Biostatistics Practice Questions

Q: In which study design is carry-over effect a PRIMARY methodological concern that requires washout periods between treatment phases?

Cross-over study. ***Correct Option: Cross-over study*** - In a **cross-over study**, each participant receives multiple treatments sequentially, meaning the effects of a previous treatment could carry over to the next treatment phase. - A **washout period** is essential in this design to allow the effects of the prior treatment to dissipate, preventing them from influencing the results of the subsequent treatment. - This is the PRIMARY design where carry-over effects are an inherent methodological concern. *Incorrect Option: Case-control study* - This design compares subjects with a condition (cases) to subjects without the condition (controls) to identify past exposures or risk factors; it does not involve sequential treatments that would lead to a carry-over effect. - The primary concern in case-control studies is **recall bias**, as participants must remember past exposures, not carry-over effects. *Incorrect Option: Concurrent parallel design* - Participants are randomly assigned to one of several treatment groups and receive *only one* treatment throughout the study, eliminating the possibility of a treatment from one phase affecting another. - While it avoids carry-over effects, it often requires a larger sample size compared to cross-over studies to achieve similar statistical power. *Incorrect Option: Cohort study* - This design tracks a defined group of individuals (cohort) over time to observe the incidence of disease and identify risk factors; it does not involve the administration of sequential treatments. - Key concerns in cohort studies include **loss to follow-up** and the potential for a long study duration, rather than carry-over effects.

Q: For calculation of sample size for a prevalence study, all of the following are necessary except:

Power of the study. ***Power of the study*** - The **power of a study** is primarily relevant when calculating sample sizes for **hypothesis testing** (e.g., comparing two groups) to detect a statistically significant difference if one exists. - In a prevalence study, the goal is to estimate a proportion or prevalence with a certain level of precision, rather than to test a hypothesis. *Prevalence of disease in population* - An **estimated prevalence** is crucial for sample size calculation in prevalence studies, as it directly influences the variability of the proportion being estimated. - A higher or lower estimated prevalence affects the required sample size to achieve a desired level of precision. *Significance level* - The **significance level (alpha)** defines the probability of rejecting the null hypothesis when it is true (Type I error). - While essential for hypothesis testing, it is still used in prevalence studies to define the **confidence level** for the estimated prevalence (e.g., 95% confidence interval corresponds to an alpha of 0.05). *Desired precision* - **Desired precision**, often expressed as the **margin of error**, is a fundamental component of sample size calculation for prevalence studies. - It specifies how close the sample estimate should be to the true population prevalence.

Q: Which among the following is the major practical problem in a cohort study?

Differential loss of follow up. ***Differential loss of follow up*** - **Differential loss to follow-up** occurs when participants lost to follow-up differ systematically concerning exposure and outcome, potentially introducing **selection bias**. - This is a significant practical problem as it can distort the observed association between exposure and outcome, leading to biased results. *Long duration of study* - While **cohort studies** can indeed be **longitudinal** and require a long duration, this is more of an inherent characteristic and resource challenge rather than a "problem" that significantly compromises the validity of the study design itself. - The long duration primarily affects costs and feasibility but doesn't inherently invalidate the findings as much as differential loss to follow-up. *Can be used only for rare conditions* - This statement is incorrect; **cohort studies** are actually **inefficient for rare diseases** because a very large sample size would be needed to observe enough cases of the outcome. - **Case-control studies** are generally preferred for investigating **rare conditions** due to their retrospective outcome-to-exposure design. *No significant problems with cohort studies.* - This statement is incorrect; **cohort studies**, like all observational study designs, have inherent **methodological challenges** and potential sources of bias. - Problems include the **cost** and **time commitment**, **loss to follow-up**, and the potential for **confounding**, all of which require careful consideration in study design and analysis.

Q: What does specificity in a diagnostic test measure?

True negative. ***True negative*** - Specificity measures the **proportion of true negatives** correctly identified by the test. - It indicates the test's ability to correctly identify individuals **without the disease** who test negative. - **Formula: Specificity = TN / (TN + FP)** where TN = True Negatives, FP = False Positives. *True positive* - **True positives** are measured by **sensitivity**, not specificity. - Sensitivity measures the proportion of people with the disease who test positive. *False positive* - **False positives** reduce specificity but are not what specificity measures. - High specificity means fewer false positives (more specific for the disease). *False negative* - **False negatives** are related to **sensitivity**, not specificity. - A test with low sensitivity will have a higher rate of false negatives.

Q: Which of the following is not a method of random sampling?

Quota sampling. ***Quota sampling*** - **Quota sampling** is a non-probability sampling method where researchers select a sample based on pre-defined characteristics to match the population's proportions. - It does not involve random selection at any stage, making it a non-random sampling technique. *Cluster sampling* - **Cluster sampling** is a probability (random) sampling technique where the population is divided into clusters, and then a random sample of these clusters is selected. - All units within the selected clusters are then included in the sample, or a random sample is taken from within the selected clusters. *Stratified sampling* - **Stratified sampling** is a probability (random) sampling method that involves dividing the population into homogeneous subgroups (strata) and then taking a random sample from each stratum. - This method ensures representation from all important subgroups within the population. *Simple random* - **Simple random sampling** is a basic probability (random) sampling technique where every member of the population has an equal chance of being selected for the sample. - This method is considered the most fundamental type of random sampling.

Q: Which of the following is a true statement regarding longitudinal studies?

Incidence rate can be calculated. ***Correct: Incidence rate can be calculated*** - **Longitudinal studies** follow participants over time, allowing researchers to identify **new cases** of disease as they occur - Since the population at risk is followed prospectively, **incidence rates** (the rate at which new cases develop) can be accurately calculated - This is a **key advantage** that distinguishes longitudinal studies from cross-sectional studies, which can only calculate **prevalence** - Calculation of incidence is essential for understanding **disease risk** and evaluating **temporal relationships** between exposure and outcome *Studies natural history of disease* - While longitudinal studies CAN observe disease progression over time, this is not their most specific or defining characteristic - Many study designs (including case series and registry studies) can study natural history - **Natural history studies** are a specific subset of longitudinal studies, not a universal feature *Primarily designed to establish causation* - Longitudinal studies provide **evidence for temporal relationships** but are not primarily designed to establish causation - **Randomized controlled trials (RCTs)** are the gold standard for establishing causation through randomization and control of confounding variables - Longitudinal observational studies are subject to confounding and bias, limiting causal inference *More time consuming than cross-sectional studies* - While factually true, this describes a **limitation** rather than a defining characteristic or advantage - Many study designs are time-consuming; this is not specific to longitudinal studies - The question asks for a true statement that characterizes what longitudinal studies ARE or DO, not their practical constraints

Q: Which of the following statements about the normal distribution curve is true?

Mean = Median. ***Mean = Median*** - In a **normal distribution curve**, the data is perfectly symmetrical around its center. - This symmetry ensures that the **mean, median, and mode** all coincide at the peak of the curve. - This is a defining characteristic of the **Gaussian (normal) distribution**. *Mean = 2 Median* - This statement is incorrect; in a **normal distribution**, the mean and median are equal, not a multiple of each other. - Such a relationship (Mean = 2 Median) would imply a **positively skewed distribution**, which is not characteristic of a normal distribution. *Median = Variance* - The **median** is a measure of **central tendency**, representing the middle value of the data set. - **Variance** is a measure of **data dispersion** (how spread out the data is), measured in squared units. - These two measures are fundamentally different concepts and generally not equal. *Standard Deviation = 2 Variance* - **Standard deviation** is the **square root of the variance** (SD = √Variance), not twice the variance. - This relationship is mathematically incorrect and does not hold true for any distribution.

Question 1

In which study design is carry-over effect a PRIMARY methodological concern that requires washout periods between treatment phases?

Accepted Answer

Cross-over study

Answer

Concurrent parallel design

Answer

Case-control study

Answer

Cohort study

Question 2

This study found a correlation coefficient of +0.7 between self-reported work satisfaction and life expectancy in a random sample of 5,000 corporate workers, with a p-value of 0.01. This means that:

Accepted Answer

Strong statistically significant (+) association between work satisfaction and life expectancy.

Answer

Correlation does not imply that 70% of people who enjoy work shall live longer.

Answer

Correlation coefficient of +0.7 indicates a moderate positive relationship, not a percentage.

Answer

Work satisfaction is moderately associated with life expectancy.

Question 3

For calculation of sample size for a prevalence study, all of the following are necessary except:

Accepted Answer

Power of the study

Answer

Significance level

Answer

Desired precision

Answer

Prevalence of disease in population

Question 4

Which of the following best describes a cohort study?

Accepted Answer

A study that observes a group of individuals over time to assess the impact of a risk factor.

Answer

A cross-sectional study that collects data at a single point in time.

Answer

A case-control study that compares individuals with a condition to those without.

Answer

A study that randomly assigns participants to intervention and control groups.

Question 5

Which among the following is the major practical problem in a cohort study?

Accepted Answer

Differential loss of follow up

Answer

Long duration of study

Answer

Can be used only for rare conditions

Answer

No significant problems with cohort studies.

Question 6

What does specificity in a diagnostic test measure?

Accepted Answer

True negative

Answer

True positive

Answer

False positive

Answer

False negative

Question 7

Which of the following is not a method of random sampling?

Accepted Answer

Quota sampling

Answer

Cluster sampling

Answer

Stratified sampling

Answer

Simple random

Question 8

Which of the following is a true statement regarding longitudinal studies?

Accepted Answer

Incidence rate can be calculated

Answer

Studies natural history of disease

Answer

Primarily designed to establish causation

Answer

More time consuming than cross-sectional studies

Question 9

After applying a statistical test, an investigator gets a p-value of 0.01. What does this indicate about the null hypothesis?

Accepted Answer

There is a 1% probability of observing the data, or something more extreme, if the null hypothesis is true.

Answer

There is a 1% probability of incorrectly rejecting the null hypothesis when it is true.

Answer

The test has a 99% chance of detecting a true effect if it exists.

Answer

The null hypothesis is likely to be rejected.

Question 10

Which of the following statements about the normal distribution curve is true?

Accepted Answer

Mean = Median

Answer

Mean = 2 Median

Answer

Median = Variance

Answer

Standard Deviation = 2 Variance

Biostatistics — MCQs

Biostatistics — MCQs

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