Computational Public Health Statistics Final Exam / S00
Part A (Closed Book)

Instructions: Please write your answers on the separate paper provided. Please number your responses. You may do this part of the exam either before or after Part B, but once you turn it in, there is no going back to it. This part of the exam should take no more 30 minutes to complete. [Square brackets contain point values.]

1. Define "confounding." [2]

2. Define "statistical interaction." [2]

3. Define "bias."[2]

4. Define "alpha."[2]

5. List the three forms of bias found in epidemiologic studies. [3]

6. Briefly discuss key differences between cohort and case control studies. [4]

7. Fill in the blank: The power of a case-control study depends on the study's alpha level, group sample sizes / allocation ratio, exposure proportion in controls and the ______________________ "worth detecting." [1]

8. Identify the key distinction between observational and experimental studies. [2]

9.Briefly, why is Bonferroni's adjustment (or Bonferroni-like adjustments) necessary when performing multiple comparisons? [2]

10. Complete this sentence: In order for an extraneous factor to confound the relationship between an exposure and disease, it must: [2]

Computational Public Health Statistics Final Exam / S00
Part B (Procedure Notebook)

EXF-S00.REC: Death rates associated with exposure (cohort study)

The data set EXF-S00.REC contains the variables EXPOSED (1 = yes, 2 = no), DEATH (1 = yes, 2 = no), and ELDERLY (1 = yes, 2 = no). Data are from a cohort study on survival following exposure to something or other.

1. Calculate the overall death rate in the sample. Include relevant counts (X of n), the point estimate of the incidence of death, and a 95% confidence interval for p. Test whether the death rate is significantly different from 25%. (List the null and the alternative, set an alpha level, report the name of the test being used, report the p value and the conclusion. Is the difference significant?) [8]

2. Determine the relationship between EXPOSED and DEATH. Calculate the death rates in the exposed and unexposed group. Report relevant counts and percentages (p^1 and p^2). Then calculate the relative risk of death associated with exposure. Report the 95% confidence interval for the RR. Test the RR for significance. Include all hypothesis testing steps (hypotheses, let alpha = .05, and so on). [10]

3. Stratify the data by age as encompassed in the variable ELDERLY. Determine the strata specific relative risk of DEATH associated with EXPOSED. The chi-square test for interaction. List the null and alternative hypotheses. Let alpha = .05. Report the chi-square statistic for interaction and its degrees of freedom. Report the conclusion of the test. Is there significant evidence of interaction? [9]

4. Evaluate the potential for interaction. In so doing, compare the crude relative risk to the Mantel-Haenszel adjusted relative risk. Report an unbiased estimate of relative risk and justify why you chose to either ignore the potential confounder or adjust for it. [5]

BREASTCA: Breast cancer and nulliparity (case-control data).

5. A case-control study looks at 400 cases and 400 controls. Of the 400 cases, 250 had not borne offspring ("nulliparity"). Among the 400 controls, 200 were nulliparious. Show these data in two-
by-two table format. What percentage of cases are nulliparious? What percentage of controls are nulliparious? Calculate the odds ratio of breast cancer associated with nulliparity. Include a confidence interval for the OR. Perform a test of association. List the null and alternative hypotheses, let alpha = .05, report the Yate's corrected chi-square statistic, its degrees of freedom, and p value. State your conclusion. Interpret your results by describing the magnitude and significance of the association. [12]

TRIAL: Three Group Clinical Trial

Fifteen people are randomly assigned to one of three groups. Group 1 receives the standard treatment, group 2 receives the experimental treatment, and group 3 receives a placebo. Clinical improvements scores for the study are:

Group 1: 4, 2, 3, 3, 3
Group 2: 7, 9, 6, 6, 6
Group 3: 7, 8, 4, 7, 9

6. Create a QES file for these data. (Remember to create separate independent and dependent variables!) Then create a REC file, and enter the data into this file. Report descriptive statistics of the scores by group. Then perform Bartlett's test (list the null and alternative, let alpha = .05, test stat, conclusion). Finally, perform an ANOVA analysis, listing all steps of the procedure (null and alternative, etc.) Do not perform multiple comparisons, even if this seems necessary. Interpret your results. [22]