Biostatistics Practice Questions

Q: Which of the following is an example of a case-control study that investigates the relationship between a risk factor and a disease?

All of the options. ***All of the options*** - **All three scenarios** represent classic examples of case-control studies in epidemiology, where investigators identified cases of disease and compared them to controls to determine past exposure to risk factors. - Case-control studies are **retrospective** in design, starting with the outcome (disease) and looking backward to identify exposure history. **Maternal smoking and congenital malformation** - Cases: Children with congenital malformations - Controls: Children without malformations - Exposure assessed: History of maternal smoking during pregnancy - This exemplifies the typical case-control approach to studying teratogenic exposures. **Thalidomide exposure and teratogenicity** - The landmark studies by **Lenz (1961)** and **McBride (1961)** were **case-control studies** - Cases: Infants with phocomelia (limb malformations) - Controls: Infants without malformations - They looked backward from the cases to identify thalidomide exposure during pregnancy - This rapid identification of the thalidomide-phocomelia link demonstrates the power of case-control methodology for rare outcomes. **Vaginal adenocarcinoma and intrauterine exposure to DES** - The classic **Herbst et al. (1971)** study was a **case-control study** - Cases: Young women with clear cell adenocarcinoma of the vagina - Controls: Age-matched women without the disease - They investigated past exposure and discovered the association with maternal DES use during pregnancy - This is a textbook example of case-control design for investigating rare diseases with long latency periods.

Q: In the context of statistical analysis, if you have the value of one variable, which coefficient would you use to predict the value of another variable?

Coefficient of regression. ***Coefficient of regression*** - The **coefficient of regression** (or **regression coefficient**) is fundamental in **regression analysis**, which is specifically designed to predict the value of a **dependent variable** based on the value of one or more **independent variables**. - It quantifies the expected change in the dependent variable for a unit change in the independent variable. *Coefficient of variation* - The **coefficient of variation** is a measure of **relative variability** or dispersion, expressing the standard deviation as a percentage of the mean. - It describes the extent of variation in relation to the mean but does not provide a basis for predicting one variable from another. *Coefficient of correlation* - The **coefficient of correlation** measures the **strength and direction of a linear relationship** between two variables. - While it indicates how well two variables move together, it does not directly enable the prediction of one variable's value from another; that is the role of regression. *Coefficient of determination* - The **coefficient of determination (R²)** represents the **proportion of the variance** in the dependent variable that can be explained by the independent variable(s) in a regression model. - It quantifies how well the regression model fits the observed data, but it is not used directly for prediction; rather it is for assessing the predictive power of the model.

Q: In a normal distribution with mean = 200 and standard deviation = 20, what is the range in which 68% of the values will fall?

180-220. ***180-220*** - In a **normal distribution**, approximately 68% of the data falls within **one standard deviation** of the mean. - With a mean of 200 and a standard deviation of 20, this range is calculated as 200 ± 20, which equals **180-220**. *160-240* - This range represents the values falling within **two standard deviations** from the mean (200 ± 2*20 = 160-240). - Approximately **95%** of the values in a normal distribution fall within this range, not 68%. *170-230* - This range does not correspond to a standard integer multiple of the standard deviation from the mean (200 ± 1.5*20 = 170-230). - It does not represent a standard percentage of values in a normal distribution like 68%, 95%, or 99.7%. *190-210* - This range represents half of one standard deviation from the mean (200 ± 0.5*20 = 190-210). - This range covers a smaller percentage of values than 68%, typically around **38%**.

Q: The variation in data is compared with another data set by:

Coefficient of variation. ***Coefficient of variation*** - The **coefficient of variation (CV)** is a standardized measure of dispersion of a probability distribution or frequency distribution. - It expresses the **standard deviation** as a percentage of the **mean**, making it useful for comparing the variability of two independent data sets with different units or widely different means. *Standard Error of Mean* - The **Standard Error of the Mean (SEM)** is used to estimate the variability between sample means if multiple samples were taken from the same population. - It primarily quantifies the accuracy with which a sample mean represents a population mean, not for comparing variations between different data sets. *Standard Deviation* - **Standard deviation (SD)** measures the amount of variation or dispersion of a set of values *within a single data set*. - While it quantifies variability, it is not ideal for directly comparing the variability of two data sets with different units or means because it isn't normalized. *Variance* - **Variance** measures how far each number in the set is from the mean; it is the **average of the squared differences** from the mean. - Like standard deviation, variance describes the spread within a single dataset and is not normalized for direct comparison between datasets with different scales.

Q: In a study of 200 patients, CA-125 testing was performed. Among the 100 patients who tested positive, 60 had ovarian cancer confirmed by histopathology. Among the 100 patients who tested negative, 20 had ovarian cancer confirmed by histopathology. What is the negative predictive value of this test?

80/100. ***80/100*** - The **negative predictive value (NPV)** is the probability that a patient who tests negative actually does not have the disease. - In this case, 100 patients tested negative, and 20 of them *did* have ovarian cancer, meaning 80 **did not** have ovarian cancer. Thus, NPV = 80/100. *20/100* - This represents the number of **false negatives** among all patients who tested negative, not the negative predictive value. - A false negative occurs when the test result is negative, but the disease is actually present. *40/100* - This value represents the number of patients who tested positive but **did not** have the disease (false positives), calculated as 100 (total positive tests) - 60 (true positives) = 40. - This is not the calculation for negative predictive value. *60/100* - This represents the number of **true positives** among all patients who tested positive. - This is a component of **positive predictive value**, not negative predictive value.

Q: In a study assessing malnutrition among young children, 100 children were selected from rural and urban areas (50 from each area). Out of these, 30 children from rural areas and 20 children from urban areas were found to be malnourished. Which statistical test is appropriate for comparing the proportions of malnourished children between the two groups?

Chi-square. ***Chi-square*** - The **chi-square test** is used to compare proportions or frequencies between two or more categorical groups. Here, we are comparing the proportion of malnourished children (a categorical outcome) between two different living areas (rural vs. urban, also categorical). - This test determines if there is a statistically significant association between the two categorical variables. *Paired t-test* - A **paired t-test** is used to compare the means of two related groups or samples, such as measurements taken before and after an intervention on the same individuals. - This scenario involves comparing independent groups (rural vs. urban children) and proportions, not means from paired samples. *The standard error of mean* - The **standard error of the mean (SEM)** is a measure of the statistical accuracy of an estimate; specifically, it's the standard deviation of the sample mean's distribution. - It is used to quantify the variability of sample means, not to perform a comparative hypothesis test between two groups. *ANOVA* - **ANOVA (Analysis of Variance)** is used to compare the means of **three or more independent groups**. While it compares means, it is not appropriate for comparing proportions between just two groups. - If we were comparing the mean weight of children across three or more living areas, ANOVA would be suitable, but not for comparing proportions between two groups.

Q: Which type of study is used to determine the cross product ratio?

Case control. ***Case control*** - **Case-control studies** are specifically designed to compare exposure histories between individuals with a disease (cases) and those without (controls), which directly facilitates the calculation of the **odds ratio**. - The odds ratio is called the **cross-product ratio** because of its calculation method: (a×d)/(b×c), where the products are "crossed" in the 2×2 contingency table. - This is the **primary measure of association** in case-control studies and serves as an approximation of the relative risk, particularly for rare outcomes. *Cohort* - **Cohort studies** follow exposed and unexposed groups over time to determine the incidence of disease, allowing for the direct calculation of **relative risk** and **attributable risk**. - While odds ratios can be calculated from cohort data, the **relative risk** is the primary and preferred measure of association in cohort studies, not the cross-product ratio. *Cross sectional* - **Cross-sectional studies** assess the prevalence of disease and exposure at a single point in time, providing a snapshot of the population's health status. - They measure **prevalence** rather than incidence and can calculate prevalence ratios, but the term "cross-product ratio" specifically refers to the odds ratio from **case-control** study designs. *RCT* - **Randomized controlled trials (RCTs)** are experimental studies where participants are randomly assigned to intervention or control groups to evaluate treatment efficacy. - They primarily focus on determining the **relative risk** or **risk ratio** of an outcome following an intervention and are not designed for calculating the cross-product ratio (odds ratio) as used in observational case-control studies.

Q: Which measure indicates the diagnostic power of a test to correctly identify those with a disease?

Sensitivity. ***Positive predictive value*** - It refers to the probability that subjects with a positive test result truly have the disease, highlighting the test's **diagnostic accuracy** [1]. - A high positive predictive value indicates that the test is effective at diagnosing the disease in the population tested. *Sensitivity* - Sensitivity measures the ability of a test to correctly identify those with the disease (true positives), but does not account for the test result's predictive capability [1]. - It is important for screening, but **not directly the diagnostic power** for those already tested. *Negative predictive value* - This indicates the probability that subjects with a negative test result truly do not have the disease, focusing on true negatives rather than correct diagnosis of the condition [1]. - While informative, it does not assess the ability to correctly diagnose the disease when the result is positive. *Specificity* - Specificity is the measure of a test's ability to correctly identify those without the disease (true negatives), not diagnosing the disease accurately among those tested [1]. - It is essential for determining false positives but not for assessing the overall diagnostic power of a test. **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 1

Which of the following is an example of a case-control study that investigates the relationship between a risk factor and a disease?

Accepted Answer

All of the options

Answer

Maternal smoking and congenital malformation

Answer

Thalidomide exposure and teratogenicity

Answer

Vaginal adenocarcinoma and intrauterine exposure to DES

Question 2

In the context of statistical analysis, if you have the value of one variable, which coefficient would you use to predict the value of another variable?

Accepted Answer

Coefficient of regression

Answer

Coefficient of variation

Answer

Coefficient of correlation

Answer

Coefficient of determination

Question 3

Which of the following best describes the Paired T test?

Accepted Answer

Test used to assess quantitative observations before and after an intervention.

Answer

Test used for categorical data.

Answer

Test applied to compare means of two independent groups.

Answer

None of the options.

Question 4

In a normal distribution with mean = 200 and standard deviation = 20, what is the range in which 68% of the values will fall?

Accepted Answer

180-220

Answer

160-240

Answer

170-230

Answer

190-210

Question 5

The variation in data is compared with another data set by:

Accepted Answer

Coefficient of variation

Answer

Standard Deviation

Answer

Standard Error of Mean

Answer

Variance

Question 6

In a study of 200 patients, CA-125 testing was performed. Among the 100 patients who tested positive, 60 had ovarian cancer confirmed by histopathology. Among the 100 patients who tested negative, 20 had ovarian cancer confirmed by histopathology. What is the negative predictive value of this test?

Accepted Answer

80/100

Answer

20/100

Answer

40/100

Answer

60/100

Question 7

In a study assessing malnutrition among young children, 100 children were selected from rural and urban areas (50 from each area). Out of these, 30 children from rural areas and 20 children from urban areas were found to be malnourished. Which statistical test is appropriate for comparing the proportions of malnourished children between the two groups?

Accepted Answer

Chi-square

Answer

Paired t-test

Answer

The standard error of Mean

Answer

ANOVA

Question 8

Which type of study is used to determine the cross product ratio?

Accepted Answer

Case control

Answer

Cohort

Answer

Cross sectional

Answer

RCT

Question 9

In a screening test for DM out of 1000 population, 90 were positive. When the gold standard test was applied to the entire population, 100 were found to have the disease. Assuming all 90 screening positives were confirmed as true positives by the gold standard, calculate the sensitivity.

Accepted Answer

True positives divided by total actual positives (90%)

Answer

All positives identified by the test assumed as true positives (100%)

Answer

Underestimated true positives divided by total actual positives (80%)

Answer

Total positives identified by the test divided by total actual positives (90%)

Question 10

Which measure indicates the diagnostic power of a test to correctly identify those with a disease?

Accepted Answer

Sensitivity

Answer

Negative predictive value

Answer

Specificity

Answer

Positive predictive value

Biostatistics — MCQs

Biostatistics — MCQs

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