Biostatistics for Public Health
Chapter Exam Questions
Course Introduction
This course introduces students to the fundamental concepts and methods of biostatistics as applied in public health practice and research. Topics include data types, descriptive and inferential statistics, probability distributions, hypothesis testing, confidence intervals, correlation, regression, and analysis of variance. Emphasis is placed on the critical interpretation of statistical results, the choice of appropriate statistical methods, and the practical application of these techniques to real-world public health data. The course integrates the use of statistical software to analyze datasets and encourages critical thinking about data-driven decision-making in public health contexts.
Recommended Textbook
Biostatistics An Applied Introduction for the Public Health Practitioner 1st Edition by Heather M. Bush
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7 Chapters
143 Verified Questions
143 Flashcards
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Chapter 1: An Overview of Statistical Concepts
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Sample Questions
Q1) Which of the following is NOT an advantage of a retrospective study?
A) Sample size required is relatively small compared to that of a prospective study.
B) Efficient when the outcome is rare.
C) Efficient when the outcome requires a long time to develop.
D) Investigators have control over the way the variables are collected.
Answer: D
Q2) A study was conducted to investigate whether the use of vitamins during prostate cancer treatment would improve the prostate-specific antigen (PSA). PSA in study patients was measured before treatment and four months after treatment. Suppose the change in PSA is 3.0 and there is a p-value of 0.07. How would you interpret this result? Answer: When a p-value is considered small but slightly larger than 0.05, we would say that the change is "marginally significant." We avoid saying that the result is insignificant to rule out the chance of overlooking a change of 3.0 in PSA, which might be clinically significant to the researcher.
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Chapter 2: Continuous Data- Making Comparisons
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Sample Questions
Q1) You have measured the blood lead levels (mg/dL) of a random sample of 30 children living in an urban area. A 90% confidence interval for the mean blood lead levels for the children is computed to be (10.1, 35.8). Provide a valid interpretation of this interval.
Answer: When random samples are taken many times, then approximately 90% of the resulting confidence intervals would cover the accurate mean blood lead level for the children living in this urban area. We are 90% confident that this interval (10.1, 35.8) covers the true mean blood lead level. Therefore, this interval estimate provides a set of plausible values for the true parameter, the true mean blood level for children in this area. According to the Center for Disease Control and Prevention (CDC), a blood lead level of 10 µg/dL or above is problematic.
Q2) When are the mean and median the same?
A) If the distribution is normally distributed.
B) If the distributions are not symmetric.
C) The mean and median can never be the same.
D) The sample size must be small.
Answer: A
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Chapter 3: Continuous Data: Correlation and Regression
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Sample Questions
Q1) A researcher needs your help in identifying the best statistical method for a research study. The researcher believes that blue-collar workers have higher cholesterol levels compared to service and white-collar workers. High levels of cholesterol can lead to heart disease and stroke. The researcher hypothesizes that controlling for race, age, and income, blue-collar workers will have higher mean cholesterol levels compared to the other two groups. Which of the following is the appropriate method to employ?
A) ANCOVA/multiple linear regression
B) Simple linear regression
C) ANOVA
D) Other method
Answer: A
Q2) When employing a multiple linear regression analysis, you realized that the regressors cholesterol and triglycerides are highly correlated. To avoid multicollinearity, what can you do?
Answer: When two variables are highly correlated with each other, there is the issue of multicollinearity. When two variables are highly correlated, it essentially means that they are measuring the same thing. So, it would be best to remove one of the regressors (cholesterol or triglycerides) from the model.
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Chapter 4: Categorical Data: Comparisons and Associations
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Sample Questions
Q1) Identify the following variables as nominal, ordinal, or dichotomous. Provide a justification for your choice.
a.Types of diabetes: type 1 (insulin-dependent), type 2 (non-insulin-dependent), gestational diabetes mellitus.
b.Time to recurrence: 0-2 years, 2-4 years, more than 4 years.
c.Clinic sites: Lexington, Covington, Frankfort, Nashville, Bowling Green.
d.Smoking status: smoker, nonsmoker.
e.Levels of body mass index (BMI): normal, overweight, obese. Types of vaccine administration: oral, injection, puncture.
f.Types of residency: urban, rural.
Q2) Why is it inappropriate to estimate risks directly in a case-control (retrospective) study?
Q3) The chi-square test of independence is used to assess which of the following hypotheses?
A) Whether the samples are independently distributed.
B) Whether the samples are associated with each other.
C) Whether the variables are independently distributed.
D) Whether the variables are associated with each other.
Q4) What is the denominator(s) used to calculate the percentages of the first cell.
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Chapter 5: A Dichotomous Outcome: Confounding and Regression
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Sample Questions
Q1) 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
Q2) 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.
Q3) Which of the following is an appropriate way to handle a confounder?
A) adjustment
B) stratification
C) group matching
D) all of the above
Q4) 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?
Q5) Define sensitivity and specificity.
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Chapter 6: Count Data
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Sample Questions
Q1) Choose the appropriate summary statistic (mean, event rate, or proportion) for the following scenarios. Explain why it is appropriate and how it could be applied.
a.Data on farm injuries are collected over a 10-year period. Describe the number of farm injuries per year that require hospitalization.
b.Data were collected at a sleep clinic to better understand the variables associated with waking during the night. Describe the number of patients who wake up more than once in a night.
c.Surveys were used to investigate smoking patterns in a group of osteoporotic women. At baseline, participants who currently smoked were asked to provide the average number of cigarettes smoked in a day. Describe the average number of cigarettes smoked per day.
Q2) The association between birth defects and smoking during pregnancy is investigated in a low SES population. Vital statistics records were used to determine the number of pregnancies that resulted in birth defects. Suppose that 4 birth defects were observed in a total of 2050 pregnancies. What would be the birth defect rate? What is the event rate per 1000 pregnancies?
Q3) What techniques can be used to detect overdispersion?
Q4) Define overdispersion. What are the consequences of overdispersion?
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Chapter 7: Time to Event Data
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Sample Questions
Q1) Describe the types of outcomes appropriate for time-to-event analyses. Provide a specific example.
Q2) Describe the difference between a survival function and a hazard function?
Q3) In the presence of tied event times (i.e., events that occur at the same time), what methods can be used to compute the likelihood? When would each method be preferred?
Q4) Which of the following statements is NOT true about hazard rates?
A) Two hazard rates at the same time can be compared.
B) They are probabilities.
C) Change in hazard rates can be described by a hazard function.
D) They are not constant over time.
Q5) Which of the following statements is NOT true about the survival function?
A) It describes the event rates.
B) A specific time point of the survival function is a probability.
C) It describes the proportion of event-free people at a particular point of time.
D) They are not constant over time.
Q6) Can the log-rank test be used to compare survival curves at a specific time point? Why or why not?
Q7) Describe in what scenario you might observe this hazard function plot.
Page 9
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