Introduction to Biostatistics Exam Solutions
Course Introduction
Introduction to Biostatistics provides students with fundamental concepts and methods used in the collection, analysis, interpretation, and presentation of biological and health-related data. Emphasizing statistical reasoning and the application of quantitative techniques, the course covers descriptive statistics, probability distributions, hypothesis testing, confidence intervals, correlation, regression, and analysis of variance. Students develop essential skills for critically evaluating scientific data, designing experiments, and drawing valid conclusions in fields such as public health, medicine, and biological research.
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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Page 2
Chapter 1: An Overview of Statistical Concepts
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20 Verified Questions
20 Flashcards
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Sample Questions
Q1) Consider a study where the majority of the study subjects have high body mass index (BMI) and only a few have low BMI. When a histogram is constructed to describe the distribution of BMI, what shape of the distribution is most likely to be observed (right-skewed, left-skewed, or symmetric)?
Answer: The distribution would be left-skewed because there would be a peak in the higher BMI categories and few people in the lower and medium BMI categories.
Q2) A 2-year study was conducted to investigate bicycle safety at a city's 10 most congested intersections. The primary variable of interest was the number of accidents that involved a bicycle. Describe the number of accidents involving a bicycle as a continuous and ordinal variable.
Answer: The number of bicycle accidents can be considered as a continuous variable if the number of accidents is large. A mean can be used to calculate the average number of accidents out of the 10 intersections. When the number of accidents is grouped as "large," "medium," and "small," an ordinal variable can be used to describe the amount of accidents.
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3
Chapter 2: Continuous Data- Making Comparisons
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20 Verified Questions
20 Flashcards
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Sample Questions
Q1) A researcher is preparing a grant that will focus on comparing mother's history of drug abuse (history vs. no history) and infant birth weight. The grant proposal requires a sample size calculation for testing the difference between the two groups. In order to calculate the sample size, the researcher needs the following three items:
A) Power, type 1 error rate, and effect size.
B) Confidence interval and power.
C) p-value, power, and effect size.
D) Sample size is not needed.
Answer: A
Q2) The investigator has two options for designing a study. One option is a single-group design where the subjects are measured before and after the intervention. Another option is a two-group design where one group receives the intervention and the other does not. Compare and contrast these two designs. Provide an example for when each would be appropriate.
Answer: With both designs, you are able to compare the intervention and the control groups. When the between-subject variability is considerable, it may be better to conduct the single-group design.
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Page 4
Chapter 3: Continuous Data: Correlation and Regression
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20 Verified Questions
20 Flashcards
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Sample Questions
Q1) A colleague was asked to present his results with 95% confidence intervals instead of using a p-value, but he does not know how to interpret confidence intervals. He conducted a simple linear regression to assess the relationship between body mass index (BMI) and number of cigarettes smoked per day. Your colleague gives you the following information: parameter estimate is 0.10 with a 95% confidence interval of 0.001 to 0.30. Provide an interpretation to your colleague.
Answer: As you increase the number of cigarettes smoked per day by 1 unit, BMI will increase by 0.10 units. A 95% confidence interval provides a set of plausible values for the real slope. The 95% confidence interval does not contain 0, so we are 95% confident that the slope is not zero. More importantly, because the interval estimates suggest that the slope is greater than 0, there is a positive association.
Q2) Data were collected on soy protein consumption and blood pressure. An investigator states that a least squares regression line was fitted to the data. What is the least squares regression line?
Answer: The least squares regression line is the line that has the smallest sum of the squared errors of any line through the data values.
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Chapter 4: Categorical Data: Comparisons and Associations
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20 Flashcards
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Sample Questions
Q1) 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
Q2) Briefly describe the similarity and difference between odds and risks. At what value of odds and risks will the probability of success be larger than the probability of failure?
Q3) The confidence interval width decreases as which of the following increases?
A) level of confidence
B) estimated variance of the proportion
C) sample size
D) size of the null parameter
Q4) Briefly describe the difference between a case-control study and a cohort study in terms of the way data are collected. What measure of association is appropriate in each study?
Q5) When is it appropriate to interpret odds ratio as relative risk?
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Q6) Describe how confidence intervals are used to determine whether an odds ratio is different than 1?
Chapter 5: A Dichotomous Outcome: Confounding and Regression
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23 Verified Questions
23 Flashcards
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Sample Questions
Q1) 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?
Q2) 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.
Q3) 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?
Q4) Briefly explain what a positive and negative parameter estimate (b) implies in a logistic regression in terms of the probability of the outcome.
Q5) 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?
Q6) Define sensitivity and specificity.
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Chapter 6: Count Data
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Sample Questions
Q1) A study is conducted to investigate the incidence of second myocardial infarction (MI). After having the first MI, 40 subjects are followed. With the exception of three patients, all subjects were observed for 10 years. The three exceptions contributed 1.5, 5.3, and 7.5 years. What are the total person-years in the study? If there were 3 events during this study, what is the event rate?
Q2) What techniques can be used to detect overdispersion?
Q3) 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.
Q4) Consider a study where the goal is to determine the association between lung cancer and crystalline silica exposures adjusting for other risk factors. When the p-value associated with age is 0.4532, the researcher decided to keep age in the model. Why?
Q5) Age-adjusted event rates are used to
A) Correct event rates for measurement errors in age.
B) Determine an event rate for each age group.
C) Compare event rates for different populations.
D) Compare event rates of the same age group.
Q6) Define overdispersion. What are the consequences of overdispersion?
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Page 8
Chapter 7: Time to Event Data
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Sample Questions
Q1) Identify the following events as right-censored, left-censored, or interval-censored. Provide a justification for your choice.
a.A person without an event who drops out of the study because he/she is too busy to participate.
b.Suppose the event of interest is the presence of tumors in rats. A rat dies during the study period but the death was before a tumor is detected.
c.Suppose the event of interest is menarche. A female entered the study and the menarche had already occurred.
d.Suppose the event of interest is dental caries in children. A healthy child entered the study with no cavities but came back one year later with a cavity.
Q2) Which of the following equations does NOT represent the proportional-hazards regression model?
A) h(t)= h0(t)(exp(b1x1+b2x2+
B) h(t)=a(t)+b1x1+b2x2+
C) ln(h(t))=a(t)+b1x1+b2x2+
+bKxK
+bKxK))
+bKxK
D) ln(h(t)/h0(t))=b1x1+b2x2+
+bKxK
Q3) Describe in what scenario you might observe this hazard function plot.
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