Biostatistics — MCQs
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The ability of a diagnostic test to correctly identify individuals who have the disease (detect disease when present) is called-
In a screening test for diabetes mellitus (DM) conducted on a population of 1000, the screening test identified 90 individuals as positive. When the gold standard test was performed on the entire population, it confirmed that 100 individuals actually had diabetes. Calculate the sensitivity of the screening test.
The Chi-square test is most commonly used in medical statistics to test for:
What is the denominator used in calculating the under-5 proportional mortality rate?
In a positively skewed distribution, which of the following is true?
Which of the following is a parametric test?
In a study a patient does not know the nature of drug [whether a placebo or curative drug] he is taking. The researcher knows the drug type to be given to the individuals in study. Type of blinding in this study is?
Which of the following is NOT a characteristic of a health index?
Why are standardized death rates used in health comparisons?
Which of the following statements about standardization is false?
Biostatistics Indian Medical PG Practice Questions and MCQs
Question 821: The ability of a diagnostic test to correctly identify individuals who have the disease (detect disease when present) is called-
- A. Negative predictive value
- B. Sensitivity (True Positive Rate) (Correct Answer)
- C. Specificity (True Negative Rate)
- D. Positive predictive value (PPV)
Explanation: ***Positive predictive value*** - It refers to the probability that individuals with a positive test result actually have the disease, indicating the test's ability to correctly diagnose the condition [1]. - A high **positive predictive value** means the test is effective in diagnosing the disease among those who test positive. *Sensitivity* - Sensitivity measures the proportion of actual positives correctly identified by the test, but does not directly indicate diagnostic power. - A test can be sensitive but still have a low positive predictive value if the prevalence of the disease is low. *Negative predictive value* - Negative predictive value indicates the probability that individuals with a negative test result truly do not have the disease, thus not reflecting the test's diagnostic power [1]. - It is more concerned with the test's ability to rule out a disease rather than to confirm it. *Specificity* - Specificity measures the proportion of actual negatives that are correctly identified, which is not concerned with the correct diagnosis of the disease itself [1]. - A test can have high specificity but not be useful for diagnosing if it lacks positive predictive value. **References:** [1] Cross SS. Underwood's Pathology: A Clinical Approach. 6th ed. (Basic Pathology) introduces the student to key general principles of pathology, both as a medical science and as a clinical activity with a vital role in patient care. Part 2 (Disease Mechanisms) provides fundamental knowledge about the cellular and molecular processes involved in diseases, providing the rationale for their treatment. Part 3 (Systematic Pathology) deals in detail with specific diseases, with emphasis on the clinically important aspects., pp. 253-254.
Question 822: In a screening test for diabetes mellitus (DM) conducted on a population of 1000, the screening test identified 90 individuals as positive. When the gold standard test was performed on the entire population, it confirmed that 100 individuals actually had diabetes. Calculate the sensitivity of the screening test.
- A. 90/100 (Correct Answer)
- B. 100/110
- C. 80/100
- D. 100/100
Explanation: ***90/100*** - **Sensitivity** measures the proportion of **true positives** correctly identified by the screening test among all individuals who actually have the disease. - **Formula:** Sensitivity = True Positives / (True Positives + False Negatives) - In this scenario, the gold standard confirmed **100 individuals** as truly positive for diabetes. - Of these 100 disease-positive individuals, the screening test correctly identified **90 as positive** (true positives). - The remaining **10 individuals** with diabetes tested negative on the screening test (false negatives). - **Sensitivity = 90/100 = 0.90 or 90%** *100/110* - This calculation is incorrect as it uses **110 as the denominator**, which has no basis in the given data. - Sensitivity denominator should be the **total number of disease-positive individuals** according to the gold standard, which is **100**, not 110. - This does not represent any standard epidemiological measure in this context. *80/100* - This option incorrectly assumes **80 true positives** were detected by the screening test. - The question clearly states that **90 individuals tested positive** on the screening test, not 80. - This contradicts the given information. *100/100* - This would represent **perfect sensitivity** (100%), meaning the screening test identified all individuals with diabetes. - However, the screening test only identified **90 positives** while the gold standard confirmed **100 positives**. - This means **10 individuals with diabetes were missed** by the screening test (false negatives), so sensitivity cannot be 100%.
Question 823: The Chi-square test is most commonly used in medical statistics to test for:
- A. Test for goodness of fit
- B. Test for independence of categorical variables (Correct Answer)
- C. Estimating population mean
- D. Comparing two population means
Explanation: ***Test for independence of categorical variables*** - The **Chi-square test of independence** is the most commonly used application in medical research to determine if there is a statistically significant **association between two categorical variables**. - It assesses whether the observed frequencies in a contingency table differ significantly from the frequencies expected if the variables were independent. - **Common medical applications**: relationship between exposure and disease, association between risk factors and outcomes, comparing proportions across groups. *Test for goodness of fit* - The Chi-square **goodness of fit test** is another important application that tests whether observed frequencies of a **single categorical variable** match an expected theoretical distribution. - While this is a valid primary use of Chi-square, it is **less commonly employed** in medical research compared to the test of independence. - **Example use**: testing if observed genetic ratios match Mendelian expectations, or if disease distribution matches a theoretical model. *Estimating population mean* - Estimating a **population mean** requires methods for **continuous data** such as confidence intervals or t-tests. - The Chi-square test operates on **frequency counts of categorical data**, not on numerical measurements, making it inappropriate for mean estimation. *Comparing two population means* - **Comparing means** requires tests designed for continuous data such as the **t-test** (for two groups) or **ANOVA** (for multiple groups). - The Chi-square test analyzes **associations between categories**, not differences in central tendency of continuous variables.
Question 824: What is the denominator used in calculating the under-5 proportional mortality rate?
- A. Number of deaths under 5 years of age
- B. Mid-year under-5 population
- C. Total deaths (Correct Answer)
- D. Mid-year population
Explanation: ***Total deaths*** - The **under-5 proportional mortality rate** measures the proportion of all deaths that occur in children under five years of age. - The denominator for this rate is the **total number of deaths** in a given population across all age groups. *Number of deaths under 5 years of age* - This value represents the **numerator** of the under-5 proportional mortality rate. - It indicates the specific number of deaths within the under-5 age group, rather than the total deaths across all ages. *Mid-year under-5 population* - This is the denominator used in calculating the **under-5 mortality rate** (a true rate), not the proportional mortality rate. - It measures the risk of death in children under five relative to the population of children in that age group. *Mid-year population* - The mid-year population is often used as the denominator for **crude mortality rates** or **incidence rates** for specific diseases within a general population. - It represents the total population at risk, not solely the total number of deaths for proportional mortality.
Question 825: In a positively skewed distribution, which of the following is true?
- A. Mean = Median = Mode
- B. Mode > Median > Mean
- C. Mean > Median > Mode (Correct Answer)
- D. None of the options
Explanation: ***Mean > Median > Mode*** - In a **positively skewed distribution**, the tail of the distribution is longer on the right side, meaning there are more extreme large values. - These large values pull the **mean** towards the right, making it greater than the **median**, which in turn is greater than the **mode**. *Mean = Median = Mode* - This equality holds true for a **symmetrical distribution**, such as a normal distribution, where data points are evenly distributed around the center. - A **positively skewed distribution** is asymmetrical, with a distinct longer tail on one side due to outliers. *Mode > Median > Mean* - This relationship is characteristic of a **negatively skewed distribution**, where the tail extends to the left, indicating a presence of more extreme small values. - In such a case, the **mode** is the largest and the **mean** is the smallest, pulled by the left-skewed tail. *None of the options* - This option is incorrect because the statement **Mean > Median > Mode** accurately describes the relationship between these measures of central tendency in a **positively skewed distribution**. - The other options describe different types of distributions.
Question 826: Which of the following is a parametric test?
- A. Student t-test (Correct Answer)
- B. Sign test
- C. Fisher exact test
- D. Chi-square test
Explanation: ***Student t-test*** - The **Student t-test** is a **parametric test** used to compare the means of two groups. - It assumes the data is **normally distributed** and has **equal variances**. *Sign test* - The **Sign test** is a **non-parametric test** used for paired data. - It examines the direction of differences between pairs, not their magnitude. *Fisher exact test* - The **Fisher exact test** is a **non-parametric test** used for analyzing **categorical data** in a 2x2 contingency table. - It is particularly useful when **sample sizes are small** and the assumptions for a chi-square test are not met. *Chi-square test* - The **Chi-square test** is a **non-parametric test** used to assess independence between **categorical variables** or to compare observed frequencies with expected frequencies. - It does not assume any specific distribution for the data, making it suitable for nominal data.
Question 827: In a study a patient does not know the nature of drug [whether a placebo or curative drug] he is taking. The researcher knows the drug type to be given to the individuals in study. Type of blinding in this study is?
- A. Single (Correct Answer)
- B. Double
- C. Triple
- D. Combined double/triple
Explanation: ***Single*** - In **single-blinded studies**, the participant (patient) is unaware of whether they are receiving the experimental treatment or a placebo. - The investigator (researcher) and/or staff administering the treatment are aware of the treatment assignment. *Double* - In a **double-blinded study**, neither the participants nor the investigators/staff administering the treatment know who is receiving the experimental treatment and who is receiving the placebo. - This method helps to minimize bias from both participant expectations and researcher influence. *Triple* - A **triple-blinded study** extends double-blinding by also ensuring that those analyzing the data are unaware of the treatment assignments. - This adds an extra layer of protection against bias in the interpretation of results. *Combined double/triple* - This option refers to scenarios where a study design might aim to incorporate aspects of both double and triple blinding. - However, in this specific case, the researcher's knowledge of the drug type prevents it from being a fully double or triple-blinded study.
Question 828: Which of the following is NOT a characteristic of a health index?
- A. Validity
- B. Reliability
- C. Affordability (Correct Answer)
- D. Feasibility
Explanation: ***Affordability*** - **Affordability** is not considered a defining characteristic or psychometric property of a health index itself. - The core characteristics of a health index relate to its **measurement properties** (validity, reliability, sensitivity) and **practical applicability** (feasibility), not economic considerations. - While cost may influence whether a health index is adopted in practice, it does not determine the index's ability to accurately measure health status. - Affordability is an external factor related to resource availability rather than an intrinsic quality of the measurement tool. *Validity* - **Validity** is a fundamental characteristic referring to whether a health index measures what it intends to measure. - Types include content validity, criterion validity, and construct validity. - Essential for ensuring the index accurately reflects the health concept being assessed. *Reliability* - **Reliability** is a core characteristic indicating consistency and reproducibility of measurements. - Includes test-retest reliability, inter-rater reliability, and internal consistency. - A reliable health index produces stable results when measuring the same phenomenon under similar conditions. *Feasibility* - **Feasibility** is a recognized characteristic referring to the practical usability of a health index in real-world settings. - Includes ease of administration, time requirements, scoring simplicity, and acceptability to users. - A feasible index can be implemented effectively within available resources and constraints, though this differs from the economic concept of affordability.
Question 829: Why are standardized death rates used in health comparisons?
- A. Calculations are more accurate.
- B. To avoid selection bias.
- C. For valid comparison between populations with different demographic characteristics (Correct Answer)
- D. None of the options.
Explanation: ***For valid comparison between populations with different demographic characteristics*** - **Standardized death rates** adjust for differences in age structure between populations, allowing for a fairer and more accurate comparison of mortality burdens. - This adjustment is crucial because crude death rates can be misleading if one population is significantly older or younger than another, as age is a strong determinant of mortality. *Calculations are more accurate.* - While standardization improves the **validity of comparisons**, it does not inherently make the calculations themselves more "accurate" in a general sense; rather, it makes them more suitable for comparative purposes. - The accuracy of the underlying data (e.g., death registration, population counts) is a separate concern from the standardization process. *To avoid selection bias.* - **Selection bias** typically refers to issues in how individuals are chosen for a study or how data are collected, leading to an unrepresentative sample. - Standardized death rates address **confounding variables** (like age distribution) in population-level comparisons, rather than resolving individual-level selection bias. *None of the options.* - This option is incorrect because there is a valid reason among the choices provided for using standardized death rates. - The primary purpose of standardization is indeed to enable **valid comparisons** between populations with differing demographic profiles.
Question 830: Which of the following statements about standardization is false?
- A. Age specific rates are required in indirect standardization
- B. Direct standardization is used when population is large (Correct Answer)
- C. All are correct
- D. Most commonly used for age differences
Explanation: ***Direct standardization is used when population is large*** - This statement is **false**. Direct standardization is typically used when **age-specific rates for the study population are known** and stable, regardless of the overall population size. - Its purpose is to compare health events across populations while **adjusting for differences in age structure**, making it suitable even for smaller populations if the specific rates are reliable. *Most commonly used for age differences* - This statement is **true**. Standardization, particularly direct and indirect, is most frequently applied to control for **age differences** between populations when comparing health outcomes or rates. - While other factors like sex or socioeconomic status can be standardized, **age** is the most common and often the most critical confounding variable. *Age-specific rates are required in indirect standardization* - This statement is **true**. Indirect standardization relies on **age-specific rates from a standard population** to calculate expected numbers of events in the study population. - This method is particularly useful when the **age-specific rates for the study population are unknown** or unstable (e.g., due to small numbers), making data from a large, known standard population essential. *All are correct* - This statement is **false** because, as explained, the statement "Direct standardization is used when population is large" is incorrect. - This option would only be true if all other individual statements were accurate, which is not the case here.
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Collection and Presentation of Data
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Measures of Central Tendency
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Measures of Dispersion
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Normal Distribution
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Sampling Methods
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Sample Size Calculation
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Hypothesis Testing
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Tests of Significance
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Correlation and Regression
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Survival Analysis
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Multivariate Analysis
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Statistical Software in Research
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