Epi - Comp Exam Flashcards

Question

What is Infectivity?

Answer 1

Infectivity is the ability to invade a patient (host) . infected / # susceptable (at risk)

Answer 2

Pathogenicity is the ability to cause clinical disease. with disease / # infected

Answer 3

Virulence is the ability to cause death. of deaths / # with infectious disease

Answer 4

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"

Answer 5

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%

Answer 6

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%

Answer 7

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.

Answer 8

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.

Answer 9

* =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.

Answer 10

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

Answer 11

Odds Ratio is interpretted just like any ratio. Example: OR = 10.09 \_\_\_\_\_ exposed are 10 x more likely to _____ than _____ not exposed.

Answer 12

3 Required Elements: * The comparison group * Percentage/times more/less likely * Compared to reference group.

Answer 13

Confounding

Answer 14

Internal Validity * 3 aspects of internal validity: * Confounding or Effect Modificaiton * Bias * Statistical significance.

Answer 15

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.

Answer 16

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.

Answer 17

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

Answer 18

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.

Answer 19

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).

Answer 20

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.

Answer 21

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.

Answer 22

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

Answer 23

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.

Answer 24

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.

Answer 25

* Confounding or effect modification * Bias * Statistical significance

Answer 26

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

Answer 27

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

Answer 28

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

Answer 29

Bias can impact: * Source/type * Magnitude/strength * Direction

Answer 30

2 Main Categories of Bias * Selection-related * Measurement-related

Answer 31

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 32

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

Answer 33

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

Answer 34

Measurement Bias - Subject-Related * Recall bias * Hawthorne Effect * Contamination bias * Compliance/Adherence bias * Lost to follow-up bias

Answer 35

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.

Answer 36

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

Answer 37

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

Answer 38

Misclassification - 2 Types * Non-differential * Differential

Answer 39

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 40

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 41

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 42

Associations (Relationships) - Types 1. Artifactual (false) associations. 2. Non-causal associations. 3. Causal associations.

Answer 43

Artifactual Associations * Can arise from Bias and/or Confounding.

Answer 44

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 45

Causal Relationships - 3 Types * Sufficient Cause * Necessary Cause * Component Cause

Answer 46

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 47

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 48

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 49

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 50

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

Answer 51

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 52

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 53

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 54

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

Answer 55

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

Answer 56

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 57

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 58

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.

Answer 59

Quantitative Study Designs * Interventional * Observational

Answer 60

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 61

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 62

Interventional Studies - General Phase Differentiators 1. Purpose/focus 2. Population studied (healthy/diseased). 3. Sample Size 4. Duration

Answer 63

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 64

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 65

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 66

Research Hypothesis - Statistical-Perspectives * Superiority * Noninferiority * Equivalence

Answer 67

Research Hypothesis - Alternative Hypothesis * H₁ * A research perspective which states there will be a (true) difference between the groups compared.

Answer 68

Research Hypothesis - Error * Type I - False positive. * Type II - False negative.

Answer 69

Sampling Schemes - Probability * Simple Random * Systemic Random * Stratified Simple * Stratified Disproportionate * Multi-Stage Random * Cluster Multi-Stage

Answer 70

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 71

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 72

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 73

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 74

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 75

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 76

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 77

4 Key Principles of Bioethics * Autonomy * Beneficence * Justice * Nonmaleficence

Answer 78

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 79

Principles of Bioethics - Beneficence * To benefit, or do good for, the patient (not society).

Answer 80

Principles of Bioethics - Justice * Equal & fair treatment regardless of patient characteristics.

Answer 81

Principles of Bioethics - Nonmaleficence * Do no harm. * Researchers must not: * Withhold info. * Provide false info. * Exhibit professional incompetence.

Answer 82

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 83

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

Answer 84

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 85

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 86

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)

Answer 87

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).

Answer 88

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+).

Answer 89

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.

Answer 90

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 91

Patient-Oriented Endpoints * Death * Stroke or MI * Hospitalization * Preventing need for dialysis

Answer 92

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 93

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 94

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 95

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 96

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 97

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 98

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 99

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 100

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 101

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 102

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 103

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 104

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 105

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 106

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 107

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 108

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 109

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 110

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 111

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 112

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 113

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 114

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 115

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 116

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 117

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 118

Medical Screening - 4 Outcomes * True positive. * True negative. * False positive. * False negative.

Answer 119

Medical Screening - Accuracy * Sensitivity * Specificity

Answer 120

Medical Screening - Confidence * Positive predictive value. * Negative predictive value.

Answer 121

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 122

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 123

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

Answer 124

Variables - Primary Levels * Levels: * Nominal * Ordinal * Interval or Ratio * ***_ALL statistical tests are selected based on level of data being compared._***

Answer 125

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 126

Variables - **_N_**ominal * Dichotomous/binary; non-ranked; non-ordered; **_N_**amed **_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 127

Variables - **_Ord_**inal * **_Ord_**ered, 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 128

Variables - Specificity/Detail of Levels * After data is collected, we can appropriately go down in specificity/detail of data, but never up.

Answer 129

Measures of Central Tendency * Mode / Median / Mean * Min / Max / Range * Interquartile Range (IQR)

Answer 130

Measures of Central Tendency - Variance

Answer 131

Measures of Central Tendency - Standard Deviation

Answer 132

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 133

Standard Deviation - Percentages

Answer 134

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 135

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._***

Answer 136

**_Stats Tests - Parametric_** * ***_Stats tests useful for normally-distributed data._***

Answer 137

Shapes of Data Distribution - Skewness * A measure of the asymmetry of a distribution. * Perfectly-normal distribution = skewness of 0.

Answer 138

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._***

Answer 139

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 140

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 141

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 142

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 143

Correlation - Spearman Correlation Test * Correlation test for ordinal data.

Answer 144

Correlation - Contingency Coefficient Test * Correlation test for nominal data.

Answer 145

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 146

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._***

Answer 147

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 148

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 149

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 150

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 151

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 152

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 153

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 154

Paired/Related - Keywords * ***_"Pre- vs. -post", "Before vs. After", Baseline vs. End", etc._***

Answer 155

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 156

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 157

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 158

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 159

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 160

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 161

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 162

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 163

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

Answer 164

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 165

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

Epi - Comp Exam Flashcards

(196 cards)