Biostatistics Practice Questions

Q: The specificity of a screening test for a disease is 95%. What does this imply?

The test correctly identifies 95% of the true negatives.. ***The test correctly identifies 95% of the true negatives.*** - **Specificity** is defined as the proportion of **true negatives** (individuals without the disease who test negative) correctly identified by the test. - A 95% specificity means that 95% of healthy individuals (without the disease) will correctly test negative. *The test correctly identifies 95% of the true positives.* - This statement describes **sensitivity**, not specificity. **Sensitivity** refers to the test's ability to correctly identify individuals who *do* have the disease (true positives). - High sensitivity is crucial for ruling out a disease when a test result is negative. *The test misses 95% of the true cases.* - This suggests a very low sensitivity (5%), which is not what specificity measures. - Missing 95% of true cases would indicate a high rate of **false negatives**. *95% of the people who test negative are disease-free.* - This statement refers to the **Negative Predictive Value (NPV)**, which is the probability that a person who tests negative truly does not have the disease. - NPV is influenced by both specificity and disease prevalence, and it's not synonymous with specificity itself. - NPV depends on the prevalence of disease in the population, whereas specificity is an intrinsic property of the test.

Q: A new screening test for breast cancer has a sensitivity of 90% and a specificity of 95%. In a population with moderate breast cancer prevalence, what does a positive test result most likely indicate?

Cannot be determined without knowing disease prevalence. ***Cannot be determined without knowing disease prevalence*** - While the test has **high sensitivity (90%)** and **high specificity (95%)**, the interpretation of a positive test result depends critically on the **Positive Predictive Value (PPV)**. - **PPV formula:** PPV = (Sensitivity × Prevalence) / [(Sensitivity × Prevalence) + (1 - Specificity) × (1 - Prevalence)] - The term **"moderate prevalence"** is too vague to calculate PPV accurately: - At **1% prevalence**: PPV ≈ 15% (positive test = low likelihood of disease) - At **10% prevalence**: PPV ≈ 67% (positive test = moderate likelihood) - At **50% prevalence**: PPV ≈ 95% (positive test = high likelihood) - Without knowing the **exact prevalence**, we cannot definitively interpret what a positive result means. *Indicates a high likelihood of breast cancer* - This would only be true if the prevalence were **high (>30-40%)**, but "moderate" is undefined. - **High specificity helps** reduce false positives, but PPV still varies significantly with prevalence in the moderate range. *Indicates a low likelihood of breast cancer* - This would be true at **very low prevalence (<5%)**, but we cannot assume this from "moderate." - The **high specificity (95%)** does reduce false positives compared to a less specific test. *Indicates a false negative result* - A **false negative** occurs when the test is **negative** in a person who actually has the disease. - The question asks about a **positive test result**, not a negative one.

Q: A study finds that a new diet reduces the risk of heart disease by 30%. What type of measure does this represent?

Relative risk reduction. ***Relative risk reduction*** - **Relative risk reduction (RRR)** is the percentage reduction in risk in the exposed group compared to the unexposed group. - A 30% reduction in risk specifically indicates RRR, calculated as: **(risk in unexposed - risk in exposed) / risk in unexposed × 100%**. *Absolute risk reduction* - **Absolute risk reduction (ARR)** is the difference in risk between the exposed and unexposed groups. - It is expressed as a **percentage point difference**, not a percentage *reduction* of the original risk. *Odds ratio* - The **odds ratio (OR)** quantifies the odds of an event occurring in one group compared to the odds of it occurring in another group. - It is typically used in **case-control studies** and does not directly express a reduction in risk. *Number needed to treat* - **Number needed to treat (NNT)** is the number of patients who need to be treated to prevent one additional adverse outcome. - It is calculated as the **reciprocal of the absolute risk reduction (1/ARR)**.

Q: In a clinical study, if the standard deviation of a measurement is 5 and the sample size is 25, what is the standard error of the mean?

1.0. **1.0** - The standard error of the mean (SEM) is calculated by dividing the **standard deviation (SD)** by the **square root of the sample size (n)**. - Given SD = 5 and n = 25, SEM = 5 / √25 = 5 / 5 = **1.0**. *2.0* - This value would result if the sample size was 6.25 (5 / √6.25 = 5 / 2.5 = 2.0), or if the formula was misapplied. - It does not correctly reflect the given **standard deviation** and **sample size**. *0.2* - This value would result if the standard deviation was 1 and the sample size was 25 (1 / √25 = 1 / 5 = 0.2), or if the standard deviation was divided by the sample size directly (5 / 25 = 0.2). - This calculation does not use the **square root of the sample size** in the denominator as required. *1.5* - This value does not correspond to the correct application of the **standard error of the mean formula** with the given parameters. - There is no direct calculation from SD=5 and n=25 that yields 1.5.

Q: In a population with a disease prevalence of 5%, a new screening test for a disease has a sensitivity of 92% and a specificity of 85%. What can be inferred about the positive predictive value (PPV) and negative predictive value (NPV) of this test?

PPV is low, NPV is high.. ***PPV is low, NPV is high.*** - In populations with **low disease prevalence** (5% in this case), the **positive predictive value (PPV)** tends to be low, even with good test sensitivity and specificity, because many positive test results will be false positives. - Conversely, with a low prevalence and good specificity (85%), the **negative predictive value (NPV)** tends to be high, meaning a negative test result is very likely to be truly negative. *Both PPV and NPV are low.* - The **NPV** is expected to be high, not low, given the good specificity and low disease prevalence. - A low NPV would indicate that negative test results are often incorrect, which is unlikely under these conditions. *PPV is high, NPV is low.* - The **PPV** is expected to be low, not high, due to the low disease prevalence leading to a higher proportion of false positives among positive tests. - The **NPV** is expected to be high, not low, due to the low prevalence and good specificity, making negative results reliable. *Both PPV and NPV are high.* - The **PPV** is unlikely to be high given the very low disease prevalence, as a significant number of positive tests would be false positives. - While NPV is high, the low PPV prevents both from being high simultaneously in this scenario.

Q: In a case-control study on hypertension and stroke, which measure is used to compare the risk between cases and controls?

Odds ratio. ***Odds ratio*** - In a **case-control study**, we start by identifying individuals with the outcome (cases) and those without (controls) and then look back to assess their exposure status. - The **odds ratio (OR)** quantifies the odds of exposure among cases relative to the odds of exposure among controls, thereby estimating the strength of association between exposure and outcome. *Relative risk* - **Relative risk (RR)** is used in **cohort studies** and **randomized controlled trials** to compare the risk of an outcome in an exposed group versus an unexposed group. - It directly measures how many times more likely an exposed group is to develop an outcome compared to an unexposed group. *Absolute risk reduction* - **Absolute risk reduction (ARR)** is a measure of the difference in risk between an intervention group and a control group, typically used in **clinical trials**. - It indicates the **absolute difference in the proportion of people** who experience an event across two groups, reflecting the direct benefit of an intervention. *Incidence rate* - The **incidence rate** measures the number of new cases of a disease or outcome that develop in a population at risk over a specified period. - It is used to describe the **frequency of disease occurrence** in a population, not to compare risk between cases and controls.

Q: A health researcher wants to compare the effectiveness of two different antihypertensive drugs. Which statistical test should be used to determine if there is a significant difference in blood pressure reduction between the two groups?

t-test. ***t-test*** - The **t-test** is appropriate for comparing the means of **two independent groups** to determine if a significant difference exists. In this scenario, we are comparing the mean blood pressure reduction between two drug groups. - Specifically, an **independent samples t-test** would be used as the two drug groups are distinct and unrelated. *Chi-square test* - The **chi-square test** is used to compare **categorical data** or frequencies, not continuous variables like blood pressure reduction. - It assesses whether there is a significant association between two categorical variables. *ANOVA* - **ANOVA (Analysis of Variance)** is used to compare the means of **three or more independent groups**. - While it *could* be used for two groups (and would yield the same p-value as a t-test), the **t-test** is the more direct and appropriate choice for a two-group comparison. *Correlation analysis* - **Correlation analysis** measures the strength and direction of a **linear relationship between two continuous variables**. - It does not test for significant differences between group means but rather how two variables move together.

Q: A study evaluates the impact of a new diabetes management program on patient outcomes. Which method is the best to analyze the effectiveness of the program?

Randomized controlled trial. ***Randomized controlled trial*** - A **Randomized Controlled Trial (RCT)** is the **gold standard** for evaluating interventions because it minimizes bias by randomly assigning participants to either an intervention group or a control group. - This design allows for a direct comparison of outcomes, providing the strongest evidence of a program's **effectiveness** by controlling for confounding variables and ensuring that observed effects are likely due to the intervention. *Pre- and post-intervention comparison* - This method compares outcomes before and after an intervention in the same group of participants, but it lacks a **control group**. - Without a control group, it's difficult to attribute observed changes solely to the intervention, as other **confounding factors** or natural progression of the disease could also influence the results. *Cross-sectional survey* - A **cross-sectional survey** collects data at a single point in time, providing a snapshot of the prevalence of certain characteristics or outcomes. - This method is useful for describing populations but **cannot establish causality** or measure changes over time in response to an intervention. *Cohort study* - A **cohort study** follows a group of individuals over time to observe the development of outcomes, often comparing those exposed to a risk factor with those unexposed. - While useful for studying **risk factors** and disease incidence, it typically does not involve a controlled intervention and is more prone to **confounding biases** than an RCT when assessing program effectiveness.

Question 1

The specificity of a screening test for a disease is 95%. What does this imply?

Accepted Answer

The test correctly identifies 95% of the true negatives.

Answer

The test correctly identifies 95% of the true positives.

Answer

The test misses 95% of the true cases.

Answer

95% of the people who test negative are disease-free.

Question 2

A new screening test for breast cancer has a sensitivity of 90% and a specificity of 95%. In a population with moderate breast cancer prevalence, what does a positive test result most likely indicate?

Accepted Answer

Cannot be determined without knowing disease prevalence

Answer

Indicates a low likelihood of breast cancer

Answer

Indicates a false negative result

Answer

Indicates a high likelihood of breast cancer

Question 3

A study finds that a new diet reduces the risk of heart disease by 30%. What type of measure does this represent?

Accepted Answer

Relative risk reduction

Answer

Absolute risk reduction

Answer

Odds ratio

Answer

Number needed to treat

Question 4

In a clinical study, if the standard deviation of a measurement is 5 and the sample size is 25, what is the standard error of the mean?

Accepted Answer

1.0

Answer

2.0

Answer

0.2

Answer

1.5

Question 5

In a population with a disease prevalence of 5%, a new screening test for a disease has a sensitivity of 92% and a specificity of 85%. What can be inferred about the positive predictive value (PPV) and negative predictive value (NPV) of this test?

Accepted Answer

PPV is low, NPV is high.

Answer

Both PPV and NPV are low.

Answer

PPV is high, NPV is low.

Answer

Both PPV and NPV are high.

Question 6

In a case-control study on hypertension and stroke, which measure is used to compare the risk between cases and controls?

Accepted Answer

Odds ratio

Answer

Relative risk

Answer

Absolute risk reduction

Answer

Incidence rate

Question 7

In a randomized controlled trial, the relative risk reduction (RRR) for a new treatment compared to the standard treatment is calculated to be 25%. How should this result be interpreted?

Accepted Answer

The new treatment reduces the risk of the outcome by 25% compared to the standard treatment.

Answer

25% of patients in the treatment group did not experience the outcome.

Answer

The risk of the outcome is 25% of what it was in the standard treatment group.

Answer

The new treatment is 25% less effective than the standard treatment.

Question 8

A public health researcher is comparing the effectiveness of two vaccines using a cohort study. After calculating the relative risk, the researcher finds an RR of 0.5 for Vaccine A compared to Vaccine B. What does this result indicate about Vaccine A?

Accepted Answer

Vaccine A reduces the risk of disease by 50% compared to Vaccine B.

Answer

Vaccine A increases the risk of disease by 50% compared to Vaccine B.

Answer

Vaccine A has no effect on disease risk compared to Vaccine B.

Answer

Vaccine A and Vaccine B are equally effective.

Question 9

A health researcher wants to compare the effectiveness of two different antihypertensive drugs. Which statistical test should be used to determine if there is a significant difference in blood pressure reduction between the two groups?

Accepted Answer

t-test

Answer

Chi-square test

Answer

ANOVA

Answer

Correlation analysis

Question 10

A study evaluates the impact of a new diabetes management program on patient outcomes. Which method is the best to analyze the effectiveness of the program?

Accepted Answer

Randomized controlled trial

Answer

Pre- and post-intervention comparison

Answer

Cross-sectional survey

Answer

Cohort study

Biostatistics — MCQs

Biostatistics — MCQs

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