WGU C784- APPLIED HEALTHCARE STATISTICS
PRE-ASSESSMENT EXAM QUESTIONS AND CORRECT
ANSWERS - 89 Questions and Answers Already Graded A+
Premium Exam Tested And Verified
Subject Area Applied Healthcare Statistics
Description This pre-assessment exam covers key topics in applied healthcare statistics,
including probability distributions, hypothesis testing, regression analysis,
survival analysis, and study design. It tests the ability to apply statistical methods
to real-world healthcare data and interpret results critically.
Expected Grade A+
Total Questions 89
Duration 3 hours
Learning Outcomes 1. Apply descriptive and inferential statistics to healthcare data
2. Interpret regression models and survival curves
3. Evaluate study designs and statistical tests for clinical research
Accreditation Designed to meet the standards of top US university curricula in healthcare
statistics (e.g., Harvard, Stanford, Johns Hopkins).
Page 1
,1. In a cohort study investigating the association between a new biomarker and
disease progression, the hazard ratio (HR) from a Cox proportional hazards model
is 1.45 (95% CI: 0.98, 2.15). Which of the following is the most appropriate
interpretation?
A. The biomarker is significantly associated with increased hazard of progression because
HR > 1.
B. The biomarker is not significantly associated with progression because the confidence
interval includes 1, but there is a trend toward increased hazard.
C. The biomarker is protective because the confidence interval is wide.
D. The hazard ratio is not interpretable because the proportional hazards assumption may be
violated.
Answer: B. The biomarker is not significantly associated with progression because
the confidence interval includes 1, but there is a trend toward increased hazard.
A hazard ratio of 1.45 suggests a 45% increase in hazard, but the 95% CI includes 1
(0.98, 2.15), indicating the result is not statistically significant at the 0.05 level. However,
the CI's lower bound close to 1 suggests a possible trend, but not definitive evidence.
Option A is incorrect because significance requires the CI to exclude 1. Option C is
incorrect as HR > 1 indicates increased hazard, not protection. Option D is speculative
without evidence of assumption violation.
Page 2
,2. A logistic regression model predicts the probability of hospital readmission within
30 days. The model includes age (continuous), comorbidity score (0-10), and prior
admissions (count). The coefficient for comorbidity score is 0.35 (p<0.001). Which of
the following best interprets this coefficient?
A. For each one-unit increase in comorbidity score, the odds of readmission increase by
35%.
B. For each one-unit increase in comorbidity score, the log-odds of readmission increase by
0.35, holding other variables constant.
C. For each one-unit increase in comorbidity score, the probability of readmission increases
by 0.35.
D. The odds ratio for comorbidity score is e^0.35, but this cannot be interpreted without the
intercept.
Answer: B. For each one-unit increase in comorbidity score, the log-odds of
readmission increase by 0.35, holding other variables constant.
In logistic regression, coefficients represent change in log-odds per unit increase in the
predictor, holding others constant. Option A incorrectly states odds increase by 35%
(the odds ratio is e^0.35 1.42, a 42% increase). Option C confuses log-odds with
probability. Option D is partially correct that odds ratio is e^0.35, but the coefficient
itself is interpretable as log-odds change, not requiring the intercept.
3. In a randomized controlled trial comparing two treatments for chronic pain, the
primary outcome is the proportion of patients reporting 50% pain reduction at 12
weeks. The results show a risk difference of 0.12 (95% CI: -0.01, 0.25) in favor of the
new treatment. Which of the following is the most appropriate conclusion?
A. The new treatment is significantly better because the risk difference is positive.
B. The new treatment is not significantly better because the confidence interval includes
zero.
C. The new treatment is worse because the confidence interval is wide.
D. The risk difference is not clinically meaningful because it is less than 0.20.
Answer: B. The new treatment is not significantly better because the confidence
interval includes zero.
The 95% CI for the risk difference includes zero (-0.01 to 0.25), indicating no
statistically significant difference at the 0.05 level. Option A is incorrect because
significance requires the CI to exclude zero. Option C is incorrect; a wide CI does not
imply the treatment is worse. Option D is a clinical judgment, not a statistical
conclusion, and a threshold of 0.20 is arbitrary.
Page 3
, 4. A Kaplan-Meier survival curve is used to compare survival between two groups in
a clinical trial. The log-rank test yields a p-value of 0.04. Which of the following is a
correct interpretation?
A. The survival curves are significantly different at all time points.
B. There is a statistically significant difference in the overall survival distributions between
the two groups.
C. The hazard ratio is significantly different from 1 at the 0.05 level.
D. The median survival time is significantly different between the groups.
Answer: B. There is a statistically significant difference in the overall survival
distributions between the two groups.
The log-rank test compares the entire survival distributions, not at specific time points.
A p-value < 0.05 indicates a significant difference in survival experience overall. Option
A is incorrect because the test does not assess differences at all time points individually.
Option C is related but the log-rank test does not directly estimate hazard ratio; it tests
equality of survival curves. Option D is incorrect because the test does not specifically
compare medians.
5. In a multiple linear regression model predicting hospital length of stay (days), the
coefficient for age (years) is 0.05 (p=0.03) and for sex (male=1, female=0) is -0.2
(p=0.10). Which of the following statements is correct?
A. Age is a statistically significant predictor, and for each additional year, length of stay
increases by 0.05 days, holding sex constant.
B. Sex is a statistically significant predictor because the coefficient is negative.
C. The model explains 5% of the variance in length of stay.
D. Age is not a clinically significant predictor because the effect size is small.
Answer: A. Age is a statistically significant predictor, and for each additional year,
length of stay increases by 0.05 days, holding sex constant.
The p-value for age is 0.03, indicating statistical significance at =0.05. The coefficient
0.05 means a one-year increase in age is associated with a 0.05-day increase in length of
stay, holding sex constant. Option B is incorrect because p=0.10 is not significant.
Option C is about R-squared, not given. Option D confuses statistical and clinical
significance; the small effect may still be clinically relevant depending on context.
Page 4
PRE-ASSESSMENT EXAM QUESTIONS AND CORRECT
ANSWERS - 89 Questions and Answers Already Graded A+
Premium Exam Tested And Verified
Subject Area Applied Healthcare Statistics
Description This pre-assessment exam covers key topics in applied healthcare statistics,
including probability distributions, hypothesis testing, regression analysis,
survival analysis, and study design. It tests the ability to apply statistical methods
to real-world healthcare data and interpret results critically.
Expected Grade A+
Total Questions 89
Duration 3 hours
Learning Outcomes 1. Apply descriptive and inferential statistics to healthcare data
2. Interpret regression models and survival curves
3. Evaluate study designs and statistical tests for clinical research
Accreditation Designed to meet the standards of top US university curricula in healthcare
statistics (e.g., Harvard, Stanford, Johns Hopkins).
Page 1
,1. In a cohort study investigating the association between a new biomarker and
disease progression, the hazard ratio (HR) from a Cox proportional hazards model
is 1.45 (95% CI: 0.98, 2.15). Which of the following is the most appropriate
interpretation?
A. The biomarker is significantly associated with increased hazard of progression because
HR > 1.
B. The biomarker is not significantly associated with progression because the confidence
interval includes 1, but there is a trend toward increased hazard.
C. The biomarker is protective because the confidence interval is wide.
D. The hazard ratio is not interpretable because the proportional hazards assumption may be
violated.
Answer: B. The biomarker is not significantly associated with progression because
the confidence interval includes 1, but there is a trend toward increased hazard.
A hazard ratio of 1.45 suggests a 45% increase in hazard, but the 95% CI includes 1
(0.98, 2.15), indicating the result is not statistically significant at the 0.05 level. However,
the CI's lower bound close to 1 suggests a possible trend, but not definitive evidence.
Option A is incorrect because significance requires the CI to exclude 1. Option C is
incorrect as HR > 1 indicates increased hazard, not protection. Option D is speculative
without evidence of assumption violation.
Page 2
,2. A logistic regression model predicts the probability of hospital readmission within
30 days. The model includes age (continuous), comorbidity score (0-10), and prior
admissions (count). The coefficient for comorbidity score is 0.35 (p<0.001). Which of
the following best interprets this coefficient?
A. For each one-unit increase in comorbidity score, the odds of readmission increase by
35%.
B. For each one-unit increase in comorbidity score, the log-odds of readmission increase by
0.35, holding other variables constant.
C. For each one-unit increase in comorbidity score, the probability of readmission increases
by 0.35.
D. The odds ratio for comorbidity score is e^0.35, but this cannot be interpreted without the
intercept.
Answer: B. For each one-unit increase in comorbidity score, the log-odds of
readmission increase by 0.35, holding other variables constant.
In logistic regression, coefficients represent change in log-odds per unit increase in the
predictor, holding others constant. Option A incorrectly states odds increase by 35%
(the odds ratio is e^0.35 1.42, a 42% increase). Option C confuses log-odds with
probability. Option D is partially correct that odds ratio is e^0.35, but the coefficient
itself is interpretable as log-odds change, not requiring the intercept.
3. In a randomized controlled trial comparing two treatments for chronic pain, the
primary outcome is the proportion of patients reporting 50% pain reduction at 12
weeks. The results show a risk difference of 0.12 (95% CI: -0.01, 0.25) in favor of the
new treatment. Which of the following is the most appropriate conclusion?
A. The new treatment is significantly better because the risk difference is positive.
B. The new treatment is not significantly better because the confidence interval includes
zero.
C. The new treatment is worse because the confidence interval is wide.
D. The risk difference is not clinically meaningful because it is less than 0.20.
Answer: B. The new treatment is not significantly better because the confidence
interval includes zero.
The 95% CI for the risk difference includes zero (-0.01 to 0.25), indicating no
statistically significant difference at the 0.05 level. Option A is incorrect because
significance requires the CI to exclude zero. Option C is incorrect; a wide CI does not
imply the treatment is worse. Option D is a clinical judgment, not a statistical
conclusion, and a threshold of 0.20 is arbitrary.
Page 3
, 4. A Kaplan-Meier survival curve is used to compare survival between two groups in
a clinical trial. The log-rank test yields a p-value of 0.04. Which of the following is a
correct interpretation?
A. The survival curves are significantly different at all time points.
B. There is a statistically significant difference in the overall survival distributions between
the two groups.
C. The hazard ratio is significantly different from 1 at the 0.05 level.
D. The median survival time is significantly different between the groups.
Answer: B. There is a statistically significant difference in the overall survival
distributions between the two groups.
The log-rank test compares the entire survival distributions, not at specific time points.
A p-value < 0.05 indicates a significant difference in survival experience overall. Option
A is incorrect because the test does not assess differences at all time points individually.
Option C is related but the log-rank test does not directly estimate hazard ratio; it tests
equality of survival curves. Option D is incorrect because the test does not specifically
compare medians.
5. In a multiple linear regression model predicting hospital length of stay (days), the
coefficient for age (years) is 0.05 (p=0.03) and for sex (male=1, female=0) is -0.2
(p=0.10). Which of the following statements is correct?
A. Age is a statistically significant predictor, and for each additional year, length of stay
increases by 0.05 days, holding sex constant.
B. Sex is a statistically significant predictor because the coefficient is negative.
C. The model explains 5% of the variance in length of stay.
D. Age is not a clinically significant predictor because the effect size is small.
Answer: A. Age is a statistically significant predictor, and for each additional year,
length of stay increases by 0.05 days, holding sex constant.
The p-value for age is 0.03, indicating statistical significance at =0.05. The coefficient
0.05 means a one-year increase in age is associated with a 0.05-day increase in length of
stay, holding sex constant. Option B is incorrect because p=0.10 is not significant.
Option C is about R-squared, not given. Option D confuses statistical and clinical
significance; the small effect may still be clinically relevant depending on context.
Page 4
