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Flashcards in First Aid, Chapter 6 Research Principles Deck (37)
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1
Q

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

A
  • 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)
2
Q

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

A
  • 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
3
Q

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

A

Cohort and RCT

4
Q

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

A
  • 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
5
Q

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

A
  • 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
6
Q

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

A
  • 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
7
Q

What is a nominal variable? What is an example?

A

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

8
Q

What is an ordinal variable? What is an example?

A

Measures of physical quantities that can be ranked

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

9
Q

What is an interval variable? What is an example?

A

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

10
Q

What is a type 1 error? Give an example.

A

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

11
Q

What is a type 2 error? Give an example.

A

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.

12
Q

What is the statistical power of a study?

A

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%.

13
Q

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

A

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.

14
Q

What is a p-value?

A

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.

15
Q

What is an odds ratio?

A

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.

16
Q

What is relative risk?

A

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.

17
Q

What is prevalence?

A

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).

18
Q

What is incidence?

A

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

19
Q

What is sensitivity? What is the calculation?

A

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)
20
Q

What is the specificity of a test?

A

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)
21
Q

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

A

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).

22
Q

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

A

Type I error

23
Q

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

A

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.
24
Q

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

A

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

25
Q

What does validity depend on?

A

Degree of systematic error

26
Q

What is internal validity?

A

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.

27
Q

What is external validity?

A

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.

28
Q

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

A

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).

29
Q

What is precision?

A

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.

30
Q

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

A

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

31
Q

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

A

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.

32
Q

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

A

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)
33
Q

What are the elements of informed consent?

A

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.

34
Q

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

A
  • 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).
35
Q

What are limitations of the adverse event reporting system?

A
  • 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.
36
Q

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

A

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.

37
Q

What is mandatory reporting to the AERS required for?

A

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)

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