Question 1:
|
BMI
< 30 |
BMI
30 - 34.9 |
BMI
Greater than 35 |
Total |
| Pre-term |
320 |
80 |
120 |
520 |
| Full-term |
4700 |
480 |
300 |
5480 |
| Total |
5020 |
560 |
420 |
6000 |
The table shows the classification of women in a study by their BMI at 16 weeks gestation and whether they had pre-term delivery.
What is the probability that a woman delivers pre-term?
Question 2:
|
BMI
< 30 |
BMI
30 - 34.9 |
BMI
Greater than 35 |
Total |
| Pre-term |
320 |
80 |
120 |
520 |
| Full-term |
4700 |
480 |
300 |
5480 |
| Total |
5020 |
560 |
420 |
6000 |
The table shows the classification of women in a study by their BMI at 16 weeks gestation and whether they had pre-term delivery.
What is the probability that a woman has BMI less than 30?
Question 3:
|
BMI
< 30 |
BMI
30 - 34.9 |
BMI
Greater than 35 |
Total |
| Pre-term |
320 |
80 |
120 |
520 |
| Full-term |
4700 |
480 |
300 |
5480 |
| Total |
5020 |
560 |
420 |
6000 |
The table shows the classification of women in a study by their BMI at 16 weeks gestation and whether they had pre-term delivery.
What is the probability that a woman has BMI less than 30and delivers pre-term?
Question 4:
|
BMI
< 30 |
BMI
30 - 34.9 |
BMI
Greater than 35 |
Total |
| Pre-term |
320 |
80 |
120 |
520 |
| Full-term |
4700 |
480 |
300 |
5480 |
| Total |
5020 |
560 |
420 |
6000 |
The table shows the classification of women in a study by their BMI at 16 weeks gestation and whether they had pre-term delivery.
What proportion of women with a BMI greater than 35 delivers full-term?
Question 5:
|
HIV + |
HIV- |
Total |
| Test + |
35 |
10 |
45 |
| Test - |
5 |
50 |
55 |
| Total |
40 |
60 |
100 |
The table shows the results from assessing the diagnostic accuracy of a new rapid test for HIV in 100 subjects, compared to the reference standard enzyme-linked immunosorbent assay (ELISA) test. The rows of the table represent the test result and the columns the true disease status (as confirmed by ELISA).
What is the sensitivity of the new rapid test for HIV? Interpret your results.
Question 6:
|
HIV + |
HIV- |
Total |
| Test + |
35 |
10 |
45 |
| Test - |
5 |
50 |
55 |
| Total |
40 |
60 |
100 |
The table shows the results from assessing the diagnostic accuracy of a new rapid test for HIV in 100 subjects, compared to the reference standard enzyme-linked immunosorbent assay (ELISA) test. The rows of the table represent the test result and the columns the true disease status (as confirmed by ELISA).
What is the specificity of the new rapid test for HIV? Interpret your results.
Question 7:
|
HIV + |
HIV- |
Total |
| Test + |
35 |
10 |
45 |
| Test - |
5 |
50 |
55 |
| Total |
40 |
60 |
100 |
The table shows the results from assessing the diagnostic accuracy of a new rapid test for HIV in 100 subjects, compared to the reference standard enzyme-linked immunosorbent assay (ELISA) test. The rows of the table represent the test result and the columns the true disease status (as confirmed by ELISA).
What is the Positive Predictive Value (PPV) for the new rapid test for HIV in this cohort? Interpret your results.
Question 8:
|
HIV + |
HIV- |
Total |
| Test + |
35 |
10 |
45 |
| Test - |
5 |
50 |
55 |
| Total |
40 |
60 |
100 |
The table shows the results from assessing the diagnostic accuracy of a new rapid test for HIV in 100 subjects, compared to the reference standard enzyme-linked immunosorbent assay (ELISA) test. The rows of the table represent the test result and the columns the true disease status (as confirmed by ELISA).
What is the Negative Predictive Value (NPV) for the new rapid test for HIV in this cohort? Interpret your results.
Question 9: Short Essay
1. Compare and contrast binomial and normal distributions. Give an example of each being used in a public health setting. You do not need to provide calculations but rather, a summary of the scenario and the data to which the distribution would be applied and evaluated. Your essay should be at least 250 words in length.
2. Explain in your own words how probability laws and concepts are used in the evaluation of screening tests and diagnostic criteria in public health. Offer two examples to support your explanation. You do not need to provide calculations but rather, a summary of the scenario and the data to which the distribution would be applied and evaluated.
3. Explain in your own words the difference between a random experiment and a trial. Illustrate each by giving a real-world example of how each has been applied in a public health setting in the last decade. Be sure to include specifics to validate your claim in at least 250 words