First Aid, Chapter 6 Research Principles

Question 1

What is the definition of cross-sectional study? What is an example? What are the strengths and weaknesses?

Answer 1

• Defined: Subjects sampled from a population and data regarding presence or absence of exposure and disease are collected at the same time
• Example: In a specific group, is there a relationship between smoking and lung cancer?
• Strengths: Inexpensive and quick to perform; population-based; and provides a timely “snapshot”
• Weaknesses: Recall bias; lacks time sequence (i.e., it is not always possible to discern whether the exposure preceded or followed the disease)

Question 2

What is the definition of case series study? What is an example? What are the strengths and weaknesses?

Answer 2

• Defined: Tracks patients with a known exposure who have been given similar treatment, or examines their records for exposure and outcome
• Example: A clinical report on a series of patients
• Strengths: Provides a method of investigating uncommon diseases; inexpensive; and can be hypothesis-generating
• Weaknesses: No control group with which to compare outcomes; no statistical validity; and may be confounded by selection bias

Question 3

If cost and time were unlimited, which two studies would yield the most robust data?

Answer 3

Cohort and RCT

Question 4

What is the definition of case control study? What is an example? What are the strengths and weaknesses?

Answer 4

• Defined: Compare subjects who have a condition (the “cases”) with patients who do not have the condition but are otherwise similar (the “controls”), examining how frequently the risk factor is present in each group
• Example: Do women who use hormone replacement therapy (HRT) have reductions in the incidence of heart disease?
• Strengths: Inexpensive and quick study of (several) risk factors; useful in studying infrequent events or when populations would have to be tracked for long periods of time (e.g., development of cancer); and useful for generating odds ratio (OR)
• Weaknesses: Do not indicate the absolute risk of factor in question; suffer from confounders since it can be difficult to separate the “chooser” from the “choice” (e.g., those who wear bike helmets vs. those who choose not to wear bike helmets); and do not show cause and effect; recall bias

Question 5

What is the definition of cohort study? What is an example? What are the strengths and weaknesses?

Answer 5

• Defined: Form of longitudinal study comparing a group of people who share a common characteristic or experience within a defined period with another group. Importantly, cohort identified before appearance of the disease or condition under investigation
• Examples: Framingham Heart Study and Nurses’ Health Study
• Strengths: Longitudinal observations over time; collection of data at regular intervals; reduced recall error; considered gold standard in observational epidemiology; and useful for generating relative risk (RR)
• Weaknesses: Expensive to conduct, sensitive to attrition, and requires lengthy follow-up time to generate useful data

Question 6

What is the definition of a randomized control trial? What is an example? What are the strengths and weaknesses?

Answer 6

• Defined: Random allocation of different interventions to subjects
• Example: Comparison of a standard drug therapy with a new experimental medication regimen; comparison of a new drug with placebo group
• Strengths: Consistent selection of subjects and randomization removes most forms of bias
• Weaknesses: Expensive; attrition to or loss of follow-up occurs; and treated individuals may not be fully compliant with treatment

Question 7

What is a nominal variable? What is an example?

Answer 7

Data in the form of frequencies fitting discrete, distinct categories.

Example: Race; gender; counting a class, where each individual is either a male or a female, and they cannot be ranked numerically by this data

Question 8

What is an ordinal variable? What is an example?

Answer 8

Measures of physical quantities that can be ranked

Example: Small, medium, large; responses on a Likert scale

Question 9

What is an interval variable? What is an example?

Answer 9

Differences between the values correspond to real differences between the physical quantities that the scale measures

Example: Differences in height correspond to actual physical differences

Question 10

What is a type 1 error? Give an example.

Answer 10

Type I Error—Occurs when the null hypothesis is falsely rejected. A p value indicates the chance that an error is made by accepting the difference between treatments when, in reality, there was no true difference.

-Example: The null hypothesis states no difference exists between the response to drug A and the response to drug B.
o Drug A increased (forced expiratory volume in 1 second) (FEV1) by 0.41 L
o Drug B increased FEV1 by 0.05 L
o p

Question 11

What is a type 2 error? Give an example.

Answer 11

Type II Error—Occurs when the null hypothesis is not rejected when it is false. In other words, the study fails to find a true difference when one is actually present. A common reason for a type II error is that the sample size is too small.

-Example: The null hypothesis states that there is no difference between the response to drug A and drug B.
o Drug A increased FEV1 by 0.26 L
o Drug B increased FEV1 by 0.09 L
o p = 0.25

-Conclusion: Drug A is “not significantly” better than drug B.

Question 12

What is the statistical power of a study?

Answer 12

The percentage chance that a difference will be detected if a difference does exist.

-Calculation: 1 minus the probability of a type II error. For example, if the probability of a type II error in a study is 5%, the statistical power of the study is 95%.

Question 13

What percentage of a bell-shaped normally distributed population is within 1 standard deviation? 2 standard deviations?

Answer 13

Produces a bell-shaped curve, where 68% of observations are within one standard deviation (1 SD), and 95% of observations are within 2 SD. Thus, 2.5% of observations are greater than 2 SD above the 95% level, and 2.5% are less than the 95% level. These two populations of 2.5% of observations represent the “two tails” of the bell-shaped curve.

Question 14

What is a p-value?

Answer 14

Express the probability of rejecting the null hypothesis due to chance, when the null hypothesis is true (type I error).  Example: If p = 0.05, there is a 5% chance that the observed results are due to chance alone.

Question 15

What is an odds ratio?

Answer 15

The probability of occurrence of an event over the probability of nonoccurrence. For example, OR = odds that a case was exposed / odds that a control was exposed. Here, however, the term odds can be defined differently, according to the situation.

Question 16

What is relative risk?

Answer 16

The ratio of the risk of disease (or death) among people who are exposed to the risk factor compared with the risk among people who are not exposed. Alternatively, relative risk can be defined as the ratio of the cumulative incidence rate among those exposed compared with the rate among those not exposed. In either case, the term relative risk is synonymous with risk ratio.

Question 17

What is prevalence?

Answer 17

The percentage of the population with existing disease (at one time point or during one time period) and, as such, is a measure of present disease (prevalence = present).

Question 18

What is incidence?

Answer 18

Incidence—The number of new disease cases in the population over an interval of observation (incidence = new).

Question 19

What is sensitivity? What is the calculation?

Answer 19

Sensitivity—The fraction of all true cases the test detects (i.e., among those who have the disease, it refers to how many test positive).

• Defined as true-positive tests or number with disease
• Sensitivity is associated with the false-negative rate of a test; and, therefore, can be used to rule out disease. For tests with a low false-negative rate, a negative result rules out disease. (Recall tip: SNout.) -Calculation: Sensitivity = true positive / (true positive + false negative) or a/(a +c)

Question 20

What is the specificity of a test?

Answer 20

Specificity—The fraction of all negative cases the test detects (i.e., among those who do not have the disease, it refers to how many test negative).

• Defined as true-negative tests / number without the disease
• Tests with high specificity are associated with a low number of false positives and can be used to rule in disease. (Recall tip: SPin.)
• Calculation: Specificity = true negative / (false positive + true negative) or d/(b + d)

Question 21

What is the positive predictive value? What is the calculation?

Answer 21

Positive Predictive Value (PPV)—Describes the probability that a positive test indicates disease.

Calculation: PPV = True positive / (true positive + false positive) or a/(a + b)

-PPV determines how many actually have the disease from among those who test positive. This information is found on the first row of a 2 × 2 table (Table 6-3).

Question 22

What type of error occurs when the null hypothesis is falsely rejected?

Answer 22

Type I error

Question 23

What is the negative predictive value? What is the calculation?

Answer 23

Negative Predictive Value (NPV) —Describes the probability that a negative test indicates no disease

• Calculation: NPV = True negative / (true negative + false negative) or c/(c + d).
• Values for NPV calculation are found on the second row of a 2 × 2 table (Table 6-3).
• Example: A new allergy test finds that, in patients with the disease, 80 are positive (true positives), whereas 5 without the disease tested positive (false positives). Of those testing negative, 95 do not have the disease (true negatives); but 20 that had the disease tested negative (false negatives). Table 6-4 shows details of the constructed 2 × 2 table in this scenario.

Question 24

What is the formula for absolute risk reduction? Relative risk? Relative risk reduction? Number needed to treat?

Answer 24

ARC = the AR of events in the control group

ART = the AR of events in the treatment group

Absolute risk reduction (ARR) = ARC – ART

Relative risk (RR) = ART/ARC

Relative risk reduction (RRR) = (ARC – ART)/ARC

RRR = 1 – RR

Number
needed to treat (NNT) = 1/ARR

Question 25

What does validity depend on?

Answer 25

Degree of systematic error

Question 26

What is internal validity?

Answer 26

Internal validity is dependent on the amount of error in measurements, including exposure, disease, and the associations between these variables. High internal validity implies a lack of error in measurement and suggests that inferences may be drawn at least as they pertain to the subjects under study.

Question 27

What is external validity?

Answer 27

External validity pertains to the process of generalizing the findings of the study to the population from which the sample was drawn (or, even beyond that population, to a more universal statement). Internal validity is a prerequisite for external validity.

Question 28

What is bias? What flaws can this be due to? What types of bias are there?

Answer 28

Bias is any deviation of results or inferences that results in an erroneous estimate of effect of an exposure on the risk of disease. This can be due to flaws in study design, conduct, or analysis of the study. Examples of bias include selection bias or information bias (e.g., recall bias, surveillance bias, misclassification bias, wish bias).

Question 29

What is precision?

Answer 29

Precision in epidemiologic variables is a measure of random error. Precision is also inversely related to random error, such that a reduction in random error increases precision. Confidence intervals are computed to demonstrate the precision of relative risk estimates. The narrower the confidence interval, the more precise the relative risk estimate.

Question 30

What are the different levels of evidence made by the US preventive services task force?

Answer 30

Level I: Evidence obtained from at least one properly designed randomized controlled trial

Level II-1: Evidence obtained from well-designed controlled trials without randomization

Level II-2: Evidence obtained from well-designed cohort or case-control analytic studies, preferably from more than one center or research group

Level II-3: Evidence obtained from multiple time series with or without the intervention. Dramatic results in uncontrolled trials might also be regarded as this type of evidence

Level III: Opinions of respected authorities, based on clinical experience, descriptive studies, or reports of expert committees

Question 31

What are the USPSTF recommendation levels based on? What are they?

Answer 31

Recommendations for a clinical service are classified by the balance of risk versus the benefit of the service and the level of evidence on which this information is based. The USPSTF uses the following recommendations:

-Level A: Good scientific evidence suggests that the benefits of the clinical service substantially outweigh the potential risks. Clinicians should discuss the service with eligible patients.

Level B: At least fair scientific evidence suggests that the benefits of the clinical intervention outweigh the potential risks. Clinicians should discuss the treatment with eligible patients.

Level C: At least fair scientific evidence suggests that the clinical treatment does provide benefits, but the balance between benefits and risks are too close for making general recommendations. Clinicians need not offer it unless there are individual considerations.

Level D: At least fair scientific evidence suggests that the risks of the clinical intervention outweigh the potential benefits. Clinicians should not routinely offer the treatment to asymptomatic patients.

Level I: Scientific evidence is lacking; it is of poor quality, or conflicting, such that the risk versus the benefit balance cannot be assessed. Clinicians should help patients understand the uncertainty surrounding the clinical treatment.

Question 32

What are the 3 fundamental ethical principles in the Belmont report?

Answer 32

The three fundamental ethical principles for using any human subjects for research are:

• Respect for persons: Protecting the autonomy of all people and treating them with courtesy and respect, and allowing for informed consent
• Beneficence: Maximizing the benefits for the research project while minimizing the risks to the research subjects
• Justice: Ensuring that reasonable, nonexploitative, and well-considered procedures are administered fairly (e.g., the fair distribution of costs and benefits to potential research participants)

Question 33

What are the elements of informed consent?

Answer 33

Elements of informed consent include:

(1) competence;
(2) disclosure;
(3) understanding;
(4) voluntariness; and
(5) consent.

In addition, the patient should have an opportunity to ask questions to elicit a better understanding of the treatment or procedure. This communication process (or any variation thereof) is both an ethical obligation and a legal requirement as spelled out in statutes and case law in all 50 states.

Question 34

What three steps must be clearly articulated when getting consent from a patient?

Answer 34

• Preconditions: These include competence (to understand and decide) and voluntariness (in deciding).
• Information elements: These include disclosure (of risks/benefits), recommendation (plan), and understanding (of information and plan).
• Consent elements: These include authorization (based on patient autonomy).

Question 35

What are limitations of the adverse event reporting system?

Answer 35

• There is no certainty that the reported event was actually due to the product. (The FDA does not require that a causal relationship between a product and event be proven.)
• The FDA does not receive all adverse event reports that occur with a product. Therefore, AERS cannot be used to calculate the incidence of an adverse event in the US population.

Question 36

Are products’ manufacturers required to report adverse events to the FDA?

Answer 36

Health care professionals and consumers may also report these events to the products’ manufacturers. If a manufacturer receives an adverse event report, it is required to send the report to the FDA as specified by regulations.

Question 37

What is mandatory reporting to the AERS required for?

Answer 37

Mandatory reporting is required for the following areas:

• Over-the-counter products and dietary supplements
• Drug, biologic, or human cell tissues, and cellular and tissue-based product manufacturers, distributors, and packers
• Adverse reactions (ARs) relating to human cell and tissue products (HCT/P)