Question 1

Which (A, B, or C) represents the best screening test? Why? What does curve C tell about the screening test?

Accepted Answer

Curve A represents the best screening test because it has the highest sensitivity and specificity. Curve C has an area under the curve of 0.5. This implies that the probability of a correct diagnosis is like flipping a coin. Thus, the screening test of curve C is not useful.

Question 2

Which one of the following is implied by a screening test with a low sensitivity?
A) The test yields a large number of false positives.
B) The test yields a large number of false negatives.
C) The test yields a large number of true positives.
D) The test yields a large number of true negatives.

Accepted Answer

Sensitivity of a screening test is the ability to correctly identify diseased people who have disease. If the sensitivity is small, the test does a poor job in correct identification. In other words, it is likely to identify diseased people as healthy, thus yielding a large number of false negatives.

Question 3

When the area under the ROC curve is 0.5
A) The diagnostic test is very helpful.
B) The diagnostic test is somewhat helpful.
C) The diagnostic test is not helpful.
D) It cannot be determined from the information given.

Accepted Answer

An area under the ROC curve of 0.5 means that the diagnostic test is correct 50% of the time. Thus, it is not helpful.

Question 4

Define sensitivity and specificity.

Accepted Answer

The answer of Define sensitivity and specificity....

Question 5

Which of the following characterizes a good screening test?
A) high sensitivity and high specificity
B) high sensitivity and low specificity
C) low sensitivity and high specificity
D) low sensitivity and low specificity

Accepted Answer

Sensitivity and specificity are the ability to correctly identify people as diseased or non-diseased. Thus, high sensitivity and high specificity of a screening test are preferred.

Question 6

In a body mass index (BMI) test, a cutoff point is established such that an individual is classified as obese when test score is above the cutoff point. As the cutoff score increases, how will the sensitivity and specificity of the test change?

Accepted Answer

The answer of In a body mass index (BMI) test,...

Question 7

How does the magnitude of the parameter estimate (b) in a logistic regression influence the interpretation of the risk factor?

Accepted Answer

The answer of How does the magnitude of the parameter...

Question 8

Which of the following is an appropriate way to handle a confounder?
A) adjustment
B) stratification
C) group matching
D) all of the above

Accepted Answer

A confounder can be accounted for in different types of experimental design. Generally, the most common ways to adjust a confounder are 1) to include it in the model, 2) to perform a stratified analysis on the confounder if it is categorical, or 3) to match cases and controls by the confounder in the experimental design process (i.e., to obtain equal number of males and females if the confounder is gender specific).

Question 9

Given that an interaction effect is found significant in the logistic regression model, the main effects associated with the interaction should NOT be interpreted independently. Why? What should be reported/interpreted when an interaction is present?

Accepted Answer

The answer of Given that an interaction effect is found...

Question 10

Three diagnostic tests (Pearson, Deviance, Hosmer-Lemeshow) can be used to evaluate the model fit of logistic regression. Provide the null hypothesis for a test of model fit. What would you conclude if the hypothesis is not rejected?

Accepted Answer

The answer of Three diagnostic tests (Pearson, Deviance, Hosmer-Lemeshow) can...

Question 11

Which of the following is a logistic regression model? (p=probability of an event)
A) p=a+b1x1+ · · · +bkxk
B) p/(1-p)= a+b1x1+ · · · +bkxk
C) log(p)= a+b1x1+ · · · +bkxk
D) log(p/(1-p))= a+b1x1+ · · · +bkxk

Accepted Answer

The logistic regression equation is characterized by the fact that the dependent variable is log(odds)=log(p/(1-p)). This transformation is to make two sides of the equation comparable.

Question 12

What is the rationale behind using logit (log(odds)) and not odds or probability of the event as the outcome variable in logistic regression?

Accepted Answer

The answer of What is the rationale behind using logit...

Question 13

When the Breslow-Day test results in a significant p-value
A) The estimated odds ratios are different.
B) The estimated odds are different.
C) The estimated relative risks are different.
D) The estimated risks are different.

Accepted Answer

The null hypothesis of the Breslow-Day test states that the estimated odds ratios were the same. A significant p-value would reject this null hypothesis and lead to the conclusion that the ratios are different (effect modifier).

Question 14

The Mantel-Haenszel's odds ratio is often used to account for confounding. When a confounding variable is found to be an effect modifier, why is it not appropriate to report Mantel-Haenszel odds ratios?

Accepted Answer

The answer of The Mantel-Haenszel's odds ratio is often used...

Question 15

Briefly explain what a positive and negative parameter estimate (b) implies in a logistic regression in terms of the probability of the outcome.

Accepted Answer

The answer of Briefly explain what a positive and negative...

Question 16

In a logistic regression model, explain how to determine whether a confounder is an effect modifier.

Accepted Answer

The answer of In a logistic regression model, explain how...

Question 17

Which of the following yields the correct estimated odds ratio for a 5-unit increase in an explanatory variable?
A) 5b
B) exp(5b)
C) 5exp(b)
D) exp(5)(exp(b))

Accepted Answer

Since log(odds_x=5)-log(odds_x=0)=(a+5b)-(a+0)=5b, odds ratio_(x=5 vs. x=0)=exp(5b).

Question 18

Which of the following represents an odds ratio in a logistic regression?
A) estimate of b
B) logit difference (logit=log(odds))
C) exp(b)
D) exp(logit)

Accepted Answer

b represents the difference between two fitted logistic regressions for 1 unit increase in the corresponding covariate. So, exp(b)=exp(log(odds ratio))=odds ratio.

Question 19

Suppose that the estimated odds ratio of asthma is reported to be 0.45 (95% CI: 0.1, 0.8) for children living near farms (measured in kilometers, km), adjusting for age, race, and sex. Interpret.

Accepted Answer

The answer of Suppose that the estimated odds ratio of...

Question 20

List situations when Mantel-Haenszel's method for calculating an adjusted odds ratio is no longer appropriate. What type of analysis is an alternative in these situations?

Accepted Answer

The answer of List situations when Mantel-Haenszel's method for calculating...

Quiz 5: A Dichotomous Outcome: Confounding and Regression

Which (A, B, or C) represents the best screening test? Why? What does curve C tell about the screening test?

Which one of the following is implied by a screening test with a low sensitivity?

When the area under the ROC curve is 0.5

Define sensitivity and specificity.

Which of the following characterizes a good screening test?

In a body mass index (BMI) test, a cutoff point is established such that an individual is classified as obese when test score is above the cutoff point. As the cutoff score increases, how will the sensitivity and specificity of the test change?

How does the magnitude of the parameter estimate (b) in a logistic regression influence the interpretation of the risk factor?

Which of the following is an appropriate way to handle a confounder?

Given that an interaction effect is found significant in the logistic regression model, the main effects associated with the interaction should NOT be interpreted independently. Why? What should be reported/interpreted when an interaction is present?

Three diagnostic tests (Pearson, Deviance, Hosmer-Lemeshow) can be used to evaluate the model fit of logistic regression. Provide the null hypothesis for a test of model fit. What would you conclude if the hypothesis is not rejected?

Which of the following is a logistic regression model? (p=probability of an event)

What is the rationale behind using logit (log(odds)) and not odds or probability of the event as the outcome variable in logistic regression?

When the Breslow-Day test results in a significant p-value

The Mantel-Haenszel's odds ratio is often used to account for confounding. When a confounding variable is found to be an effect modifier, why is it not appropriate to report Mantel-Haenszel odds ratios?

Briefly explain what a positive and negative parameter estimate (b) implies in a logistic regression in terms of the probability of the outcome.

In a logistic regression model, explain how to determine whether a confounder is an effect modifier.

Which of the following yields the correct estimated odds ratio for a 5-unit increase in an explanatory variable?

Which of the following represents an odds ratio in a logistic regression?

Suppose that the estimated odds ratio of asthma is reported to be 0.45 (95% CI: 0.1, 0.8) for children living near farms (measured in kilometers, km), adjusting for age, race, and sex. Interpret.

List situations when Mantel-Haenszel's method for calculating an adjusted odds ratio is no longer appropriate. What type of analysis is an alternative in these situations?