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2021 Epi Comps > Epi Methods 752 > Flashcards

Epi Methods 752 Flashcards

1
Q

Degree to which study is free from bias; inferences from study population reflect inferences that would be observed in target population; prerequisite for external validity

A

Internal Validity

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2
Q

Includes allocation concealed, masked, common data collection, quality assurance monitoring

A

Metrics of Study Quality

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3
Q

Degree to which results of study may apply, be relevant, or be generalized to populations/groups that didn’t participate in study; assessing whether internally valid inferences apply to other target populations; representativeness

A

External Validity

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4
Q

Vague definition of target population, inability to define source population, or problems with process of obtaining study population

A

Barriers to Internal Validity

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5
Q

Condition –> identify risk factors –> test intervention –> dissemination –> repeat cycle

A

Process of Studying Health Outcomes

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6
Q

Study design where subsets of defined population identified with exposure to factor(s) hypothesized to influence occurrence of outcome; compare incidences in groups that differ by exposure levels; denominators are typically persons or person-time

A

Cohort Study

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7
Q

Research question identified, protocol developed, & cohort assembled, then exposure measured, then participants followed for outcomes

A

Prospective Cohort Study

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8
Q

Exposures & outcome occur and measured for other purpose, then research question identified, protocol developed, & cohort assembled

A

Retrospective Cohort Study

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9
Q

Cohort that can gain & lose members over time; fixed or dynamic

A

Open Cohort

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10
Q

Cohort cannot gain members after defined time/event & loses members only to outcome or end of study

A

Closed Cohort

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11
Q

Evaluate exposure with many outcomes, temporality, calculate risk, time-varying effects; expensive, can take long time to conduct, not efficient for long disease process/rare outcome, may not be warranted for rare exposure

A

Pros & Cons of Cohort

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12
Q

Enroll all individuals who meet eligibility criteria; non-probabilistic & may not reflect target population

A

Convenience Sample

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13
Q

All individuals have known probability of selection; must be able to enumerate population

A

Probability Sample

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14
Q

Probability sample, random start then sample every “nth” unit

A

Systematic Sample

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15
Q

Probability sample, divide population into homogenous strata & select random sample within each strata

A

Stratified Random Sample

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16
Q

Probability sample, divide population into heterogeneous clusters, randomly sample clusters, & measure within chosen cluster

A

Cluster Sample

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17
Q

Immigrative selection bias by self-referral, immigrative non-response bias, emigrative loss to follow-up

A

Selection Bias in Cohort Study

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18
Q

Exposure time that doesn’t biologically contribute to outcome (after etiologically relevant time window for exposure); including time in analysis dilutes effect of exposure

A

Wasted Exposure

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19
Q

Collect follow-up data by linkage to external systems

A

Passive Follow-Up

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20
Q

Collect follow-up data by interaction with participants or proxies

A

Active Follow-Up

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21
Q

Enumerate group of at risk individuals followed for outcome

A

Cohort

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22
Q

Cohort members still at risk for outcome at time of event; aligned by time origin & time metric

A

Risk Set

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23
Q

Persons credited for time at risk during which effect cannot possibly occur; including time in analysis creates artificially lower event rate; typically occurs when cohort entry dependent on survival

A

Immortal Person Time

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24
Q

Persons continue to contribute person-time to cohort after death due to imperfect mortality ascertainment; typically occurs in aging cohorts

A

Ghost Time

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25
Q

Planned experiment designed to assess efficacy of treatment by comparing outcomes in comparable groups receiving intervention or control, participants in both groups enrolled, treated, & followed over same time period

A

Randomized Clinical Trial

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26
Q

Genuine uncertainty within professional community as to which of 2 treatment arms is superior; justification for randomization

A

Clinical Equipoise

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27
Q

Process where treatment follows no describable deterministic pattern but a probabilistic pattern; exposure decision removed from participant/provider

A

Randomization

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28
Q

Avoids selection bias (confounding by indication), groups should be similar by baseline characteristics (exchangeability), valid significance levels for statistical tests, defined time origin

A

Pros of Randomization

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29
Q

Restriction, restricted/adaptive randomization, or adjustment for baseline characteristics

A

Control for Confounding in RCT

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30
Q

Process for preventing disclosure of treatment assignments to participants or study staff; prevents immigrative selection bias; occurs at enrollment

A

Concealment of Random Allocation

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31
Q

Treatment assignment not known after randomization; prevents differential measurement error; can be single, double, or triple; occurs during course of trial

A

Masking

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32
Q

Each new assignment independent; number of patients & characteristics in groups should be equal in long run (potentially not in small trials)

A

Simple Randomization

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33
Q

Blocking ensures number of individuals in each treatment arm is equal; stratification ensures number of individuals in each treatment arm is equal by confounder

A

Restricted Randomization

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34
Q

Current group composition influences next allocation

A

Adaptive Randomization

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35
Q

Adaptive randomization where software chooses treatment assignment to yield smallest imbalance

A

Minimization

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36
Q

Mix of effect & sample size and doesn’t prove causality, show factor is confounder, or consider definitions of confounding

A

Limitations of Statistical Testing

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37
Q

Analysis of RCT according to randomization irrespective of what happens afterwards; keeps randomization intact but may blur treatment effects (conservative)

A

Intention to Treat Analysis

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38
Q

Analysis of RCT according to treatment participants received; may overestimate treatment effects relative to “real world” effects; often secondary analysis (primary analysis in some non-inferiority trials)

A

Per Protocol Analysis

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39
Q

Immigrative selection bias (insufficient concealment of random allocation), emigrative selection bias (differential loss to follow-up), information bias (differential measurement error due to insufficient masking), reporting bias

A

Bias in RCTs

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40
Q

More than 1 treatment, examine treatments independently & together; indicated if 2 treatments act independently or influence each other

A

Factorial 2x2 Trial

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41
Q

Designed to show test intervention not inferior to comparison by margin of non-inferiority (Δ); tests Ho that treatment effects differ between groups

A

Non-Inferiority Trial

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42
Q

Designed to show test intervention equivalent to comparison (-Δ to +Δ)

A

Equivalence Trial

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43
Q

Tests Ho of no difference between treatment groups

A

Superiority Trial

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44
Q

Unit of randomization is group not individual; useful when intervention cannot be easily isolated; account for correlations in analysis

A

Cluster Trial

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45
Q

Participants randomized to treatment, then measurement of outcomes & washout, then receive opposite treatment, then measurement of outcomes; each subject is own control (closest to exchangeability); condition must be stable, intervention must not cause permanent change, outcome must be repeatable, carry-over effects must be small, drop out must be low

A

Cross-Over Trial

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46
Q

Use accumulating data to decide how to modify aspects of trial

A

Adaptive Trial

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47
Q

Randomization used to achieve balance across prognostic factors at baseline; equal allocation probabilities; stratified randomization for confounding

A

Fixed Allocation Rule

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48
Q

Randomization used interim data to unbalance allocation probabilities in favor of “better” treatments (“playing the winner”)

A

Adaptive Allocation Rule

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49
Q

Occurs earlier in disease process; should have strong consistent association with clinical outcome (in causal pathway) & yield same inference as outcome

A

Surrogate Outcome

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50
Q

More frequent events, shorter time; potential inconsistent relationship with outcome, may not be reliable indicator of treatment effects

A

Pros & Cons of Surrogate Outcome

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51
Q

Outcomes must be of similar importance, incidence, & effect; reduces sample size, useful if no obvious primary outcome, measure for common underlying mechanism, reduces multi-dimensionality

A

Composite Outcome

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52
Q

Study individuals with & without disease; examine relationship of exposure by comparing diseased & non-diseased subjects with regard to frequency of exposure

A

Case-Control Study

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53
Q

Individuals with disease, representative of individuals with disease in source population; individuals who could have had disease but didn’t, representative of individuals without disease in source population; do not select based on exposure status

A

Cases vs. Controls

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54
Q

Shorter time period, assess multiple exposures for 1 outcome, efficient for outcomes with long latency period/rare outcome

A

Pros of Case-Control Study

55
Q

Immigrative selection bias due to diagnostic bias, Berkson’s bias, self-selection, non-response, healthy worker, survival bias (often exclude prevalent cases)

A

Bias in Case-Control Studies

56
Q

Data collection occurs after outcome has developed (historical information) & at one time; recall bias possible

A

Measuring Exposure in Case-Control Study

57
Q

Cases remember exposure differently than controls; if cases remember exposure better & controls underreport exposure –> increase A or decrease B then increase OR

A

Recall Bias

58
Q

OR exposure = OR disease = 1/OR non-disease

A

Invariance of Odds Ratio

59
Q

OR approximates IRR when . . . ; sampling of cases & controls must be independent of exposure

A

Rare Disease Approximation

60
Q

Select cases & controls to be similar based on strong confounder; not useful to have more than 1:4 ratio of cases to controls

A

Matched Case-Control Study

61
Q

Calculate matched OR (otherwise effect underestimated) using conditional logistic regression; cannot examine effect of matched factor(s) but controls for confounding of matched factor(s); avoid over-matching; increases internal validity but may decrease external validity

A

Matched Case-Control Analysis

62
Q

Case-comparison at time of event; source population well-characterized; use existing data; often used to assess invasive/expensive exposure

A

Nested Study

63
Q

Incidence density sampling to match for time at risk; randomly select controls (still at risk) at each time case occurs; can also match on confounders; efficient for time-varying exposures

A

Nested Case-Control Study

64
Q

Select sub-cohort at baseline & analyze as prospective cohort; compare cases to all controls (still at risk) in sub-cohort; cases not in sub-cohort “pop” into analysis just for time of event; efficient for rare outcomes & time-fixed exposures

A

Case-Cohort Study

65
Q

Case-only study in which case serves as own matched control; useful for when brief/transient exposure triggers rise in risk of outcome with acute onset; exposure status assessed at different times & compared to exposure status at time of event; analyze using conditional logistic regression

A

Case-Crossover Study

66
Q

Examines relationship between outcome & exposure in defined population at 1 particular time; describes prevalence of outcome or exposure; sampling not guided by disease status; useful to determine burden of disease, prevalence of risk factors, or monitor community over time

A

Cross-Sectional Study

67
Q

Limited for causal inference, no temporality, information bias (recall bias), selection bias (participation bias, survival bias), reverse causality, relevant exposure window, inefficient for rare exposure or outcome

A

Limitations of Cross-Sectional Study

68
Q

Units of analysis are groups not individuals; conclusion may not apply to individuals (ecologic fallacy)

A

Ecologic Study

69
Q

Not causality checklist; strength, consistency, temporality, biological gradient, experiment, analogy, specificity, & plausibility

A

Bradford Hill Criteria

70
Q

Minimal set of conditions & events sufficient for outcome to occur; conceptualized using Rothman’s pies

A

Sufficient Cause

71
Q

Particular type of component cause required for outcome to occur

A

Necessary Cause

72
Q

Theoretical comparison group; compare rate of outcome if population exposed to rate of outcome if same population unexposed

A

Counterfactual

73
Q

Measured value = true value + error = true value + bias + random error

A

Measurement Error Model

74
Q

Systematic difference between true & measured value; assessed by comparing to gold standard

A

Bias

75
Q

Error not due to systematic measurement error; assessed by performing repeated measurements on same person/sample

A

Random Error

76
Q

Measurement error in variable in question doesn’t depend on levels of other variables (e.g. accuracy for measuring outcome same in exposed & unexposed)

A

Non-Differential Error

77
Q

Measurement error in variable in question depends on levels of other variables (e.g. accuracy for measuring outcome different in exposed & unexposed); bias can go in any direction

A

Differential Error

78
Q

Measurement error in variable in question not associated with errors in measuring other variables

A

Independent Error

79
Q

Measurement error in variable in question associated with errors in measuring other variables (e.g. both variables derived from same questionnaire)

A

Dependent Error

80
Q

Sensitivity/specificity, kappa

A

Quantify Measurement Error for Dichotomous Variables

81
Q

Spearman correlation coefficient, kappa

A

Quantify Measurement Error for Categorical Variables

82
Q

Coefficient of variation, intraclass correlation coefficient

A

Quantify Measurement Error for Continuous Variables

83
Q

Variance between individuals/(variance between individuals + variance within individuals)

A

Intraclass Correlation Coefficient

84
Q

SD replicates/mean replicates * 100

A

Coefficient of Variation

85
Q

Se + Sp > 1.0, effect estimate attenuated

A

Informative Test

86
Q

Se + Sp = 1.0, null effect estimate

A

Uninformative Test

87
Q

Se + Sp < 1.0, effect estimate flipped

A

Misinformative Test

88
Q

Phenomenon that if continuous variable is extreme on first measurement, it will tend to be closer to average on subsequent measurements; may result from intra-individual variability or random error

A

Regression Toward the Mean

89
Q

Expression of how close measurement is to true value; opposite of bias; assessed using sensitivity/specificity, correlation coefficient, scatterplot, Bland-Altman plot

A

Validity

90
Q

Ability of test to correctly identify those who have disease –> proportion correctly classified as having disease by measure compared with gold standard

A

Sensitivity

91
Q

Ability of test to correctly identify those who don’t have disease –> proportion correctly classified as not having disease by measure compared with gold standard

A

Specificity

92
Q

Probability of having disease given results of test; depends on sensitivity, specificity, & prevalence of condition in population

A

Predictive Value

93
Q

Proportion of persons with positive test result defined as having condition

A

Positive Predictive Value

94
Q

Proportion of persons with negative test result defined as not having condition

A

Negative Predictive Value

95
Q

Measures how close data is to line of best fit (not line of agreement); measures linear trends

A

Pearson’s Correlation Coefficient

96
Q

Special case of Pearson’s correlation for ordinal factor, non-linear relationship, or skewed data; measures increasing or decreasing trends

A

Spearman’s Rank Correlation Coefficient

97
Q

Expression for how precise measurement are; assessed by performing repeated measurements & calculating percent agreement, percent positive agreement, kappa, correlation coefficient, coefficient of variation, scatterplot, or Bland-Altman plot

A

Reliability

98
Q

Sum of agreement cells/total # individuals; can be heavily weighted by individuals classified as negative on both; does not take chance into account

A

Percent Agreement

99
Q

(Observed agreement - expected agreement)/(100-expected agreement)

A

Kappa

100
Q

Biased estimate of exposure-outcome association resulting from selection of study participants as effect of exposure & outcome; exposure-outcome association conditioned on study participation

A

Selection Bias

101
Q

Immigrative selection bias; issue with detection/classification of cases & non-cases

A

Ascertainment Bias

102
Q

Ascertainment bias; physician aware of possible associations between exposure & outcome –> follows exposed persons more closely

A

Diagnostic Bias

103
Q

Ascertainment bias; combination of exposure & disease increases risk of admission to hospital –> exposure & disease co-occur in hospital setting

A

Berkson’s Bias

104
Q

Ascertainment bias; participants had to survive up to certain time to be sampled

A

Survival Bias

105
Q

Immigrative selection bias; issue with how individuals end up in study

A

Participation Bias

106
Q

Participation bias; volunteers different from non-volunteers

A

Self-Selection Bias

107
Q

Participation bias; responders different from non-responders

A

Non-Response Bias

108
Q

Participation bias; people who are employed are healthier than unemployed & workers who continue working (more exposed) are healthier than those who stop working

A

Healthy Worker Effect

109
Q

Emigrative selection bias with composition of study population changing relative to source population

A

Differential Loss to Follow-Up

110
Q

Differential LTFU; susceptible participants more likely to die or drop-out

A

Depletion of Susceptibles

111
Q

Differential LTFU; event whose occurrence precludes occurrence of another event or alters probability of occurrence of event

A

Competing Risks

112
Q

Differential LTFU; participant not trackable, didn’t adhere to protocol, did not complete follow-up, withdrew, etc.

A

Dropout

113
Q

Effect of exposure of interest mixed together with effect of another variable leading to incorrect effect estimate

A

Mixing of Effects Definition

114
Q

Factor is associated with exposure, risk factor for outcome, & not intermediate step in causal pathway

A

Classical Definition

115
Q

Effect is difference in outcome caused by different exposure states in one study population during one time period; formalized using potential outcomes

A

Counterfactual Definition

116
Q

Effect is homogenous across strata defined by factor & crude effect estimate different from adjusted estimate by > 10%

A

Collapsibility Definition

117
Q

Overestimation of effect (away from null); adjustment weakens estimate

A

Anticonservative Confounding

118
Q

Underestimation of effect (toward null); adjustment strengthens estimate

A

Conservative Confounding

119
Q

Inference crosses null or reaches null

A

Qualitative Confounding

120
Q

Confounder-exposure & confounder-outcome associations either both + or -

A

Positive Confounding

121
Q

Confounder-exposure association + & confounder-outcome association -, or vice versa

A

Negative Confounding

122
Q

Restriction, matching, or randomization

A

Control for Confounding at Design/Conduct Stage

123
Q

Sum of stratum-specific rates weighted by person-time distribution of standard population; ∑(Tk)(IkA)/∑Tk; distribution of standard population but comparable

A

Direct Standardization

124
Q

Calculate expected number of events applying stratum-specific rates of standard population to observed population weights; observed/expected; same distribution of population of interest but not comparable

A

Indirect Standardization

125
Q

Partition sample according to confounder/EMM, calculate stratum-specific estimates, compare to crude estimate

A

Stratified Analysis

126
Q

Weighted average of stratum-specific measures; ∑(lnORk)*(weightk)/∑(weightk); use if p>0.05 for test of heterogeneity

A

Generalized Inverse Variance Method

127
Q

Weighted average of stratum-specific measures; ∑(AkDk/Nk)/∑(BkCk/Nk); use if p>0.05 for test of heterogeneity; better statistical properties, but can only use for a small number of categorical confounders

A

Mantel-Haenszel Method

128
Q

2 or more risk factors modify effect of each other with regard to outcome; heterogeneity of effects

A

Effect Measure Modifier

129
Q

Each stratified effect measure suggests increased or decreased risk but of different magnitudes

A

Quantitative EMM

130
Q

One stratified effect measure suggests increased risk & other suggests decreased risk, or one is null

A

Qualitative EMM

131
Q

IRRxy different from IRRy|not x * IRRx|not y; sub or supra

A

Multiplicative EMM

132
Q

IRDxy different from IRRy|not x + IRRx|not y; sub or supra

A

Additive EMM

133
Q

Difference of risk differences expressed as proportion of reference risk; measure of departure from additivity obtained from multiplicative models; RR11-RR01-RR10+1

A

Relative Excess Risk due to Interaction

134
Q

Prevalence of exposure decreases, different sensitivity/specificity pair, determined by specificity for low prevalence vs. sensitivity for high prevalence

A

Factors Increasing Bias

2021 Epi Comps (5 decks)

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