Biostatistics Practice Questions

Q: Bias in clinical trials can be minimized by all of the following methods, EXCEPT:

Multivariate analysis. ***Multivariate analysis*** - **Multivariate analysis** is a statistical tool used to analyze data with multiple variables, but it cannot prevent bias during the design or execution phases of a clinical trial. - While it can help control for confounding variables during data analysis, it is applied *after* data collection and does not eliminate sources of bias related to participant selection, intervention assignment, or outcome assessment. *Matching* - **Matching** involves selecting control group participants who share similar characteristics (e.g., age, sex, comorbidities) with the intervention group, thereby reducing confounding by known variables. - This method helps to ensure that groups are comparable at baseline, minimizing **selection bias** and making observed differences more attributable to the intervention. *Blinding* - **Blinding** involves concealing the treatment assignment from participants, researchers, or both (single- or double-blinding) to prevent their expectations or preconceptions from influencing outcomes or assessments. - This method effectively minimizes various forms of **performance bias** (e.g., in participant behavior or co-interventions) and **detection bias** (e.g., in outcome assessment). *Randomization* - **Randomization** is the process of assigning participants to intervention or control groups purely by chance, which helps ensure that known and unknown confounding factors are evenly distributed between groups. - This method is crucial for minimizing **selection bias**, making the groups comparable and increasing the likelihood that any observed differences are due to the intervention rather than pre-existing differences.

Q: In a cohort study, which measure is most appropriate for comparing the risk of developing a disease between exposed and non-exposed groups?

Relative risk. ***Relative risk*** - The **relative risk (RR)** directly compares the **incidence of disease** in the exposed group to the incidence in the non-exposed group in a **cohort study**. - It quantifies how many times more likely (or less likely) the exposed group is to develop the disease compared to the unexposed group. *Odds ratio* - The **odds ratio (OR)** is primarily used in **case-control studies** to estimate the association between exposure and outcome. - While it can approximate the relative risk when the disease is rare, it is not the most direct measure of risk comparison in a cohort study. *Absolute risk* - **Absolute risk** refers to the **incidence of the disease** in a specific group and does not inherently involve a comparison between exposed and non-exposed groups. - It represents the probability of developing the disease over a specified period. *Incidence rate* - **Incidence rate** measures the **frequency of new cases** of a disease in a population over a given period. - While essential for calculating relative risk, incidence rate itself is a measure of occurrence, not a direct comparative measure between two groups.

Q: In an international health study, researchers want to assess the impact of socioeconomic status on health outcomes. Which statistical method is best suited for this analysis?

Multivariate regression. ***Multivariate regression*** - This method is ideal for examining the relationship between multiple independent variables (like **socioeconomic status** components such as income, education, occupation) and one or more dependent variables (various **health outcomes**). - It allows researchers to control for confounding factors and determine the independent contribution of each socioeconomic variable to health outcomes. *Chi-square test* - The Chi-square test is typically used to assess the association between **two categorical variables**, such as different socioeconomic categories and the presence or absence of a specific health condition. - It is not suitable for analyzing the impact of multiple continuous or ordinal socioeconomic factors on complex health outcomes. *ANOVA* - **ANOVA (Analysis of Variance)** is used to compare the means of a continuous dependent variable across **two or more groups** defined by a categorical independent variable. - While it could compare health outcomes between different socioeconomic *groups*, it is less efficient for modeling the continuous or complex relationships among multiple socioeconomic factors and health outcomes. *Survival analysis* - **Survival analysis** focuses on the time until an event occurs (e.g., time until disease onset, death, or recovery) and is often used in longitudinal studies. - While socioeconomic status can be a predictor in survival models, this method is not the primary choice for broadly assessing the overall impact of socioeconomic status on health outcomes in a cross-sectional or general population study.

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: 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: 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.

Question 1

Bias in clinical trials can be minimized by all of the following methods, EXCEPT:

Accepted Answer

Multivariate analysis

Answer

Matching

Answer

Randomization

Answer

Blinding

Question 2

A study comparing two antihypertensive drugs reports a p-value of 0.03 for the primary outcome. What is the correct interpretation of this result?

Accepted Answer

There is a 3% chance that the observed difference occurred by chance if the null hypothesis is true.

Answer

The probability of making a Type I error is 3%.

Answer

The study has a 97% chance of detecting a true difference if it exists.

Answer

There is a 3% chance that the null hypothesis is false.

Question 3

In a cohort study, which measure is most appropriate for comparing the risk of developing a disease between exposed and non-exposed groups?

Accepted Answer

Relative risk

Answer

Odds ratio

Answer

Absolute risk

Answer

Incidence rate

Question 4

A study reports a confidence interval (CI) for a relative risk (RR) ranging from 0.8 to 1.2. How should this result be interpreted in relation to the association between exposure and outcome?

Accepted Answer

No significant association exists between the exposure and outcome.

Answer

The exposure reduces the risk of the outcome.

Answer

The study may have low statistical power.

Answer

The exposure increases the risk of the outcome.

Question 5

In an international health study, researchers want to assess the impact of socioeconomic status on health outcomes. Which statistical method is best suited for this analysis?

Accepted Answer

Multivariate regression

Answer

Chi-square test

Answer

ANOVA

Answer

Survival analysis

Question 6

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 7

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 8

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 9

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 10

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.

Biostatistics — MCQs

Biostatistics — MCQs

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