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Comprehensive Review > Epi - Comp Exam > Flashcards

Epi - Comp Exam Flashcards

1
Q

What is Descriptive Epidemiology?

A

Distribution of Disease

Who/When/Where

  • Frequencies of disease occurrences.
    • Counts and their relation to size of population.
  • Patterns of disease occurances:
    • Encompasses person place and time.
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2
Q

What is Analytic Epidemiology?

A

Determinants of Disease

Associations vs. Causes (Causation)

Why/How

  • Factors of susceptibility/exposure/risk
  • Etiology/causes of disease
  • Mode(s) of transmission
  • Social/environmental/biologic elements that determine the ocurrence/presence of disease.
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3
Q

What are the 6 core functions of epidemiology?

A
  1. Public health surveillance
  2. Field investigation
  3. Analytic studies
  4. Evaluation
  5. Linkages
  6. Policy development
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4
Q

What is an epidemic?

A

Occurance of disease clearly in excess of normal expectancy with community/period clearly defined.

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5
Q

What is an outbreak?

A

An epidemic limited to a localized increase in the occurance of disease. Sometimes interchanged with ‘cluster.’

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6
Q

What is an endemic?

A

The constant presence of a disease within a given area or population in excess of normal levels in other areas.

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7
Q

What is an emergency of international concern?

A

An epidemic that alerts the world to the need for high vigilance (pre-pandemic labeling).

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8
Q

What is a pandemic?

A

An epidemic spread world-wide (global health impact), could be multi-national or multi-continent.

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9
Q

What is Incidence Density?

A

Incidence Rate when summed over multiple time periods.

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10
Q

What is Point Prevalence?

A

Prevalence at a given point in time.

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11
Q

What is Period Prevalence?

A

Prevalence over a given period of time.

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12
Q

What is a Crude Morbidity Rate?

A

of Persons with Disease / # of Persons in Population

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13
Q

What is a Crude Mortality Rate?

A

of Deaths (all causes) / # of Persons in Population

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14
Q

What is a Cause-Specific Morbidity Rate?

A

of Persons with cause-specific disease / # of Persons in Population

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15
Q

What is a Cause-Specific Mortality Rate?

A

of Cause-Specific Deaths / # of Persons in Population

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16
Q

What is a Case-Fatality Rate?

A

of Cause-Specific Deaths / # of Cases of Disease

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17
Q

What is a Cause-Specific Survival Rate?

A

of Cause-Specific Cases Alive / # of Cases of Disease

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18
Q

What is a Proportional Mortality Rate (PMR)?

A

of Cause-Specific Deaths / total # of Deaths in Population

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19
Q

What is a Live Birth-Rate?

A

of Live Births / 1000 Population

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20
Q

What is a Fertility Rate

A

of Live Births / 1000 women of childbearing age (15-44)

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21
Q

What is a Neonatal Mortality Rate?

A

of deaths in those <28 days of age / 1000 live births

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22
Q

What is a Postnatal Mortality Rate?

A

of deaths in those between 28 days and 1 year of age / 1000 live births

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23
Q

What is an Infant Mortality Rate?

A

of deaths in those < 1 year of age / 1000 live births

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24
Q

What is a Maternal Mortality Ratio?

A

of female deaths related to pregnancy / 100,000 live births

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25
Q

What is Infectivity?

A

Infectivity is the ability to invade a patient (host) .

infected / # susceptable (at risk)

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26
Q

What is Pathogenicity?

A

Pathogenicity is the ability to cause clinical disease.

with disease / # infected

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27
Q

What is Virulence?

A

Virulence is the ability to cause death.

of deaths / # with infectious disease

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28
Q

What is Risk?

A

Risk

  • A proportion that calculates the probability of outcome.
  • Example:
    • Risk of Outcome in the ‘Exposed’ is:
      • “exposed with disease” / “all exposed”
    • Risk of Outcome in the “Non-exposed” is:
      • “non-exposed with disease” / “all non-exposed”
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29
Q

What is Absolute Risk Reduction (ARR)?

A

Absolute Risk Reduction (ARR), aka Attributable Risk, is the risk difference of the outcome attributable to exposure difference between groups.

Example:

If Risk is 40.9% in the treatment group and 53.6% in non-treatment group then:

ARR = |40.9% - 53.6%| = 12.7%

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30
Q

What is Relative Risk Reduction?

A

The Relative Risk Reduction is the ARR compared with the Risk of the exposed.

Example:

If Risk is 40.9% in the treatment group and 53.6% in non-treatment group then:

ARR = |40.9% - 53.6%| = 12.7%

RRR = 12.7% / 53.6% = 23.7%

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31
Q

What is the Number Needed to Treat (NNT) / Number Needed to Harm (NNH)?

A

The NNT is the number of patients needed to be treated to receive the stated benefit/harm. NNT = 1 / ARR (in decimal format) with answer always being rounded to nearest whole number.

Example:

If you have a certain medication that has shown an absolute risk reduction of 12.7% then:

NNT = 1 / 0.127 = 8 patients.

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32
Q

How are Ratios interpreted?

A

All ratios (RR, OR, HR) compare 2 groups and describe the liklihood of event/outcome in 1 group compared to another.

Ratio values:

  • If Ratio is 1.0 then the event/outcome is equally likely for both groups.
  • If Ratio is >1.0 then the event/outcome is more likely to occur in comparison group.
  • If Ratio is <1.0 then the event/outcome is less likely to occur in comparison group.
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33
Q

How are Ratios reported?

A
  • =1.0 - no difference
  • >1.0 - Increased Ratio (RR/OR/HR)
    • 1.01 - 1.99 = use decimal value (converted to %)
      • RR = 1.53, then comparator group is at a 53% increased risk.
    • >1.99 = use phrase “x” times greater for interpretation
      • OR = 6.18, then comparator group has 6.18 times greater odds
  • <1.0 - Decreased Ratio (RR/OR/HR)
    • 0.0001 - 0.99 = subtract from 1.0, then convert to %
      • HR = 0.73, then comparator group has a 27% decreased probability of the hazard outcome.
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34
Q

How are Confidence Intervals for Ratios interpreted?

A

If both values of the CI for a Ratio is on the same side of 1.0, it is always statistically significant.

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35
Q

How is the Odds Ratio interpretted?

A

Odds Ratio is interpretted just like any ratio.

Example: OR = 10.09

_____ exposed are 10 x more likely to _____ than _____ not exposed.

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36
Q

What are the 3 required elements in interpretting Odds Ratios?

A

3 Required Elements:

  • The comparison group
  • Percentage/times more/less likely
  • Compared to reference group.
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37
Q

If there is a lack of apparent exchangeability/comparability, what type of bias can that cause?

A

Confounding

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38
Q

Before declaring a real or true association, what 3 aspects do epidemiologists evaluate and what kind of validity are they evaluating?

A

Internal Validity

  • 3 aspects of internal validity:
    • Confounding or Effect Modificaiton
    • Bias
    • Statistical significance.
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39
Q

How do you test for confounding?

A

Testing for Confounding

  1. Calculate CRUDE (unadjusted) measure of association (OR/RR) between exposure and outcome.
  2. Calculate outcome measure of association (OR/RR) between exposure and outcome for each individual strata (levels, groupings, or categories) of the potential confounder.
    • Create a weighted-average of all strata (if near-equal), commonly called adjusted association.
      • Authors must indicate what variables are utilized in ‘adjusting’ the measure of association.
  3. Compare the Crude vs. Adjusted measures of association between exposure and outcome.
    • If they vary by 15% or more, then confounding is present.
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40
Q

What are the two main impacts of confounding?

A

Impact of Confounding

  • Magnitude (strength) of association
    • If association is more extreme (ratio goes up), less extreme (ratio goes down) compared to unadjusted association.
  • Direction of association
    • Can produce an association in an opposite direction, towards or away from a null (equal) association.
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41
Q

What are ways of controlling confounding?

A

Controlling Confounding

  • Study design phase:
    • Randomization
    • Restriction
    • Matching
  • Analysis of data stage:
    • Stratification (w/ weighting)
    • Multivariate statistical analysis (regression analyses)
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42
Q

How does randomization help control for confounding and what are its strengths and weaknesses?

A

Controlling for Confounding - Randomization

  • Randomization hopefully allocates an equal number of subjects with the known (and unassessed) confounders into each intervention group.
  • Strength:
    • With sufficent sample size (N), randomization will likely be successful in making groups equal.
    • Stratified version more precisely assures equal-ness.
  • Weakness:
    • Sample size (N) may not be large enough to control for all unknown or unassessed confounders.
    • Process doesn’t guarantee successful, equal allocation between all intervention groups for all known and unknown confounders.
    • Practical only for Interventional studies.
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43
Q

How does restriction help control confounding and what is its strengths and weaknesses?

A

Controlling for Confounding - Restriction

  • Study participation is restricted to only subjects who do not fall within pre-specified category(ies) of the confounder.
  • Strength:
    • Straight forward, convenient and inexpensive.
    • Does not negatively impact internal validity.
  • Weakness:
    • Sufficiently narrow restriction criteria may negatively impact ability to enroll subjects (reduced sample size (N))
    • If restriction criteria is not sufficiently narrow it will allow the introduction of residential (other) confounding effects.
    • Eliminates researchers ability to evaluate varying levels of the factor being excluded.
    • Can negatively impact external validity (generalizability).
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44
Q

How does matching help control for confounding and what is its strengths and weaknesses?

A

Controlling for Confounding - Matching

  • Study subjects selected in matched-pairs related to the confounding variable, to equally distribute confounder among each study group.
  • Strength:
    • Intuitive, some feel it gives greater analytic efficiency.
  • Weakness:
    • Difficult to accomplish, can be time consuming, and potentially expensive.
    • Doesn’t control for any confounders other than those matched.
      • Over-matching possible; this will mask (blunt) findings.
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45
Q

How does stratification help control for confounding and what are its strengths and weaknesses?

A

Controlling for Confounding - Stratification

  • Descriptive and statistical analysis of data evaluating association between exposure and outcome within the various strata (categories/levels) within the confounding variable(s).
  • Strength:
    • Intuitive (to some), straight-forward and enhances understanding of data.
  • Weakness:
    • Impractical for simultaneous control of multiple confounders, especially those with multiple strata (categories) within each variable being controlled.
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46
Q

How does multi-variate analysis help control for confounding and what are its strengths and weaknesses?

A

Controlling for Confounding - Multi-Variate Analysis

  • Statistical analysis of data by mathematically factoring out the effects of the confounding variable(s).
  • Strength:
    • Can simultaneously control for multiple confounding variables.
    • In statistical regressions, ORs can be obtained and interpreted.
  • Weakness:
    • Process requires individuals (researchers/readers) to clearly understand (interpret) the data (results).
    • Can be time consuming for researcher/biostatistician.
  • Examples:
    • Regressions (linear and logistic versions)
    • Cox Proportional Hazards
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47
Q

What is effect modification?

A

Effect Modification

  • A 3rd variable, that when present, modifies the magnitude of effect of a true association by varying it within different strata of the 3rd variable.
    • If an interaction is present, the researcher must report the measures of association for each strata individually.
    • Unlike confounding, an effect modifying variable should be described and reported at each level of the variable, rather than controlled, or adjusted, for.
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48
Q

How do you test for effect modification?

A

Testing for Effect Modification

  1. Calculate crude measure of association between exposure and outcome (OR/RR)
  2. Calculate strata-specific measures of association between exposure and outcome (OR/RR) for each strata of 3rd variable.
  3. Compare each of the strata-specific measures of associations between each other [while referencing the adjusted measure of association].
    • The measure of association between the lowest and highest strata of the effect-modifying variable will be 15% or more different if effect modification is present.
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49
Q

What 3 aspects of a study must a researcher evaluate before declaring a real, true association?

A
  • Confounding or effect modification
  • Bias
  • Statistical significance
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50
Q

What is bias?

A

Bias - Systematic (non-randon) error in study design or conduct leading to erroneous results.

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51
Q

What does bias distort?

A

Bias distorts the relationship (association) between exposure and outcome.

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52
Q

What can be done to “fix” bias once it has already occured?

A

Nothing

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53
Q

What can minimize bias and its impact?

A

Prospective (pre-study) consideration and adjustment can minimize bias and its impact.

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54
Q

What 3 elements can bias impact?

A

Bias can impact:

  • Source/type
  • Magnitude/strength
  • Direction
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55
Q

Considering bias impact on “Source/Type,” what are the 2 main categories of bias.

A

2 Main Categories of Bias

  • Selection-related
  • Measurement-related
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56
Q

What is selection-related bias?

A

Selection-Related Bias

  • Any aspect in the way the researcher selects or acquires study subjects which creates a systematic difference between groups.
    • Commonly seen when comparative groups not coming from the same population/group or not being representative of the full population or even differentially-selected (processes)

DON’T DO ANYTHING THAT IS DIFFERENT, OR CREATES A DIFFERENCE, BETWEEN GROUPS!!!

SAME-SAME-SAME

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57
Q

What is measurement-related bias?

A

Measurement-Related Bias

  • Any aspect in the way the researcher collects information, or measures/observes subjects which creates a systematic difference between groups.
    • Errors in measurement can also cause a resultant error in patient classification (misclassification)

DON’T DO ANYTHING THAT IS DIFFERENT, OR CREATES A DIFFERENCE, BETWEEN GROUPS!!!

SAME-SAME-SAME

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58
Q

What are the 2 types of selection bias?

A

Selection Bias

  • Healthy-Worker Bias
  • Self-Selection/Participant (Responder) Bias
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59
Q

Under measurement bias, what are the 5 subject-related types of bias?

A

Measurement Bias - Subject-Related

  • Recall bias
  • Hawthorne Effect
  • Contamination bias
  • Compliance/Adherence bias
  • Lost to follow-up bias
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60
Q

What is recall bias?

A

Recall Bias

  • A differential level of accuracy/detail in provided information between study groups.
    • Exposed or diseased subjects may have a greater sensitivity for recalling their history (better memory; easier to remember if more severe) or amplify (exaggerate) their responses.
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61
Q

What is the Hawthorne Effect?

A

Hawthorne Effect

  • Individuals alter/modify their behavior because they are part of a study and know they are under observation.
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62
Q

What is misclassification bias?

A

Misclassification Bias - error in classifying the disease, exposure status, or both.

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63
Q

What are the two types of misclassification bias?

A

Misclassification - 2 Types

  • Non-differential
  • Differential
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64
Q

What is non-differential misclassification bias?

A

Non-Differential Misclassification

  • Error is distributed equally in both groups.
    • Misclassification of exposure or disease that is unrelated to the other (disease or exposure), depending on study design.
    • Effect: For dichotomous (2 cat.) variables, bias can move the measure of association (RR/OR) towards 1.0; it attenuates your effect estimates of association.
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65
Q

What is differential misclassification bias?

A

Differential Misclassification

  • Error in one group is different than other.
    • Misclassification of exposure or disease is RELATED to the other (disease or exposure), depending on study design.
    • Effect: Bias can move the measure of association (RR/OR) in either direction in relation to 1.0; it can inflate (away from 1.0) or attenuate (towards 1.0) your effect estimates of association.
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66
Q

What methods can be used to control for bias?

A

Controlling for Bias

  • Select the most precise, acurate, & medically-appropriate measures of assessment and evaluation/observation.
  • Blinding/masking
  • Use multiple sources to gather all information
  • Randomly allocate observers/interviewers for data collection (and train them!; use technology!)
  • Build as many methods necessary to minimize loss to follow-up.
    • Lost-to-followup bias (differential attrition bias)
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67
Q

What are the 3 types of associations (relationships).

A

Associations (Relationships) - Types

  1. Artifactual (false) associations.
  2. Non-causal associations.
  3. Causal associations.
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68
Q

What can cause artifactual associations?

A

Artifactual Associations

  • Can arise from Bias and/or Confounding.
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69
Q

How can non-causal associations occur?

A

Non-Causal Associations

  • Can occur in 2 different ways:
    • The disease may cause the exposure (rather than the exposure causing the disease)
    • The disease and the exposure are both associated with a third factor (confounding).
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70
Q

What are the 3 types of causal relationships?

A

Causal Relationships - 3 Types

  • Sufficient Cause
  • Necessary Cause
  • Component Cause
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71
Q

What are Hill’s Guidelines?

A

Hill’s Guidelines

  • Inductively-oriented criteria for epi causal inference process.
  • Hill disagreed that “hard-fast” rules of evidence could be generated by which to judge likelihood of causation.
  • Criteria:
    • Strength
    • Consistency
    • Temporality
    • Biologic Gradient
    • Plausibility
  • The higher the number of criteria met, when evaluating an ‘association,’ the more likely it may be causal.
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72
Q

In Hill’s guidelines, what does strength refer to?

A

Strength

  • Strength refers to the size of the measure of association (RR/OR/HR)
    • The greater the association the more convincing it is that the association might actually be causal.
  • “A strong association is neither necessary nor sufficient for causality and weakness of an association is neither necessary nor sufficient for absence of casuality.”
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73
Q

In Hill’s guidelines, what does consistency refer to?

A

Consistency

  • The repeated observations of an association in different populations under different circumstances in different studies (not just once).
  • Consistency may still obscure the truth.
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74
Q

In Hill’s guidelines, what does temporality refer to?

A

Temporality

  • Reflects that the cause precede the effect/outcome in time.
  • Time-order also describable:
    • Proximate cause (short-term interval)
    • Distant cause (long-term interval)
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75
Q

In Hill’s guidelines, what does biologic gradient refer to?

A

Biologic Gradient

  • Presence of a gradient of risk (dose-response) associated with the degree of exposure.
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76
Q

In Hill’s guidelines, what does plausibility refer to?

A

Plausibility

  • Presence of a biological feasibility to the association, which can be understood and explained (biologically, physiologically/medically)
    • Is the event/exposure biologically-plausible, if really true?
  • At issue: Plausibility decision on criterion-based from existing/known beliefs, which may be flawed or incomplete.
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77
Q

Determining incubation periods.

A
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78
Q

What is incidence and how is it calculated?

A

Incidence (a.k.a. Risk or Attack Rate) is new cases of disease divided by the “at risk” or base population. (Proportion)

Useful for non-dynamic populations.

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79
Q

What is prevalence and how is it calculated?

A

Existing cases of disease + new cases of disease divided by the “at risk” or base population. (Proportion)

  • Time frames for numerator/denominator must be the same.
  • The denominator includes those already with the disease and those at risk of getting the disease.
  • May also be represented as:
    • Point Prevalence
    • Period Prevalence
80
Q

What is Incidence Rate and how is it calculated?

A

Incidence Rate is the proportion of new cases over the person-time at risk for the disease (or in pop).

81
Q

What is Risk Ratio (a.k.a. Relative Risk)?

A

Risk Ratio or Relative Risk is the ratio of the Risks from 2 different groups.

82
Q

What is an Odds Ratio?

A

Odds Ratio - Ratio of the odds from 2 different groups.

Example:

Odds of Exposure in Diseased / Odds of Exposure in Non-Diseased

or

(A/C) / (B/D)

*Can cross multiply in 2x2 table to get odds ratio. (AD/BC)

83
Q

What is confounding?

A

Confounding

  • A 3rd variable (characteristic related to study subjects) that distorts an association (RR/OR/HR) between the exposure and outcome.
    • An alternative explanation of the association
84
Q

To be a confounder, what three requirements must be met?

A

3 Requirements of Confounders

  1. Independently associated with the exposure.
  2. Independently associated with the outcome.
  3. Not directly in the causal-pathway linking exposure to outcome.
85
Q

What does a quantitative study design use to represent data?

A

Numbers

86
Q

What does a qualitative study design use to represent data?

A

Words

87
Q

What are the 2 types of quantitative studies?

A

Quantitative Study Designs

  • Interventional
  • Observational
88
Q

What are interventional studies?

A

Interventional Study Designs

  • Considered “experimental”
  • Investigator selects interventions (exposure).
  • Researcher-forced group allocation.
    • Commonly done by randomization.
  • Can demostrate causation.
  • AKA: clinical trial, clinical study, experimental study, human study, investigational study.
89
Q

What are observational studies?

A

Observational Study Designs

  • Considered “natural”
  • Researchers “observe” subject-elements occuring naturally or selected by individual.
  • Useful for unethical study designs using forced interventions.
  • Most observational study designs not able to prove causation.
  • No researcher-forced group allocation.
90
Q

In interventional studies, what are the general differentiators between the phases?

A

Interventional Studies - General Phase Differentiators

  1. Purpose/focus
  2. Population studied (healthy/diseased).
  3. Sample Size
  4. Duration
91
Q

What is study design selection based on?

A

Study Design - Selection

Based on:

  • Perspective of hypothesis.
  • Ability/desire of researcher to force group allocation (randomization).
  • Ethics of methodology.
  • Efficiency & practicality.
  • Costs
  • Validity of acquired information (Internal Validity).
  • Applicability of acquired information to non-study patients (External Validity - Generalizability).
92
Q

What is study population selection based on?

A

Study Population - Selection

  • Research hypothesis.
  • Population of interest.
  • Criteria:
    • Desired vs. logical vs. plausible selection criteria:
      • Inclusion & exclusion.
      • Case & control group.
      • Exposed & non-exposed group.
  • Ethics - Principles of bioethics must be met.
  • Equipoise - genuine confidence that an intervention may be worthwhile (risk vs. benefit) in order to use it in humans.
93
Q

What is the null hypothesis?

A

Research Hypothesis - Null Hypothesis

  • H0
  • A research perspective that states there will be NO (true) difference between the groups compared.
    • Most conservative and commonly utilized.
  • Researchers either reject or don’t reject this perspective.
94
Q

What are the 3 statistical-perspectives that can be taken by a researcher?

A

Research Hypothesis - Statistical-Perspectives

  • Superiority
  • Noninferiority
  • Equivalence
95
Q

What is an alternative hypothesis?

A

Research Hypothesis - Alternative Hypothesis

  • H1
  • A research perspective which states there will be a (true) difference between the groups compared.
96
Q

What are the 2 types of error in inaccurately accepting or rejecting the null hypothesis?

A

Research Hypothesis - Error

  • Type I - False positive.
  • Type II - False negative.
98
Q

What are examples of probability sampling schemes?

A

Sampling Schemes - Probability

  • Simple Random
  • Systemic Random
  • Stratified Simple
  • Stratified Disproportionate
  • Multi-Stage Random
  • Cluster Multi-Stage
99
Q

Describe simple random sampling.

A

Sampling Schemes - Simple Random

  • Assign random numbers, then take randomly-selected numbers to get desired sample size, OR
  • Assign random numbers, then sequentially-list numbers and take desired sample size from top (or bottom) of listed numbers.
100
Q

Describe systematic random sampling.

A

Sampling Schemes - Systematic Random

  • Assign random numbers, then randomly sort these random numbers, then select highest (or lowest) number, then systematically, by a pre-determined sampling-interval take every nth numbers to get desired sample size.
101
Q

Describe stratified simple random sampling.

A

Sampling Schemes - Stratified Simple

  • Stratify sampling frame by desired characteristics (e.g., gender), then use simple random sampling to select desired sample size.
  • Example:
    • Population has 40 females and 60 males, random sampling may not give a sample that is 40% F/60% M.
    • Could separate population into strata of females and males, and for each round of sampling select 4 females and 6 males. This gives 4:6 ratio.
    • Keeps sample proportionate to population.
102
Q

Describe multi-stage random sampling.

A

Sampling Schemes - Multi-Stage Random

  • Uses simple random sampling at multiple-stages towards patient selection.
    • Regions/counties (primary sampling unit; PSU).
    • City blocks/zip codes (secondary sampling unit; SSU).
    • Clinic/hospital/household
    • Individual/occurrence
103
Q

Describe cluster multi-stage random sampling.

A

Sampling Schemes - Cluster Multi-Stage

  • Same as multi-stage random sampling but ALL ‘elements’ clustered together (at any stage) are selected for inclusion.
    • ALL clinics in zip code.
    • ALL housholds in community.
104
Q

Describe stratified disproportionate random sampling.

A

Sampling Schemes - Stratified Disproportionate

  • Disproportionately utilizes stratified simple random sampling when baseline population is not at the desired proportional percentages to the referent population.
  • Stratified sample ‘weighted’ to return sample population back to baseline population.
    • Useful for ‘over-sampling’
107
Q

What are non-probability sampling schemes?

A

Sampling Schemes - Non-Probability

  • Quasi-systematic or convenience samples (not really, completely, random or fully probabilistic).
    • Decide on what fraction of population is to be sampled and how:
    • Examples:
      • All persons whose last name begins with “M-Z.”
      • All members of a professional business association.
      • All persons attending clinic every M/W/F for 6 months.
      • All persons referred by selected-peers.
  • CONCERN: There is some known or unknown order to the sample generated by the selected scheme which may introduce bias (selection bias).
108
Q

What are the 4 key principles of bioethics?

A

4 Key Principles of Bioethics

  • Autonomy
  • Beneficence
  • Justice
  • Nonmaleficence
109
Q

Describe the bioethical principle of autonomy.

A

Principles of Bioethics - Autonomy

  • Self-rule / self-determination.
    • Participants decide for themselves without outside influences (i.e., coercion, reprisal financial manipulation.
  • Have full and complete understanding of risks and benefits.
    • No misinformation, incomplete information, or ineffectively-conveyed information (language or ed level).
110
Q

Describe the bioethical principle of beneficence.

A

Principles of Bioethics - Beneficence

  • To benefit, or do good for, the patient (not society).
111
Q

Describe the bioethical principle of justice.

A

Principles of Bioethics - Justice

  • Equal & fair treatment regardless of patient characteristics.
112
Q

Describe the bioethical principle of nonmaleficence.

A

Principles of Bioethics - Nonmaleficence

  • Do no harm.
  • Researchers must not:
    • Withhold info.
    • Provide false info.
    • Exhibit professional incompetence.
113
Q

What are the levels of IRB review?

A

IRB - Levels and Differences

  • Full Board - used for all interventional trials with more than minimal risk to patients.
  • Expedited - minimal risk and/or no patient identifiers.
  • Exempt - no patient identifiers, low/no risk, de-identified dataset analysis, environmental studies, use of existing data/specimens (de-identified).
114
Q

Who decides the level of IRB review, and what are the main differences between the levels?

A

IRB - Determining Level

  • Higher level requires more members, time, and level of detail, for committee review/approval.
  • Level determined by
115
Q

What is the Data Safety & Monitoring Board (DSMB)?

A

Data Safety & Monitoring Board

  • Semi-independent committee not involved with the conduct of study but charged with reviewing study data as study progresses.
    • Done to assess for undue risk/benefit between groups.
    • Pre-determined review periods.
    • Can stop study early, for either overly-positive or overly-negative findings in one or more groups (compared to others).
116
Q

Describe Phase 0 in interventional studies.

A

Interventional Studies - Phase 0

Exploratory; Investigational New Drug

  • Exploratory; Investigational New Drug
    1. Assess drug-target actions and possibly pharmacokinetics in single or ‘a few’ doses (first in human use).
    2. Healthy (or diseased patients (oncology)) volunteers.
    3. Very small N (e.g., <20).
    4. Very short duration (e.g., single dose to just a few days).
117
Q

Describe Phase 1 of interventional studies.

A

Interventional Studies - Phase 1

Investigational New Drug

  1. Assess safety/tolerance and pharmacokinetics of one or more dosages (first-in-human / early-in-human use).
  2. Healthy or disease volunteers (depends on disease).
  3. Small N (e.g., 20-80).
  4. Short duration (e.g., just a few weeks)
118
Q

Describe Phase 2 of interventional studies.

A

Interventional Studies - Phase 2

Investigational New Drug

  1. Assess effectiveness (continue to assess safety/tolerability; expands on Phase 1 purpose)
  2. Diseased volunteers (may have narrow inclusion criteria for isolation of effects).
  3. Larger N (e.g., 100-300)
  4. Shorter-to-Medium duration (e.g., a few weeks to a few months).
119
Q

Describe Phase 3 of interventional studies.

A

Interventional Studies - Phase 3

Investigational New Drug; Indication/Population

  1. Assess effectiveness (continues to assess safety/tolerability)
  2. Diseased volunteers:
    • May expand inclusion criteria and comparison group(s) for delineation of effects
    • Various statistical-perspectives can be taken in studies:
      • Superiority
      • Noninferiority
      • Equivalency
  3. Larger N (e.g., 500-3000)
  4. Longer duration (e.g., a few months to a year+).
120
Q

Describe Phase 4 of interventional studies.

A

Interventional Studies - Phase 4

Post FDA-Approval

  1. Assess long-term safety, effectiveness, optimal use (risk/benefits)
  2. Diseased volunteers
    • Expand use criteria (comorbidities/concomitant meds.) for delineation of long-term safety/effects.
  3. Population N (e.g., a few-hundred to a few-hundred-thousand)
  4. Wide-range of durations (e.g., a few weeks to several years; ongoing)
    • Interventional or observational designs.
    • Registries/surveys also used in observational studies.
121
Q

What are the advantages and disadvantages of interventional trials?

A

Interventional Trials - Advantages & Disadvantages

  • Advantages:
    • Can demonstrate causation (cause precedes effect).
    • Only designs used by the FDA for “approval” process.
  • Disadvantages:
    • Cost
    • Complexity/time (development, approval, conduct)
    • Ethical considerations (risk v. benefit eval)
    • Generalizability (external validity)
122
Q

What are some examples of paitent-oriented endpoints (POEM’s)?

A

Patient-Oriented Endpoints

  • Death
  • Stroke or MI
  • Hospitalization
  • Preventing need for dialysis
123
Q

What are some examples of disease-oriented endpoints (DOE’s)?

A

Disease-Oriented Endpoints (DOE’s)

  • DOE’s; surrogate markers (elements used in place of evaluation patient-oriented (direct) endpoints)
    • Blood pressure (for risk of stroke)
    • Cholesterol (for risk of heart attack)
    • Change in SCr (for worsening renal function)
124
Q

What is group allocation and the 2 types of group allocation?

A

Interventional Study Design - Group Allocation

  • Group allocation is similar to sample selection, however, it is the allocation of that sample into groups.
  • Types:
    • Non-random
    • Random
125
Q

Describe non-random group allocation.

A

Group Allocation - Non-Random

  • Subjects don’t have an equal probability of being selected or assigned to each intervention group (e.g., convenience sampling/non-probabilistic allocation.
    • Patients attending morning clinic assigned to group 1, patients attending afternoon clinic assigned to group 2.
    • Patients attending clinic on even days assigned to group 1, patients attending clinic on even days assigned to group 2.
    • The first 100 patients admitted to hospital.
126
Q

Describe random group allocation and its purpose.

A

Group Allocation - Random

  • Subject do have an equal probability of being assigned to each intervention group.
    • Random-number generating programs.
  • Purpose: to make groups as equal as possible; based on known and unknown important factors (confounders).
    • Attempts to reduce systematic differences (bias) between groups which could impact results/outcomes.
    • Equality of groups not guaranteed.
    • Documentation of equality of groups (effectiveness of randomization process) reported in 1-of-several locales:
      • p values shown in table format.
      • p values not shown but text statement given in key of table.
      • p values not shown but text-statement given in article.
127
Q

What are the forms of randomization?

A

Interventional Study Designs - Forms of Randomization

  • Simple - equal probability for allocation within one of the study groups.
  • Blocked - ensures balance within each intervention group.
    • When researchers want to assure that all groups are equal in size.
  • Stratified - ensures balance with known confounding variable.
    • Examples: gender, age, disease severity/duration, comorbidities.
    • Can also pre-select levels to be balanced within each interfering factor (confounder).
128
Q

What are case-control studies?

A

Case-Control Studies

  • Observational studies.
  • Allows researcher to be a passive observer of natural events occuring in individuals with the disease/condition of interest (cases) who are compared with people who do not have the condition of interest (controls).
    • Controls supply info about expected baseline risk profile in population from which cases are drawn.
  • Group assigned by disease status.
  • Useful when studying a rare disease or investigating an outbreak.
  • Commonly generates:
    • Odds of exposure.
    • Odds ratio (OR).
129
Q

What are the reasons a case-control study design may be selected?

A

Case-Control Study Design - Reasons for Selection

  • Unable to force group allocation (unethical, not feasible).
  • Limited resources (time, money, subjects).
  • Disease of interest is rare in occurence and little is known about its associations/causes.
    • Directly assesses the perspective of the hypothesis.
  • Prospective exposure data is difficult/expensive to obtain and/or very time inappropriate.
  • Customarily conducted in a retrospective fashion.
130
Q

What are the strengths of a case-control study?

A

Case-Control Studies - Strengths

  • Good for assessing multiple exposures of one outcome.
  • Useful when diseases are rare.
  • Useful when determining associations (not causation).
  • Less expensive and time consuming than interventional trials and prospective cohort studies.
  • Useful when ethical issues limit interventional studies.
  • Useful when disease has a long induction/latent period.
131
Q

What are the weaknesses of case-control studies?

A

Case-Control Studies - Weaknesses

  • Can’t demonstrate causation.
  • Can be impacted by unassessed confounders.
  • Retrospective; can’t control for other exposures or potential changes in amount of study-exposure during study frame.
  • Can be impacted by various biases, most importantly selection & recall/assessment biases.
  • Limited by available data (retrospective nature of design).
132
Q

Where can the control group come from?

A

Case-Control Studies - Control Groups

  • Population (state/community/neighborhood).
    • Can be obtained via numerous avenues, even randomly.
  • Institutional/Organizational/Provider
    • Illness(es) of controls should be unrelated to exposure(s) being studied.
  • Spouse/Relatives/Friends
    • Genetic, environmental, socio-economic, etc… similarities.
133
Q

What is a cohort study?

A

Cohort Study Design

  • Observational studies allowing researcher to be a passive observer of natural events occuring in naturally-exposed and unexposed (comparison) groups.
  • Group-allocation based on:
    • Exposure-status OR
    • Group membership (something in common).
  • Useful when studying a rare exposure
  • Also called incidence studies/longitudinal studies.
134
Q

How are cases selected for case-control studies?

A

Case-Control Studies - Case Selection

  • Defined by the investigator (hopefully) using accurate, medically-reliable, efficient data sources.
    • Applied to all study participants:
      • Objectively, consistently, accurately, and with validity.
      • Clinically-supportable/definable criteria are best.
        • From published, professionally-recognized and accepted diagnostic criteria and/or from multiple sources of data.
  • Classifying patients correctly is ideal, but present is the risk of ‘misclassifying’ subjects (into wrong group)
    • Misclassification:
      • Differential
      • Non-differential
135
Q

What is non-differential misclassification bias?

A

Non-Differential Misclassification

  • Error is distributed equally in both groups.
    • Misclassification of exposure or disease that is unrelated to the other (disease or exposure), depending on study design.
    • Effect: For dichotomous (2 cat.) variables, bias can move the measure of association (RR/OR) towards 1.0; it attenuates your effect estimates of association.
136
Q

What is differential misclassification bias?

A

Differential Misclassification

  • Error in one group is different than other.
    • Misclassification of exposure or disease is RELATED to the other (disease or exposure), depending on study design.
    • Effect: Bias can move the measure of association (RR/OR) in either direction in relation to 1.0; it can inflate (away from 1.0) or attenuate (towards 1.0) your effect estimates of association.
139
Q

Why would the cohort study design be selected?

A

Cohort Study Design - Reason for Selection

  • Unable to force group allocation (‘randomize’).
    • Unethical / Not feasible
  • Limited resources.
  • Exposure of interest is rare in occurence and little is known about its association/outcomes.
  • More interested in incidence rates or risks for outcome of interest than effects of interventions.
140
Q

What are the strengths of cohort studies?

A

Cohort Studies - Strengths

  • Good for assessing multiple outcomes of 1 exposure (hard to control for other exposures if more than one potentially associated with outcome).
  • Useful when exposures are rare.
  • Useful in calculating risk and RR.
  • Less expensive than interventional trials.
  • Good when ethincal issues limit use of interventional study.
  • Good for long induction/latent periods (retrospective).
  • Able to represent “temporality” (Prospective).
141
Q

What does sensitivity mean?

A

Medical Screening - Sensitivity

  • How well a test can detect presence of disease when in fact disease is present.
  • Proportion of time that a test returns a true positive.
    • Highly sensitive = low rate of false negative.
  • Sensitivity =
    • TP/(TP+FN) * 100%
    • TP/(All Diseased) * 100%
142
Q

What does specificity mean?

A

Medical Screening - Specificity

  • How well a test can detect absence of disease when in fact the disease is absent.
  • Proportion of time a test returns true negatives.
  • Specificity =
    • TN/(TN+FP) * 100%
    • TN/(All Without Disease) * 100%
143
Q

What are the weaknesses of cohort studies?

A

Cohort Studies - Weaknesses

  • Can’t demonstrate causation (well-controlled prospective more likely to approximate causation).
  • Hard to control for other exposures if more than one plausible for being associated with an outcome (primarily retrospective).
  • Retrospective - can’t control for other exposures (if not known/assessed) or potential changes in amount of study-exposure during study frame.
  • Not good for long induction/latent periods (retrospective much better here).
  • Can be impacted by unassessed confounders (more so with retrospective).
  • Can be impacted by various biases, most importantly selection & recall / assessment biases (retrospective).
  • Limited by available data (retrospective; much less for prospective).
144
Q

What does positive predictive value mean?

A

Medical Screening - Positive Predictive Value

  • How accurately a positive test predicts the presence of disease.
  • Proportion of TPs in patients with positive test.
  • PPV =
    • TP/(TP+FP) * 100%
    • TP/(All Positive Tests) * 100%
145
Q

What does negative predictive value mean?

A

Medical Screening - Negative Predictive Value

  • How accurately a negative test predicts the absence of disease.
  • Proportion of TNs in patients with negative tests.
  • NPV =
    • TN/(TN+FN) * 100%
    • TN/(All Negative Tests) * 100%
146
Q

What is diagnostic accuracy or diagnostic precision?

A

Medical Screening - Diagnostic Accuracy (DA)/ Diagnostic Precision (DP)

  • Proportion of total screenings that a patient is correctly identified as either having a disease (TP) or not (TN) with corresponding test results.
  • DA/DP =
    • (TP+TN)/(TP+FP+FN+TN) * 100%
    • (TP+TN) / (All Patients) * 100%
147
Q

What are likelihood ratios (LR)?

A

Medical Screening - Likelihood Ratio (LR)

  • Ratio of the probability of a given test result for those with disease to a given test result of those without disease.
  • Can be calculated for positive and negative test results.
  • LR+ should be >10 to demonstrate benefit.
  • LR- should be <0.1 to demonstrate benefit.
  • LR+ = [(A/(A+C))/(B/(B+D))]
  • LR- = [(C/(A+C))/(D/(B+D))]
148
Q

What is a cross-sectional study design?

A

Cross-Sectional Studies

  • Observational studies that capture health/disease and exposure statuses at the same time.
  • AKA prevalence study.
  • Called cross-sectional because information gathered represents what is occuring at a point in time or time frame across a large population (a snap-shot in time).
  • Aquired without regard to exposure or disease/outcomes status.
  • Entire pop or a subset is selected for study.
  • May be large-scale, national surveys or databases capturing different aspects of the pop.
    • Data from different perspectives (e.g., inpatient vs. outpatient).
149
Q

What 2 questions should a patient ask their physician when a medical screening test is recommended?

A

Medical Screening - Questions

  • How accurate is the screening test?
  • When the results are announced, how confident will you be in your prediction of whether or not I have the disease?
150
Q

What does it mean if a test has multiple cutoff values?

A

Medical Screening - Multiple Cutoff Values

  • Many diagnoses typically have 2 dichotomous outcomes (pos/neg).
  • However, with multiple cutoffs there may be an overlap of positive and negative outcomes.
151
Q

What are the 4 possible outcomes to medical screenings?

A

Medical Screening - 4 Outcomes

  • True positive.
  • True negative.
  • False positive.
  • False negative.
152
Q

When a patient asks about the accuracy of a screening test, what information are they looking for?

A

Medical Screening - Accuracy

  • Sensitivity
  • Specificity
155
Q

When a patient asks how confident a physician will be in their prediction of disease when the results are anounced, what information are they looking for?

A

Medical Screening - Confidence

  • Positive predictive value.
  • Negative predictive value.
160
Q

What is validity in medical screening?

A

Medical Screening - Validity

  • Ability to accurately discern between those that do and those that do not have the disease (precision).
  • Internal validity - extent of accurate results in study.
  • External validity - extent of accurate results in pop.
161
Q

What is reliability in medical screening?

A

Medical Screening - Reliability

  • Ability of test to return same result on repeated uses (reproducibility or consistency).
  • NOTE: A _valid_ test is _always reliable_, yet a _reliable_ test is _not always valid_.
163
Q

How would you interpret a LR+ of 1.0?

A

The test is just as likely to return a positive result in a person with disease as in a person without disease.

164
Q

What are the 3 primary levels or groupings of variables (data)?

A

Variables - Primary Levels

  • Levels:
    • Nominal
    • Ordinal
    • Interval or Ratio
  • ALL statistical tests are selected based on level of data being compared.
165
Q

What are the 3 key attributes of data that define their level or grouping, and how are they assessed?

A

Variables - Key Attributes

  • Attributes:
    • Order/Magnitude
    • Consistency of scale / equal distances
    • Rational absolute zero
  • Each attribute is assessed with a “Yes” or “No,” ‘does the variable have it?’
166
Q

What is a nominal variable?

A

Variables - Nominal

  • Dichotomous/binary; non-ranked; non-ordered; Named categories.
    • No - order or magnitude.
    • No - consistency of scale or equal distances (discrete).
    • Nominal variables are simply labeled-variables without quantitative characteristics (or dichotomous/binary).
  • ALL DICHOTOMOUS VARIABLES ARE NOMINAL.
  • INCLUDES CATEGORICAL VARIABLES DESPITE NUMBER OF CATEGORIES.
167
Q

What is an ordinal variable?

A

Variables - Ordinal

  • Ordered, rank-able categories, non-equal distance.
    • Yes - order of magnitude.
    • No - consistency of scale or equal distances (discrete).
  • PAIN SCALE WILL ALWAYS BE ORDINAL ON EXAM.
169
Q

How do the levels of data vary in specificity/detail?

A

Variables - Specificity/Detail of Levels

  • After data is collected, we can appropriately go down in specificity/detail of data, but never up.
170
Q

What are measures of central tendency.

A

Measures of Central Tendency

  • Mode / Median / Mean
  • Min / Max / Range
  • Interquartile Range (IQR)
171
Q

What is variance?

A

Measures of Central Tendency - Variance

172
Q

What is standard deviation?

A

Measures of Central Tendency - Standard Deviation

175
Q

What is kurtosis?

A

Shapes of Data Distribution - Kurtosis

  • A measure of the extent to which observations cluster around the mean.
  • Kurtosis statistic:
    • 0 = normal distribution.
    • Positive = more cluster.
    • Negative = less cluster.
176
Q

What percentages coincide with standard deviation ranges?

A

Standard Deviation - Percentages

177
Q

What is an interval/ratio variable?

A

Variables - Interval/Ratio

  • Order, magnitude, and equal distances (units).
    • Interval = arbitrary zero value (0 doesn’t mean absence)
    • Ratio = Absolute (rational) zero value (0 means absence of measurement value), i.e. physiological parameters).
    • Yes - order or magnitude.
    • Yes - consistency of scale or equal distances (continuous).
      • Living siblings (number) & personal age (in years).
178
Q

How is interval data, that is not normally-distributed, handled?

A

Interval Data - Not Normally-Distributed

  1. Use a statistical test that does not require the data to be normally-distributed (non-parametric tests), or
  2. Transform data to a standardized value (z-score or log transformation), hoping it will allow the data to be normally distributed.
  • ALWAYS RUN DESCRIPTIVE STATISTICS & GRAPHS.
183
Q

What are parametric tests?

A

Stats Tests - Parametric

  • Stats tests useful for normally-distributed data.
184
Q

What is skewness?

A

Shapes of Data Distribution - Skewness

  • A measure of the asymmetry of a distribution.
  • Perfectly-normal distribution = skewness of 0.
185
Q

What are the 4 key questions to ask when selecting the correct statistical test?

A

Statistical Test Selection - Questions for Selection

  1. What data level is being recorded?
    • Does data have order/magnitude?
    • Does data have equal, consistent distances along the entire scale?
  2. What type of comparison/assessment is desired?
    • Correlation -> correlation test
    • Event-occurrence / time-to-event -> survival test
    • Outcome prediction/association (OR) -> regression
  3. How many groups are being compared?
  4. Is the data independent or related (paired)?
    • Data from the same (paired) or different groups (independent).
  • Questions 2-4 get you around the other portions of each individual sheet.
186
Q

What is a correlation test?

A

Statistical Tests - Correlation Test

  • Correlation (r) - provides a quantitative measure of the strength & direction of a relationship between variables.
    • Range from -1.0 - +1.0.
    • -1.0 = strong negative correlation.
    • 0.0 = no correlation.
    • +1.0 = strong positive correlation.
  • Partial correlation - correlation that controls for confounding variables.
  • Types:
    • Nominal correlation test = Contingency Coefficient.
    • Ordinal correlation test = Spearman Correlation
    • Interval correlation test = Pearson Correlation
      • p>0.05 for a Pearson Correlation just means there is no linear correlation; may still be a non-linear correlations present.
  • All correlations can be run as a “partial correlation” to control for confounding.
187
Q

What is a survival test?

A

Statistical Tests - Survival Test

  • “Changes over time.”
  • Compares the proportion of events over time, or time-to-events, between groups.
  • Commonly represented by a Kaplan-Meier curve (all types can be represented by a Kaplan-Meier curve).
  • Types:
    • Nominal survival test = Log-Rank test
    • Ordinal survival test = Cox-Proportional Hazards test
    • Interval survival test = Kaplan-Meier curve
188
Q

What is a regression?

A

Statistical Test - Regression

  • “Predict likelihood of some outcome” = regression test.
  • Provide a measure of the relationship between variables by allowing the prediction about the dependent, or outcome, variable (DV) knowing the value/category of independent variables (IVs)
  • Basically, helps to determine how well variables can predict outcome.
  • Also able to calculate OR for a measure of association.
  • Types:
    • Nominal regression test = Logistic Regression
    • Ordinal regression test = Multinominal Logistic Regression
    • Interval regression test = Linear Regression
189
Q

What is a partial correlation, and how is it used to control for confounding?

A

Partial Correlation

  • Correlation that factors in the confounders, trying to keep them “mathematically quiet” to get a better idea of true correlation.
  • Can be used to validate correlation test result by controlling for possible confounders.
  • Example: Spearman’s returns small, but statistically significant, positive correlation. Partial returns even smaller postive correlation that is statistically not significant. Therefore initial correlation was not true.
190
Q

What is the Spearman Correlation test?

A

Correlation - Spearman Correlation Test

  • Correlation test for ordinal data.
191
Q

What is the Contingency Coefficient test?

A

Correlation - Contingency Coefficient Test

  • Correlation test for nominal data.
194
Q

For proper selection of a parametric test, what are the required assumptions?

A

Parametric Test - Selection

  • Required assumptions:
    1. Normally-distributed
    2. Equal variances
      • Multiple tests available to assess for equal variances between groups.
        • Levene’s test.
    3. Randomly-derived & independent.
196
Q

How is sample size determined?

A

Sample Size

  1. Minimum difference between groups deemed significant:
    • The smaller the difference necessary to be significant = the larger the N needed.
  2. Expected variation of measurement (known or estimated from past studies/population).
  3. Type 1 & type 2 error rates & confidence interval (usually 90%-99%)
  • ADD IN ANTICIPATED DROP-OUTS OR LOSS TO FOLLOW-UPS.
197
Q

What is a p value?

A

p Value

  • Statistical tests determine possible error-rate or liklihood of chance in comparing difference or relationship between variables.
    1. A statistical test critical value is calculated.
    2. Test statistic value is compared to the appropriate table of probabilities for that test.
    3. A probability (p) value is obtained; based on the probability of observing, due to chance alone, a test statistic value as extreme or more extreme than actually observed if groups were similar (not different)
      • The p-value is selected by investigators before the study starts (a priori).
198
Q

Describe the statistical significance of the p value.

A

p Value - Statistical Significance

  • If p value is lower than the pre-selected alpha value (customarily 5% (0.05)), then we say it is statistically significant.
    • This means that the risk of experiencing a type 1 error is acceptably low if we reject H0.
199
Q

How is a pre-set p value interpreted?

A

Pre-Set p Value - Interpretation

  • The probability of:
    • making a type 1 error if the H0 is rejected.
    • erroneously claiming a difference between groups when one does not really exist.
    • obtaining group differences as great or greater if the groups were actually the same/equal.
    • obtaining a test statistic as high/higher if the groups were actually the same/equal.
200
Q

What is Levene’s test?

A

Levene’s Test for Equality of Variances

  • Used to assess the equality or homogeneity of variances.
  • Independent t-tests assume homogeneity of variances.
  • Interpretation:
    • >0.05 means variances are NOT significantly different, thus the variances are similar.
    • 0.05 or less means variances are significantly different and thus there is no equality or homogeneity of variances.
201
Q

What is a confidence interval?

A

Confidence Interval (CI)

  • Most common selections are 90%, 95%, or 99%.
  • Interpretation:
    • We are 95% confident that the “true” difference or relationship between the groups is contained within the confidence interval range.
    • Without p value:
      • If CI crosses 1.0 (for ratios) or 0.0 (for absolute differences) = NOT significant.
  • Calculated at an a priori percentage of confidence, including a high and low value, that statistically includes the real (yet unknown) difference or relationship being compared.
  • Based on V/SD and N.
  • Journals are moving away from solely reporting p values; or even showing them at all.
202
Q

What is the Kappa test?

A

Kappa Tests

  • Kappa statistic - a correlation test showing relationship or agreement between evaluators (consistency of “decisions”, “determinations”).
  • Kappa interpretation:
    • Kappa (K) value can be + or -; + = good agreement; - = poor agreement.​
    • +1 = the observers perfectly “classify” everyone exactly the same way.
    • 0 = There is no relationship at all between the observers’ “classifications,”above the agreement that would be expected by chance.
    • -1 = The observers “classify” everyone exactly the opposite of each other.
203
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204
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205
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213
Q

What statistical tests are used to compare independent, nominal data and what are they comparing?

A

Independent Nominal Data - Statistical Tests

  • 2 groups:
    • (Pearson’s) Chi-square test
  • 3 groups:
    • Chi-square test of independence
      • Both tests compares group proportions and if they are different from that expected by chance.
      • Assumptions-Both test:
        • Usual chi-square (binomial; non-normal) distribution for nominal data.
        • No cell count < 5 observations.
    • For statistically significant findings (p<0.05) in 3 or more comparisons, one must perform subsequent analysis (post-hoc testing) to determine which groups are different:
      • Multiple chi-square tests are NEVER acceptable, risk of Type I error increases with each additional test (almost guaranteed after 4-5).
  • 2 or more groups with expected cell count of < 5:
    • Fisher’s Exact test
  • Tests compare frequencies/counts/proportions.
214
Q

What keywords indicate that data is paired or related?

A

Paired/Related - Keywords

  • “Pre- vs. -post”, “Before vs. After”, Baseline vs. End”, etc.
215
Q

If given both chi-square and Fisher’s Exact, what is the only situtation in which that can occur?

A

When the expected cell count is < 5. If you can confirm cell count < 5, then the chi-square is no longer valid and Fisher’s Exact is the right answer.

216
Q

What statistical tests are used to compare paired/related nominal data and what are they comparing?

A

Paired/Related Nominal Data - Statistical Tests

  • 2 groups of paired/related data:
    • McNemar test
  • 3 or more groups of paired/related data:
    • Cochran
      • Same as principle and assumptions as chi-square yet mathematically factors in concept of paired, or related, data.
      • Bonferroni test of inequality (Bonferroni correction)
        • Adjusts the p value for # of comparisons being made (very conservative).
  • Tests compare frequencies/counts/proportions.
  • “Pre- vs. -post”, “Before vs. After”, Baseline vs. End”, etc.
217
Q

What statistical tests are used to compare independent, ordinal data and what do they compare?

A

Independent Ordinal Data - Statistical Tests

  • 2 groups of independent data:
    • Mann-Whitney test
  • 3 or more groups of independent data:
    • Kruskal-Wallis test
  • The tests compare the median values between groups.
    • Both also used for interval data not meeting parametric requirements.
    • If 3+ group comparison significant, must perform a post-hoc test to determine where difference(s) is(are).
218
Q

What statistical tests are used to compare paired/related, ordinal data and what do they compare?

A

Paired/Related Ordinal Data - Statistical Tests

  • 2 groups of paired/related data:
    • Wilcoxon Signed Rank test.
  • 3 or more groups of paired/related data.
    • Friedman test
  • Tests compares the median values between groups.
    • Each effective for non-normally distributed interval data or don’t meet all parametric requirements.
    • If 3+ group comparison significant, must perform a post-hoc test to determine where differences are.
  • “Pre- vs. -post”, “Before vs. After”, Baseline vs. End”, etc.
219
Q

What are the post-hoc tests used for ordinal data?

A

Ordinal Data - Post-Hoc Tests

  • Student-Newman-Keul test
    • All groups MUST be equal in size.
    • Compares all pairwise comparisons possible.
  • Dunnett test
    • Compares all pairwise comparisons against a single control.
    • All groups MUST be equal in size.
  • Dunn test
    • Compares all pairwise comparisons possible.
    • Useful when all groups are NOT of equal size.
220
Q

What statistical tests are used to compare independent, interval data and what do they compare?

A

Interval Data - Statistical Tests

  • Independent Data:
    • 2 groups of independent data:
      • Student t-test.
    • 3 or more groups of independent data:
      • Analysis of variance (ANOVA)
    • Tests compare the means of all groups (along with intra- and inter-group variations) against a dependent variable.
      • If 3+ group comparison significant, must perform a post-hoc test to determine where differences are.
  • 3 or more groups of independent data w/ confounders:
    • Analysis of Co-Variance (ANCOVA)
      • Compares the means of all groups (along with intra- and inter-group variations) against a dependent variable while also controlling for the co-variance of confounders.
221
Q

What statistical tests are used to compare paired/related, interval data and what do they compare?

A

Paired/Related Interval Data - Statistical Tests

  • 2 groups of paired/related data:
    • Paired t-test.
      • Compares the mean values between groups that are related.
  • 3 or more groups of paired/related data:
    • Repeated Measures ANCOVA (1 DV)
      • Compares the means of all groups (along with intra- and inter-group variations) of related data against a dependent variable.
        • If 3+ group comparison significant, must perform a post-hoc test to determine where differences are.
  • 3 or more groups of paired/related data w/ confounders:
    • Repeated Measures ANOVA:
      • Compares the means of all groups (along with intra- and inter-group variations) against a dependent variable while also controlling for the co-variance of confounders.
222
Q

What are the post-hoc tests used for interval data?

A

Interval Data - Post-Hoc Tests

  • Student-Newman-Keul test
    • All groups MUST be equal in size.
    • Compares all pairwise comparisons possible.
  • Dunnett test
    • Compares all pairwise comparisons against a single control.
    • All groups MUST be equal in size.
  • Dunn test
    • Compares all pairwise comparisons possible.
    • Useful when all groups are NOT of equal size.
  • Tukey or Scheffe tests
    • Compares all pairwise comparisons possible
    • All groups must be equal in size.
      • Tukey test slightly more conservative than the Stu.N.K.
      • Scheffe test less affected by violations in normality and homogeneity of variances - most conservative.
  • Bonferroni correction
    • Adjusts the p value for # of comparisons being made (very conservative).
223
Q

What are ways of controlling confounding?

A

Controlling Confounding

  • Study design phase:
    • Randomization
    • Restriction
    • Matching
  • Analysis of data stage:
    • Stratification (w/ weighting)
    • Multivariate statistical analysis (regression analyses)
224
Q

What is selection-related bias?

A

Selection-Related Bias

  • Any aspect in the way the researcher selects or acquires study subjects which creates a systematic difference between groups.
    • Commonly seen when comparative groups not coming from the same population/group or not being representative of the full population or even differentially-selected (processes)

DON’T DO ANYTHING THAT IS DIFFERENT, OR CREATES A DIFFERENCE, BETWEEN GROUPS!!!

SAME-SAME-SAME

225
Q

What is measurement-related bias?

A

Measurement-Related Bias

  • Any aspect in the way the researcher collects information, or measures/observes subjects which creates a systematic difference between groups.
    • Errors in measurement can also cause a resultant error in patient classification (misclassification)

DON’T DO ANYTHING THAT IS DIFFERENT, OR CREATES A DIFFERENCE, BETWEEN GROUPS!!!

SAME-SAME-SAME

Comprehensive Review (7 decks)

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