Epi - Comp Exam Flashcards

Question 1

Q

What is Descriptive Epidemiology?

Answer

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.

Question 2

Q

What is Analytic Epidemiology?

Answer

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.

Question 3

Q

What are the 6 core functions of epidemiology?

Answer

A

Public health surveillance
Field investigation
Analytic studies
Evaluation
Linkages
Policy development

Question 4

Q

What is an epidemic?

Answer

A

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

Question 5

Q

What is an outbreak?

Answer

A

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

Question 6

Q

What is an endemic?

Answer

A

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

Question 7

Q

What is an emergency of international concern?

Answer

A

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

Question 8

Q

What is a pandemic?

Answer

A

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

Question 9

Q

What is Incidence Density?

Answer

A

Incidence Rate when summed over multiple time periods.

Question 10

Q

What is Point Prevalence?

Answer

A

Prevalence at a given point in time.

Question 11

Q

What is Period Prevalence?

Answer

A

Prevalence over a given period of time.

Question 12

Q

What is a Crude Morbidity Rate?

Answer

A

of Persons with Disease / # of Persons in Population

Question 13

Q

What is a Crude Mortality Rate?

Answer

A

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

Question 14

Q

What is a Cause-Specific Morbidity Rate?

Answer

A

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

Question 15

Q

What is a Cause-Specific Mortality Rate?

Answer

A

of Cause-Specific Deaths / # of Persons in Population

Question 16

Q

What is a Case-Fatality Rate?

Answer

A

of Cause-Specific Deaths / # of Cases of Disease

Question 17

Q

What is a Cause-Specific Survival Rate?

Answer

A

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

Question 18

Q

What is a Proportional Mortality Rate (PMR)?

Answer

A

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

Question 19

Q

What is a Live Birth-Rate?

Answer

A

of Live Births / 1000 Population

Question 20

Q

What is a Fertility Rate

Answer

A

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

Question 21

Q

What is a Neonatal Mortality Rate?

Answer

A

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

Question 22

Q

What is a Postnatal Mortality Rate?

Answer

A

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

Question 23

Q

What is an Infant Mortality Rate?

Answer

A

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

Question 24

Q

What is a Maternal Mortality Ratio?

Answer

A

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

Question 25

Q

What is Infectivity?

Answer

A

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

infected / # susceptable (at risk)

Question 26

Q

What is Pathogenicity?

Answer

A

Pathogenicity is the ability to cause clinical disease.

with disease / # infected

Question 27

Q

What is Virulence?

Answer

A

Virulence is the ability to cause death.

of deaths / # with infectious disease

Question 28

Q

What is Risk?

Answer

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”

Question 29

Q

What is Absolute Risk Reduction (ARR)?

Answer

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%

Question 30

Q

What is Relative Risk Reduction?

Answer

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%

Question 31

Q

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

Answer

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.

Question 32

Q

How are Ratios interpreted?

Answer

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.

Question 33

Q

How are Ratios reported?

Answer

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.

Question 34

Q

How are Confidence Intervals for Ratios interpreted?

Answer

A

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

Question 35

Q

How is the Odds Ratio interpretted?

Answer

A

Odds Ratio is interpretted just like any ratio.

Example: OR = 10.09

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

Question 36

Q

What are the 3 required elements in interpretting Odds Ratios?

Answer

A

3 Required Elements:

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

Question 37

Q

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

Answer

A

Confounding

Question 38

Q

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

Answer

A

Internal Validity

3 aspects of internal validity:
- Confounding or Effect Modificaiton
- Bias
- Statistical significance.

Question 39

Q

How do you test for confounding?

Answer

A

Testing for Confounding

Calculate CRUDE (unadjusted) measure of association (OR/RR) between exposure and outcome.
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.
Compare the Crude vs. Adjusted measures of association between exposure and outcome.
- If they vary by 15% or more, then confounding is present.

Question 40

Q

What are the two main impacts of confounding?

Answer

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.

Question 41

Q

What are ways of controlling confounding?

Answer

A

Controlling Confounding

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

Question 42

Q

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

Answer

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.

Question 43

Q

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

Answer

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).

Question 44

Q

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

Answer

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.

Question 45

Q

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

Answer

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.

Question 46

Q

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

Answer

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

Question 47

Q

What is effect modification?

Answer

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.

Question 48

Q

How do you test for effect modification?

Answer

A

Testing for Effect Modification

Calculate crude measure of association between exposure and outcome (OR/RR)
Calculate strata-specific measures of association between exposure and outcome (OR/RR) for each strata of 3rd variable.
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.

Question 49

Q

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

Answer

A

Confounding or effect modification
Bias
Statistical significance

Question 50

Q

What is bias?

Answer

A

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

Question 51

Q

What does bias distort?

Answer

A

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

Question 52

Q

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

Question 53

Q

What can minimize bias and its impact?

Answer

A

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

Question 54

Q

What 3 elements can bias impact?

Answer

A

Bias can impact:

Source/type
Magnitude/strength
Direction

Question 55

Q

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

Answer

A

2 Main Categories of Bias

Selection-related
Measurement-related

Question 56

Q

What is selection-related bias?

Answer

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

Question 57

Q

What is measurement-related bias?

Answer

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

Question 58

Q

What are the 2 types of selection bias?

Answer

A

Selection Bias

Healthy-Worker Bias
Self-Selection/Participant (Responder) Bias

Question 59

Q

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

Answer

A

Measurement Bias - Subject-Related

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

Question 60

Q

What is recall bias?

Answer

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.

Question 61

Q

What is the Hawthorne Effect?

Answer

A

Hawthorne Effect

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

Question 62

Q

What is misclassification bias?

Answer

A

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

Question 63

Q

What are the two types of misclassification bias?

Answer

A

Misclassification - 2 Types

Non-differential
Differential

Question 64

Q

What is non-differential misclassification bias?

Answer

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.

Answer 64

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.

Answer 65

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)

Answer 66

A

Associations (Relationships) - Types

Artifactual (false) associations.
Non-causal associations.
Causal associations.

Answer 67

A

Artifactual Associations

Can arise from Bias and/or Confounding.

Answer 68

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).

Answer 69

A

Causal Relationships - 3 Types

Sufficient Cause
Necessary Cause
Component Cause

Answer 70

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.

Answer 71

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.”

Answer 72

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.

Answer 73

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)

Answer 74

A

Biologic Gradient

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

Answer 75

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.

Answer 76

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.

Answer 77

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

Answer 78

A

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

Answer 79

A

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

Answer 80

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)

Answer 81

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

Answer 82

A

3 Requirements of Confounders

Independently associated with the exposure.
Independently associated with the outcome.
Not directly in the causal-pathway linking exposure to outcome.

Answer 83

A

Quantitative Study Designs

Interventional
Observational

Answer 84

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.

Answer 85

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.

Answer 86

A

Interventional Studies - General Phase Differentiators

Purpose/focus
Population studied (healthy/diseased).
Sample Size
Duration

Answer 87

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).

Answer 88

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.

Answer 89

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.

Answer 90

A

Research Hypothesis - Statistical-Perspectives

Superiority
Noninferiority
Equivalence

Answer 91

A

Research Hypothesis - Alternative Hypothesis

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

Answer 92

A

Research Hypothesis - Error

Type I - False positive.
Type II - False negative.

Answer 93

A

Sampling Schemes - Probability

Simple Random
Systemic Random
Stratified Simple
Stratified Disproportionate
Multi-Stage Random
Cluster Multi-Stage

Answer 94

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.

Answer 95

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 n^th numbers to get desired sample size.

Answer 96

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.

Answer 97

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

Answer 98

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.

Answer 99

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’

Answer 100

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).

Answer 101

A

4 Key Principles of Bioethics

Autonomy
Beneficence
Justice
Nonmaleficence

Answer 102

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).

Answer 103

A

Principles of Bioethics - Beneficence

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

Answer 104

A

Principles of Bioethics - Justice

Equal & fair treatment regardless of patient characteristics.

Answer 105

A

Principles of Bioethics - Nonmaleficence

Do no harm.
Researchers must not:
- Withhold info.
- Provide false info.
- Exhibit professional incompetence.

Answer 106

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).

Answer 107

A

IRB - Determining Level

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

Answer 108

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).

Answer 109

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).

Answer 110

A

Interventional Studies - Phase 1

Investigational New Drug

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

Answer 111

A

Interventional Studies - Phase 2

Investigational New Drug

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

Answer 112

A

Interventional Studies - Phase 3

Investigational New Drug; Indication/Population

Assess effectiveness (continues to assess safety/tolerability)
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
Larger N (e.g., 500-3000)
Longer duration (e.g., a few months to a year+).

Answer 113

A

Interventional Studies - Phase 4

Post FDA-Approval

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

Answer 114

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)

Answer 115

A

Patient-Oriented Endpoints

Death
Stroke or MI
Hospitalization
Preventing need for dialysis

Answer 116

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)

Answer 117

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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

Answer 118

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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.

Answer 119

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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.

Answer 120

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).

Answer 121

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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).

Answer 122

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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.

Answer 123

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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.

Answer 124

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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).

Answer 125

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.

Answer 126

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.

Answer 127

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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

Answer 128

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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.

Answer 129

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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.

Answer 130

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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.

Answer 131

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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).

Answer 132

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%

Answer 133

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%

Answer 134

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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).

Answer 135

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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%

Answer 136

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%

Answer 137

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%

Answer 138

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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))]

Answer 139

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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).

Answer 140

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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?

Answer 141

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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.

Answer 142

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Medical Screening - 4 Outcomes

True positive.
True negative.
False positive.
False negative.

Answer 143

A

Medical Screening - Accuracy

Sensitivity
Specificity

Answer 144

A

Medical Screening - Confidence

Positive predictive value.
Negative predictive value.

Answer 145

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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.

Answer 146

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_.

Answer 147

A

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

Answer 148

A

Variables - Primary Levels

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

Answer 149

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?’

Answer 150

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.

Answer 151

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.

Answer 152

A

Variables - Specificity/Detail of Levels

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

Answer 153

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Measures of Central Tendency

Mode / Median / Mean
Min / Max / Range
Interquartile Range (IQR)

Answer 154

A

Measures of Central Tendency - Variance

Answer 155

A

Measures of Central Tendency - Standard Deviation

Answer 156

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.

Answer 157

A

Standard Deviation - Percentages

Answer 158

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).

Answer 159

A

Interval Data - Not Normally-Distributed

Use a statistical test that does not require the data to be normally-distributed (non-parametric tests), or
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.

Answer 160

A

Stats Tests - Parametric

Stats tests useful for normally-distributed data.

Answer 161

A

Shapes of Data Distribution - Skewness

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

Answer 162

A

Statistical Test Selection - Questions for Selection

What data level is being recorded?
- Does data have order/magnitude?
- Does data have equal, consistent distances along the entire scale?
What type of comparison/assessment is desired?
- Correlation -> correlation test
- Event-occurrence / time-to-event -> survival test
- Outcome prediction/association (OR) -> regression
How many groups are being compared?
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.

Answer 163

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.

Answer 164

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

Answer 165

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

Answer 166

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.

Answer 167

A

Correlation - Spearman Correlation Test

Correlation test for ordinal data.

Answer 168

A

Correlation - Contingency Coefficient Test

Correlation test for nominal data.

Answer 169

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.

Answer 170

A

Sample Size

Minimum difference between groups deemed significant:
- The smaller the difference necessary to be significant = the larger the N needed.
Expected variation of measurement (known or estimated from past studies/population).
Type 1 & type 2 error rates & confidence interval (usually 90%-99%)

ADD IN ANTICIPATED DROP-OUTS OR LOSS TO FOLLOW-UPS.

Answer 171

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).

Answer 172

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.

Answer 173

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.

Answer 174

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.

Answer 175

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.

Answer 176

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.

Answer 177

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.

Answer 178

A

Paired/Related - Keywords

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

Answer 179

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.

Answer 180

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.

Answer 181

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).

Answer 182

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.

Answer 183

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.

Answer 184

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.

Answer 185

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.

Answer 186

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).

Answer 187

A

Controlling Confounding

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

Answer 188

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

Answer 189

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