ip - Ine

Question 82

Interpretation of pop risk difference

Answer 82

_____per _____ of the incident cases of _____ in the entire pop are attributable to ______.

Question 83

Population attributable risk definition

Answer 83

The proportion of risk in the entire pop d/t the exposure

Question 84

Population attributable risk formula

Answer 84

Population risk difference / incidence of the dz in the entire population or (Ip - Ine)/ Ip

Question 85

Interpretation of population attributable risk

Answer 85

___% of incident cases of ___ in the whole population are attributable to _____.