Introduction to Biostatistics Final Exam Questions
Course
Introduction
Introduction to Biostatistics provides students with fundamental concepts and methods used in the application of statistics to biological and health sciences. The course covers descriptive statistics, probability theory, sampling techniques, hypothesis testing, confidence intervals, and the basics of study design. Emphasis is placed on interpreting statistical results in biomedical contexts, using real-world data, and applying statistical software to analyze biological and health data. Students will develop the skills necessary to critically evaluate scientific literature and effectively communicate statistical findings in the context of biostatistics.
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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20 Verified Questions
20 Flashcards
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Sample Questions
Q1) John waited 30 minutes to be treated in an emergency room. A 30-minute wait is in the 20th percentile of the wait time. Did he have a comparatively long- or short wait time? Interpret this percentile.
Answer: John had a short wait time. In the emergency room, 20% of the patients were treated in less than 30 minutes and 80% of the patients had to wait more than 30 minutes.
Q2) Briefly describe the difference between a bar graph and a histogram. Consider your own area of interest. Provide an example where a bar graph would be most appropriate. Provide an example where a histogram would be most appropriate.
Answer: A bar graph is often used to show the visual comparison of a categorical variable. A histogram is typically used to describe the distribution of a categorized continuous variable. Generally, bar graphs have a gap between the bars, but the bars in a histogram touch each other.
Q3) Suppose you are told that your BMI is 32, the 70th percentile for your age and sex.
Interpret this percentile.
Answer: This means that among 100 people of your age and sex, your BMI is the 70th. This also indicates that 70% of the 100 typical people of your age and sex have BMI less than 32.
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3
Chapter 2: Continuous Data- Making Comparisons
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Sample Questions
Q1) You have decided to conduct a study where you are interested in observing mean differences in blood lead levels (mg/dL) in individuals who drink well water versus those who drink tap water. A previous study reported an effect size of 0.10, and you use this in planning your study. This effect size is considered small, what impact will a small effect size have on sample size planning? Explain.
Answer: If the researcher wants to observe small effect sizes, then the sample size will need to be large in order to have sufficient power.
Q2) An investigator collected HbA1c levels on a sample of 10 patients who are suspected to be at risk for diabetes. The values for the 10 patients are 2, 2, 2, 4, 4, 6, 7, 7, 9, and 9. The mean level was 5.2 with a standard deviation of 2.78. The median was 5 with an interquartile range of 2 and 7. The investigator adds two more patients with values 1 and 12. Explain what will happen to the center and spread of this distribution.
Answer: The spread and mean will increase because of the value 12. The median will not be affected by the additions.
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4
Chapter 3: Continuous Data: Correlation and Regression
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Sample Questions
Q1) 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.
Q2) 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
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Page 5
Chapter 4: Categorical Data: Comparisons and Associations
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Sample Questions
Q1) When is it appropriate to interpret odds ratio as relative risk?
Q2) Which of the following is NOT required to determine the sample size for a test of two proportions?
A) power
B) significance level
C) proportion estimates
D) standard deviation
Q3) Suppose an odds ratio between gender (male, female) and smoking status (yes, no) is 2.5. Explain why it is incorrect to interpret the odds ratio as "the probability of smoking for males is 2.5 times that for females." Provide a correct interpretation.
Q4) Assume the estimated odds ratio of obesity for the amount of weekly exercise (in minutes) is 0.93 (95% CI: 0.88, 0.99). Interpret this odds ratio for every minute and for 30-minute increases in exercising time.
Q5) Describe how confidence intervals are used to determine whether an odds ratio is different than 1?
Q6) Briefly explain how the expected and observed cell counts are obtained. How do they influence the significance of the hypothesis test?
Q7) 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 represents an odds ratio in a logistic regression?
A) estimate of b
B) logit difference (logit=log(odds))
C) exp(b)
D) exp(logit)
Q2) 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?
Q3) 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
Q4) 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.
Q5) 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?
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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) Suppose the event rate of cavity per child-tooth is 0.6. If a child has 3 teeth, how many cavities are likely to be observed in this child? Explain.
Q3) Consider your own area of research. Provide an example where it would be appropriate to use Poisson regression. Defend your answer.
Q4) Define overdispersion. What are the consequences of overdispersion?
Q5) What techniques can be used to detect overdispersion?
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Chapter 7: Time to Event Data
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Sample Questions
Q1) Describe how an interaction with time in a proportional-hazards model can be used to check the proportional hazards assumption.
Q2) The major assumption of proportional-hazards regression is that the hazards are proportional. Explain what this means.
Q3) Can the log-rank test be used to compare survival curves at a specific time point? Why or why not?
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) What is the difference between a life table method and Kaplan-Meier method in estimating the survival probability? Provide an example when the life table method is preferred to the Kaplan-Meier method.
Q6) The log-rank test CANNOT be used to
A) compare two survival curves
B) compare multiple survival curves
C) compare two survival curves while adjusting for continuous covariates
D) compare hazard ratios
Page 9
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