PHAR 6123 Exam 2 Chicago State University
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Free PHAR 6123 Exam 2 Chicago State University Questions
Which of the following best describes the need for pharmacoepidemiology in the U.S. Healthcare system?
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Creates a sterile environment for drugs to be studied
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The healthcare system is piecemealed with data located in a number of different "data silos"
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Creates additional work for pharmaceutical manufacturers
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Promotes evidence based medicine, inexpensive to run, and determines real-world effectiveness
Explanation
Explanation:
Correct Answer: (B) The healthcare system is piecemealed with data located in a number of different "data silos"
The U.S. healthcare system is fragmented, with patient data spread across numerous separate databases including insurance claims, electronic health records, pharmacy records, and hospital systems. Pharmacoepidemiology is needed to navigate and integrate these dispersed data sources to generate meaningful, population-level evidence about drug safety and effectiveness in real-world settings.
A. Creates a sterile environment for drugs to be studied — Pharmacoepidemiology operates in real-world settings, not sterile or controlled environments. This describes experimental research conditions. C. Creates additional work for pharmaceutical manufacturers — The need for pharmacoepidemiology is driven by public health and patient safety needs, not by creating additional workload for manufacturers. D. Promotes evidence-based medicine and determines real-world effectiveness — While these are benefits of pharmacoepidemiology, they describe outcomes rather than the underlying need that drives its use in the fragmented U.S. healthcare system.
The Student's t-test is mainly used when:
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Sample size is always greater than 100
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Population variance is unknown
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Population variance is known
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Data is categorical
Explanation
Correct Answer: B) Population variance is unknown
The Student's t-test is specifically used when the population variance (or standard deviation) is unknown and must be estimated from the sample data. This is the most common real-world scenario in research, as the true population variance is rarely known. When the population variance is known, the z-test is the more appropriate statistical test.
The t-test does not require a sample size greater than 100 — it is actually most valuable with smaller sample sizes. It is also not appropriate for categorical data, which is analyzed using non-parametric tests such as the chi-square test.
A patient satisfaction survey uses the following responses: Strongly agree, Agree, Disagree, Strongly disagree. This variable is best classified as:
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Nominal
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Interval
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Ordinal
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Continuous
Explanation
Correct Answer: C) Ordinal
The responses Strongly agree, Agree, Disagree, and Strongly disagree represent an ordinal level of measurement because the categories have a meaningful rank or order, but the exact distance between each category is not equal or precisely measurable. Nominal data has categories with no inherent order, such as blood type or gender.
Interval data has ordered categories with equal and measurable intervals between values, such as temperature in Celsius. Continuous data can take any value within a range and is measured on an uninterrupted scale, such as weight or blood pressure. Likert-scale responses like this survey are the classic example of ordinal data.
A Type II error (β) represents which of the following scenarios in hypothesis testing?
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Rejecting a true null hypothesis
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Accepting a true null hypothesis
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Correctly rejecting a false null hypothesis
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Failing to reject a false null hypothesis
Explanation
Explanation:
Correct Answer: (D) Failing to reject a false null hypothesis
A Type II error (β) occurs when a researcher fails to reject a null hypothesis that is actually false — in other words, missing a true effect that exists. This is also known as a false negative. The probability of avoiding a Type II error is the statistical power of the study (1 - β).
A. Rejecting a true null hypothesis — This describes a Type I error (α), also known as a false positive. B. Accepting a true null hypothesis — This is a correct decision, not an error. C. Correctly rejecting a false null hypothesis — This describes statistical power, which is the desired outcome, not an error.
Which of the following best defines "medication error"?
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An injury resulting from the use of a drug
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A response to a drug which is noxious and unintended and which occurs at doses normally used in man for prophylaxis, diagnosis, or therapy of disease or for the modification of physiologic function
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Any untoward medical occurrence that may present during treatment with a pharmaceutical product but which does not necessarily have a causal relationship with the treatment
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Postmarketing surveillance occurs when after approval of a drug, healthcare providers or patients may report adverse events to the FDA via FDA MedWatch
Explanation
Explanation:
Correct Answer: (A) An injury resulting from the use of a drug
A medication error is a preventable event that may cause or lead to inappropriate medication use or patient harm. It can occur at any stage of the medication process including prescribing, dispensing, or administration, and results in injury or potential injury from the use of a drug.
B. This defines an adverse drug reaction (ADR) as established by the WHO, not a medication error. C. This defines an adverse event, which is any untoward medical occurrence during treatment regardless of causal relationship, not specifically a medication error. D. This describes postmarketing surveillance and the MedWatch reporting system, not the definition of a medication error.
What does a significance level (α) of 0.05 represent in clinical research?
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The probability of accepting the null hypothesis when it is false
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The probability of rejecting the null hypothesis when it is true
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The probability of a study having low power
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The probability of a study taking too long
Explanation
Explanation:
Correct Answer: (B) The probability of rejecting the null hypothesis when it is true
A significance level (α) of 0.05 represents the threshold for a Type I error — the probability of incorrectly rejecting a true null hypothesis (a false positive). Setting α at 0.05 means the researcher accepts a 5% risk of concluding there is an effect when none actually exists.
A. The probability of accepting the null hypothesis when it is false — This describes a Type II error (β), not the significance level α. C. The probability of a study having low power — Statistical power is related to β, not α. A low α does not directly indicate low power. D. The probability of a study taking too long — This is not a statistical concept and has no relation to the significance level.
Which statement best distinguishes a prospective cohort study from a case-control study?
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Cohort studies start with the outcome; Case-control studies start with the exposure
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Cohort studies cannot calculate risk, whereas case-control studies can
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Cohort studies group subjects based on exposure; Case-control studies group subjects based on the presence of a disease
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Cohort studies are always cheaper and faster than case-control studies
Explanation
Correct Answer: C) Cohort studies group subjects based on exposure; Case-control studies group subjects based on the presence of a disease
The fundamental distinction between these two study designs lies in how participants are grouped. In a cohort study, participants are grouped based on their exposure status (exposed vs. unexposed) and then followed forward in time to see who develops the outcome.
In a case-control study, participants are grouped based on whether they already have the disease (cases) or do not (controls), and researchers then look backward to compare their exposure histories. Option A is the reverse of the truth. Option B is incorrect — cohort studies can directly calculate relative risk, whereas case-control studies cannot.
Option D is incorrect — cohort studies are generally more time-consuming and expensive, while case-control studies tend to be quicker and cheaper.
Which of the following tests is used to test differences in the independent t-test when the data is non-normally distributed? (Select all that apply.)
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Pearson correlation coefficient
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Mann-Whitney U test
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Fisher's exact test
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Spearman's rank correlation
Explanation
Explanation:
Correct Answer: (B) Mann-Whitney U test
The Mann-Whitney U test is the non-parametric equivalent of the independent t-test. It is used when the assumption of normality is violated and compares the distributions of two independent groups without requiring normally distributed data.
A. Pearson correlation coefficient — Used to measure the strength and direction of the linear relationship between two continuous variables, not for group comparisons. C. Fisher's exact test — Used to examine the association between two categorical variables in a contingency table, not for comparing non-normal continuous data between groups. D. Spearman's rank correlation — A non-parametric measure of correlation between two variables, not a substitute for the independent t-test.
A study is analyzing patient survival after receiving a new chemotherapy regimen. Which of the following statistical methods is most appropriate for comparing survival times between two treatment groups while adjusting for confounding variables?
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Logistic regression
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Cox proportional hazards model
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Linear regression
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Chi-square test
Explanation
Explanation:
Correct Answer: (B) Cox proportional hazards model
The Cox proportional hazards model is the most appropriate method for analyzing time-to-event data (survival analysis) while simultaneously adjusting for multiple confounding variables. It estimates hazard ratios and can handle censored data, making it ideal for comparing survival times between treatment groups.
A. Logistic regression — Predicts the probability of a binary outcome but does not account for the time until an event occurs or handle censored data. C. Linear regression — Used for continuous outcome variables and assumes a linear relationship; not appropriate for survival time data with censoring. D. Chi-square test — Used for comparing categorical proportions and cannot adjust for confounding variables or handle time-to-event data.
In case control studies, researchers identify individuals with the outcome of interest and compare them with individuals without the outcome with respect to their exposure to certain risk factors.
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True
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False
Explanation
Correct Answer: A) True
This statement accurately describes the defining methodology of a case-control study. Researchers begin by identifying cases — individuals who have already developed the outcome or disease of interest — and controls — individuals who have not developed the outcome. Both groups are then compared retrospectively with respect to their past exposure to specific risk factors.
This backward-looking design makes case-control studies particularly useful for studying rare diseases or outcomes, as researchers do not need to wait for the outcome to develop. The measure of association used is the Odds Ratio (OR), since incidence rates cannot be directly calculated from this design.
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