Introduction to Biostatistics Practice Questions
Course Introduction
Introduction to Biostatistics provides students with a foundational understanding of statistical methods and principles used in the analysis of biological, medical, and public health data. The course covers essential topics such as data collection, data summarization, probability, sampling distributions, hypothesis testing, confidence intervals, and basic statistical inference techniques. Students will gain practical experience in applying statistical reasoning to real-world problems using appropriate software tools, enabling them to critically interpret and communicate the results of scientific studies in the health sciences.
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
Source URL: https://quizplus.com/quiz/12175
Sample Questions
Q1) 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.
Q2) Which of the following is most likely a type 2 error?
A) Correctly concluding that an effect exists.
B) Correctly concluding that no effect exists.
C) Falsely concluding that an effect exists.
D) Falsely concluding that no effect exists.
Answer: D
Q3) 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
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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) 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) Cotinine levels have been used as biomarkers to assess tobacco exposure among women. There have been questions on whether increased cotinine levels increase an individual's reactive hyperemia index (RHI), which is a measure of endothelial function and cardiovascular risk. For your study, you gathered cotinine and RHI data on 200 women. You are interested in the correlation between the two variables. You calculate a correlation (r) to be 0.017 with a 95% confidence interval (-0.2405, 0.2716). What does this suggest?
Answer: Based on the correlation, there appears to be no relationship between RHI and cotinine levels. The correlation is near zero. The confidence interval contains zero, so we cannot rule out 0 as a possible parameter. It is possible that RHI and cotinine levels are not related.
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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5
Chapter 4: Categorical Data: Comparisons and Associations
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20 Flashcards
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Sample Questions
Q1) 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
Q2) 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?
Q3) In the following scenarios, p = the probability of lung cancer. If the odds ratio estimate is 1.2, interpret for the following variables:
a.age (continuous)
b.education level (graduate school vs. high school)
c.race (Black vs. White)
d.smoking status (smoker vs. nonsmoker)
Q4) What is the denominator(s) used to calculate the percentages of the first cell.
Q5) 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.
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Chapter 5: A Dichotomous Outcome: Confounding and
Regression
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23 Verified Questions
23 Flashcards
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Sample Questions
Q1) 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.
Q2) What is the rationale behind using logit (log(odds)) and not odds or probability of the event as the outcome variable in logistic regression?
Q3) Briefly explain what a positive and negative parameter estimate (b) implies in a logistic regression in terms of the probability of the outcome.
Q4) 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)
Q5) How does the magnitude of the parameter estimate (b) in a logistic regression influence the interpretation of the risk factor?
Q6) Define sensitivity and specificity.
Page 7
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Chapter 6: Count Data
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Sample Questions
Q1) A study was performed to investigate disease relapses between two groups of Crohn's disease patients. All subjects entered the study with the disease under control. Suppose 18 patients in group A experienced relapses, and only 5 patients in group B experienced relapses. Why would it be incorrect to say that patients in group A have higher risks of relapse than those in group B? What information do we need to make a justified comparison?
Q2) When the event rate is of interest, which of the following equations represents the Poisson regression model? (µ=true mean count of events, t=subject years)
A) µ/t = + <sub>1</sub>x<sub>1</sub>+ <sub>2</sub>x<sub>2</sub>+
+ K<sup>x</sup>K
B) µ-t = + <sub>1</sub>x<sub>1</sub>+ <sub>2</sub>x<sub>2</sub>+
+ K<sup>x</sup>K
C) logit(µ/t) =( + <sub>1</sub>x<sub>1</sub>+ <sub>2</sub>x<sub>2</sub>+
+ K<sup>x</sup>K)
D) ln(µ) = + <sub>1</sub>x<sub>1</sub>+ <sub>2</sub>x<sub>2</sub>+
+ K<sup>x</sup>K + ln(t)
Q3) Consider your own area of research. Provide an example where it would be appropriate to use Poisson regression. Defend your answer.
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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) Which of the following methods is used to estimate the b's (coefficient of parameters) in a proportional hazards regression?
A) least squares method
B) maximum likelihood estimation
C) partial likelihood estimation
D) probability plotting method
Q3) The major assumption of proportional-hazards regression is that the hazards are proportional. Explain what this means.
Q4) Can the log-rank test be used to compare survival curves at a specific time point? Why or why not?
Q5) Describe in what scenario you might observe this hazard function plot.
Q6) 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.
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