Biostatistics

Question 1

Define sensitivity.

Answer 1

The sensitivity of a test refers to how often a positive result correctly identifies those who have the disease. The higher the sensitivity, the lower the number of results that are falsely negative.

Question 2

How is sensitivity calculated?

Answer 2

Sensitivity = true positive/(true positive + false negative) = true positive / total diseased

Question 3

How can SPIN and SNOUT be used to help differentiate between sensitivity and specificity?

Answer 3

SPIN: SPecific tests help to rule IN a disease because the false Positive rate is low. SNOUT: SeNsitive tests help to rule OUT a disease because the false Negative rate is low.

Question 4

Define specificity.

Answer 4

The specificity of a test determines how often a negative result correctly identifies those who do not have the disease. The higher the specificity, the lower the number of false positive test results (i.e. higher specificity = higher likelihood that a negative result indicates true absence of disease).

Question 5

How is specificity calculated?

Answer 5

Specificity = true negative / (true negative + false positive) = true negative / total not diseased

Question 6

Draw a Bayesian foursquare (test results and disease statuses) and use it to demonstrate how to calculate sensitivity.

Answer 6

***Insert Figure 19-1

Question 7

Draw a Bayesian foursquare (test results and disease statuses) and use it to demonstrate how to calculate specificity.

Answer 7

***Insert Figure 19-1

Question 8

Draw a Bayesian foursquare (test results and disease statuses) and use it to demonstrate how to calculate positive predictive value.

Answer 8

***Insert Figure 19-1

Question 9

Draw a Bayesian foursquare (test results and disease statuses) and use it to demonstrate how to calculate negative predictive value.

Answer 9

***Insert Figure 19-1

Question 10

Define positive predictive value.

Answer 10

The positive predictive value of a diagnostic test is the probability that a patient with a positive test actually has the disease being tested for.

Question 11

How is positive predictive value calculated?

Answer 11

PPV = true positive / (true positive + false positive) = true positive / all positive tests

Question 12

What does the positive predictive value help determine?

Answer 12

PPV helps to determine how effective a test is as a screening tool.

Question 13

Describe the relationship between prevalence of a disease and its effect on sensitivity/specificity vs its effect on positive and negative predictive values.

Answer 13

Sensitivity and specificity are independent of disease prevalence, whereas positive and negative predictive values are influenced by the prevalence of a disease.

Question 14

Define negative predictive value.

Answer 14

Negative predictive value of a diagnostic test is the probability of not having a disease if the test is negative.

Question 15

How is negative predictive value calculated?

Answer 15

NPV = true negative / (true negative + false negative) = true negative / all negative tests

Question 16

Define prevalence.

Answer 16

Prevalence of a disease is the percent of those with the disease in the population being studied.

Question 17

How is prevalence calculated?

Answer 17

Prevalence = total diseased / total population = (TP + FN) / (TP + FN + TN + FP). ***Note that the Bayesian formula is the same for both prevalence and incidence, but the study setup is different.

Question 18

How does prevalence affect how one might interpret a given test result?

Answer 18

Prevalence affects the pretest probability associated with a given test.

Question 19

Define incidence.

Answer 19

The incidence of a disease is the occurrence of new cases of a disease within a specified period of time (it can also be seen as the probability that a person develops that disease during the period of time).

Question 20

How is incidence calculated?

Answer 20

Incidence = total new cases of disease / total # tested during the given time period = (TP + FN) / (TP + FN + TN + FP). ***Note that the Bayesian formula is the same for both prevalence and incidence, but the study setup is different.

Question 21

What happens if you increase the threshold of a test?

Answer 21

If you increase the threshold of a test you get more negative results (both true and false negatives). This decreases the sensitivity and increases the specificity.

Question 22

How can one conceptualize the threshold of a test in relationship with the Bayesian foursquare?

Answer 22

Think of the threshold like the horizontal line of the Bayesian foursquare: as the line moves upward (threshold increases), the total number of negative tests increases; as the line moves downward (threshold decreases), the total number of negative tests decreases. These changes will then be reflected in the sensitivity and specificity calculations due to their affects on the numerators and denominators of those equations.

Question 23

How is sensitivity affected by changes in the threshold of a test?

Answer 23

As the threshold increases, the sensitivity decreases.

Question 24

How is specificity affected by changes in the threshold of a test?

Answer 24

As the threshold increases, the specificity increases.

Question 25

How can one conceptualize the effect of changing disease prevalence/incidence on the false positive and false negative rates of a test using the Bayesian foursquare?

Answer 25

Think of prevalence/incidence as the vertical line. As the line moves left (decreased prevalence/incidence), the number of true positives and false negatives decreases, while the number of false positives and true negatives increases.

Question 26

What are the two primary categories of study design?

Answer 26

Observational and experimental

Question 27

What is the purpose of an observational study, and how are they designed?

Answer 27

Observational studies attempt to correlate exposures with outcomes but do not assign patients to one group or another.

Question 28

What are the two main types of observational study design?

Answer 28

Cohort studies and case-control studies

Question 29

Describe cohort study design.

Answer 29

Cohort studies are a type of observational study which follow groups of individuals prospectively over time to see which exposures cause disease (i.e. babies exposed to thimerosal vs babies not exposed are followed over time to see if there is a correlation with exposure and developmental disorders later in life).

Question 30

Describe case-control study design.

Answer 30

Case-control studies are a type of observational study in which people with the disease are compared to those without the disease to identify relevant risk factors (e.g. to see if there is a relationship between acetaminophen use and development of Reye syndrome).

Question 31

Describe randomized controlled trial study design.

Answer 31

RCTs are a type of experimental study in which the researchers randomly assign patients to different study groups, usually an intervention vs a control.

Question 32

What is the advantage of a randomized trial?

Answer 32

The strength of RCTs is that the randomization process reduces the risk of confounding variables and biases affecting the data, which makes the results more reliable.

Question 33

What are the benefits of observational study design?

Answer 33

Observational studies are less expensive than randomized trials and make it possible to study interventions to which researchers cannot ethically randomize subjects (e.g. smoking carcinogens).

Question 34

What is Type 1 error?

Answer 34

Type 1 error occurs when a study concludes that there is a difference in outcomes when there is not (i.e. rejecting the null hypothesis incorrectly).

Question 35

What statistical value is used to determine the presence of Type 1 error in a study?

Answer 35

Type 1 errors are exposed by investigating the p value (statistical significance) of the results.

Question 36

What is Type 2 error?

Answer 36

Type 2 error occurs when a study concludes that there is no difference in outcomes when there actually is (i.e. failing to reject the null hypothesis).

Question 37

What statistical value is used to determine the presence of Type 2 error in a study?

Answer 37

Type 2 errors are exposed by investigating the power of a study.

Question 38

What p value is considered statistically significant?

Answer 38

A p value ≤ 0.05 is considered statistically significant, with significance increasing as the p value gets smaller.

Question 39

What does the p value represent?

Answer 39

The p value is a way of expressing a study's statistical significance. It represents the probability of finding an association (by chance) when one genuinely does not exist.

Question 40

What is the power of a study?

Answer 40

The power of a study is the probability that it can detect a treatment effect if it is present (i.e. that the test can correctly reject the null hypothesis).

Question 41

How would one increase the power of a study?

Answer 41

The only way to increase the power of a study is to increase the number of subjects, continue the study for a longer duration, or both (basically, the more data you are able to collect, the higher your power).

Question 42

What is considered to be a statistically significant confidence interval?

Answer 42

A CI of 95% is essentially the same as p=0.05. So a consistent result that falls outside the confidence interval of 95% is "statistically significant".

Question 43

What is the function of an odds ratio?

Answer 43

The null hypothesis can be expressed as an odds ratio, which is a way of comparing whether the probability of an event is the same for two groups (control vs study). It is a ratio, so if there is no difference between groups, the ratio would be 1 (same odds = no difference)

Question 44

How is statistical significance represented on confidence interval and/or "forest plot" charts?

Answer 44

The confidence interval is represented by horizontal lines, with the vertical line representing a "no effect" result. Therefore, if the horizontal line crosses the vertical line, the result is not statistically significant.

Question 45

What is the number needeed to treat?

Answer 45

NNT is the number of people who would need to be treated to prevent one event.

Question 46

How is number needed to treat calculated?

Answer 46

NNT = 1/ absolute risk reduction = 1/(control event rate - experimental event rate)

Question 47

What is number needed to harm?

Answer 47

NNH is the number of patients who get a drug or intervention for each patient harmed. The calculation is the same as for number needed to treat, but uses control vs study harm rates rather than rates of positive treatment effects.