WGU C784 APPLIED HEALTHCARE STATISTICS FINAL
EXAM LATEST UPDATE (GRADED) QUESTIONS AND
DETAILED ANSWERS - 200 Questions and Answers Already
Graded A+ Premium Exam Tested And Verified
Subject Area Applied Healthcare Statistics
Description This comprehensive exam assesses advanced statistical reasoning in healthcare
contexts, covering probability, hypothesis testing, regression, survival analysis,
and evidence-based interpretation. It requires synthesis of statistical theory with
clinical application.
Expected Grade A+
Total Questions 200
Duration 3 hours
Learning Outcomes 1. Apply probability distributions to model clinical events
2. Interpret confidence intervals and p-values in research studies
3. Perform and critique regression models for health outcomes
4. Analyze survival data using Kaplan-Meier and Cox regression
5. Evaluate diagnostic test performance using ROC curves
Accreditation This exam meets the rigorous standards of top-tier US universities (Ivy
League/R1) for applied biostatistics courses.
Page 1
,1. A randomized trial compares a new drug to placebo for reducing hospital
readmission rates. The trial reports a relative risk (RR) of 0.72 with a 95%
confidence interval (0.53, 0.98). Which of the following is the most accurate
interpretation of this result?
A. The drug reduces readmission risk by 28% with 95% certainty.
B. There is a 95% probability that the true RR lies between 0.53 and 0.98.
C. The p-value for the null hypothesis RR=1 is less than 0.05.
D. The absolute risk reduction is 28%.
Answer: C. The p-value for the null hypothesis RR=1 is less than 0.05.
The 95% CI excludes 1.0, which corresponds to a p-value < 0.05 for testing RR=1.
Option A is incorrect because the CI does not provide certainty about the point
estimate. Option B misinterprets the CI as a probability statement about the parameter.
Option D confuses relative and absolute risk reduction.
2. In a cohort study of 500 patients, researchers model time to hospital readmission
using Cox proportional hazards. The hazard ratio for a binary treatment is 1.45
(95% CI: 1.12-1.88). Which assumption must be verified for this model to be valid?
A. The baseline hazard function is constant over time.
B. The log hazard for treatment is linear in follow-up time.
C. The hazard ratio for treatment is constant over time.
D. The survival times follow a Weibull distribution.
Answer: C. The hazard ratio for treatment is constant over time.
Cox proportional hazards assumes proportional hazards: the HR is constant over time.
Option A describes an exponential model, not Cox. Option B is not required; Cox
models are semi-parametric. Option D refers to parametric survival models.
Page 2
,3. A diagnostic test for a disease has sensitivity 0.85 and specificity 0.90. The
prevalence of the disease in a certain population is 0.20. What is the positive
predictive value (PPV) of the test in this population?
A. 0.68
B. 0.72
C. 0.85
D. 0.90
Answer: A. 0.68
PPV = (sensitivity * prevalence) / (sensitivity * prevalence + (1 - specificity)*(1 -
prevalence)) = (0.85*0.20)/(0.85*0.20 + 0.10*0.80) = 0..25 = 0.68. Options B, C, D
are incorrect due to miscalculation or confusion with sensitivity/specificity.
4. A study reports that a new biomarker has an area under the ROC curve (AUC) of
0.72. Which of the following is the most appropriate conclusion?
A. The biomarker correctly classifies 72% of patients.
B. The biomarker has no discriminatory ability.
C. For any randomly selected pair of a diseased and a non-diseased individual, the
probability that the biomarker value is higher in the diseased individual is 0.72.
D. The optimal cutoff yields sensitivity equal to specificity.
Answer: C. For any randomly selected pair of a diseased and a non-diseased
individual, the probability that the biomarker value is higher in the diseased
individual is 0.72.
AUC represents the probability that a randomly chosen diseased subject has a higher
test value than a randomly chosen non-diseased subject. Option A is incorrect; AUC is
not classification accuracy. Option B is false; AUC > 0.5 indicates some discrimination.
Option D is not necessarily true; optimal cutoff depends on costs.
Page 3
, 5. In a multiple linear regression predicting hospital length of stay (LOS) from age
(years), comorbidity count, and insurance type (categorical with 3 levels), the
coefficient for age is 0.12 (p=0.03). Which of the following is the correct
interpretation, assuming all other variables held constant?
A. Each additional year of age increases LOS by 0.12 days, but this is not statistically
significant.
B. LOS increases by 0.12 days for each year increase in age, controlling for other factors,
and the effect is statistically significant at =0.05.
C. The correlation between age and LOS is 0.12.
D. Age explains 12% of the variance in LOS.
Answer: B. LOS increases by 0.12 days for each year increase in age, controlling
for other factors, and the effect is statistically significant at =0.05.
The coefficient (0.12) is the expected change in LOS per year increase in age, holding
other predictors constant. The p-value < 0.05 indicates statistical significance. Option A
incorrectly states non-significance. Option C confuses regression coefficient with
correlation. Option D confuses coefficient with R-squared contribution.
6. A researcher wants to compare the median survival time between two treatment
groups using a non-parametric test. Which test is most appropriate?
A. Two-sample t-test
B. Wilcoxon rank-sum test
C. Chi-square test
D. Pearson correlation
Answer: B. Wilcoxon rank-sum test
The Wilcoxon rank-sum test (Mann-Whitney U) is a non-parametric test for comparing
two independent groups, appropriate when normality assumptions are violated or for
survival data. Option A assumes normality. Option C is for categorical data. Option D
assesses association.
Page 4
EXAM LATEST UPDATE (GRADED) QUESTIONS AND
DETAILED ANSWERS - 200 Questions and Answers Already
Graded A+ Premium Exam Tested And Verified
Subject Area Applied Healthcare Statistics
Description This comprehensive exam assesses advanced statistical reasoning in healthcare
contexts, covering probability, hypothesis testing, regression, survival analysis,
and evidence-based interpretation. It requires synthesis of statistical theory with
clinical application.
Expected Grade A+
Total Questions 200
Duration 3 hours
Learning Outcomes 1. Apply probability distributions to model clinical events
2. Interpret confidence intervals and p-values in research studies
3. Perform and critique regression models for health outcomes
4. Analyze survival data using Kaplan-Meier and Cox regression
5. Evaluate diagnostic test performance using ROC curves
Accreditation This exam meets the rigorous standards of top-tier US universities (Ivy
League/R1) for applied biostatistics courses.
Page 1
,1. A randomized trial compares a new drug to placebo for reducing hospital
readmission rates. The trial reports a relative risk (RR) of 0.72 with a 95%
confidence interval (0.53, 0.98). Which of the following is the most accurate
interpretation of this result?
A. The drug reduces readmission risk by 28% with 95% certainty.
B. There is a 95% probability that the true RR lies between 0.53 and 0.98.
C. The p-value for the null hypothesis RR=1 is less than 0.05.
D. The absolute risk reduction is 28%.
Answer: C. The p-value for the null hypothesis RR=1 is less than 0.05.
The 95% CI excludes 1.0, which corresponds to a p-value < 0.05 for testing RR=1.
Option A is incorrect because the CI does not provide certainty about the point
estimate. Option B misinterprets the CI as a probability statement about the parameter.
Option D confuses relative and absolute risk reduction.
2. In a cohort study of 500 patients, researchers model time to hospital readmission
using Cox proportional hazards. The hazard ratio for a binary treatment is 1.45
(95% CI: 1.12-1.88). Which assumption must be verified for this model to be valid?
A. The baseline hazard function is constant over time.
B. The log hazard for treatment is linear in follow-up time.
C. The hazard ratio for treatment is constant over time.
D. The survival times follow a Weibull distribution.
Answer: C. The hazard ratio for treatment is constant over time.
Cox proportional hazards assumes proportional hazards: the HR is constant over time.
Option A describes an exponential model, not Cox. Option B is not required; Cox
models are semi-parametric. Option D refers to parametric survival models.
Page 2
,3. A diagnostic test for a disease has sensitivity 0.85 and specificity 0.90. The
prevalence of the disease in a certain population is 0.20. What is the positive
predictive value (PPV) of the test in this population?
A. 0.68
B. 0.72
C. 0.85
D. 0.90
Answer: A. 0.68
PPV = (sensitivity * prevalence) / (sensitivity * prevalence + (1 - specificity)*(1 -
prevalence)) = (0.85*0.20)/(0.85*0.20 + 0.10*0.80) = 0..25 = 0.68. Options B, C, D
are incorrect due to miscalculation or confusion with sensitivity/specificity.
4. A study reports that a new biomarker has an area under the ROC curve (AUC) of
0.72. Which of the following is the most appropriate conclusion?
A. The biomarker correctly classifies 72% of patients.
B. The biomarker has no discriminatory ability.
C. For any randomly selected pair of a diseased and a non-diseased individual, the
probability that the biomarker value is higher in the diseased individual is 0.72.
D. The optimal cutoff yields sensitivity equal to specificity.
Answer: C. For any randomly selected pair of a diseased and a non-diseased
individual, the probability that the biomarker value is higher in the diseased
individual is 0.72.
AUC represents the probability that a randomly chosen diseased subject has a higher
test value than a randomly chosen non-diseased subject. Option A is incorrect; AUC is
not classification accuracy. Option B is false; AUC > 0.5 indicates some discrimination.
Option D is not necessarily true; optimal cutoff depends on costs.
Page 3
, 5. In a multiple linear regression predicting hospital length of stay (LOS) from age
(years), comorbidity count, and insurance type (categorical with 3 levels), the
coefficient for age is 0.12 (p=0.03). Which of the following is the correct
interpretation, assuming all other variables held constant?
A. Each additional year of age increases LOS by 0.12 days, but this is not statistically
significant.
B. LOS increases by 0.12 days for each year increase in age, controlling for other factors,
and the effect is statistically significant at =0.05.
C. The correlation between age and LOS is 0.12.
D. Age explains 12% of the variance in LOS.
Answer: B. LOS increases by 0.12 days for each year increase in age, controlling
for other factors, and the effect is statistically significant at =0.05.
The coefficient (0.12) is the expected change in LOS per year increase in age, holding
other predictors constant. The p-value < 0.05 indicates statistical significance. Option A
incorrectly states non-significance. Option C confuses regression coefficient with
correlation. Option D confuses coefficient with R-squared contribution.
6. A researcher wants to compare the median survival time between two treatment
groups using a non-parametric test. Which test is most appropriate?
A. Two-sample t-test
B. Wilcoxon rank-sum test
C. Chi-square test
D. Pearson correlation
Answer: B. Wilcoxon rank-sum test
The Wilcoxon rank-sum test (Mann-Whitney U) is a non-parametric test for comparing
two independent groups, appropriate when normality assumptions are violated or for
survival data. Option A assumes normality. Option C is for categorical data. Option D
assesses association.
Page 4
