Principles Of Epidemiology Midterm

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Goals of epidemiology; what do they all require?

Back 1

describing disease in population, understanding underlying causes of disease, preventing disease

all require reliable & valid measurement

Front 2

Central tenet of measurement theory

Back 2

all scores equal the 'true' score plus measurement error

Front 3

About reliability

Back 3

higher measurement error => lower reliability
consequences of low reliability:
1) tendency to mask significance (i.e. biases studies towards finding no differences)
2) as a result, need larger sample size for sufficient statistical power

Front 4

two measures of reliability

Back 4

inter-observer agreement and test-retest reliability

Front 5

What's Cohen's Kappa? What is it for?

Back 5

Cohen's kappa is a measure of inter-observer agreement; equivalent to:

extent to which two measures agree beyond chance / maximum possible improvement in agreement

(% agreement observed - % chance agreement expected) /
(100%-chance agreement expected)

% agreement observed = (agreed '+' + agreed '-') / (total responses) i.e. ( a + d / (a+b+c+d) )

% agreement by chance expected = '+' agreement expected + '-' agreement expected

'+' chance agreement expected = ('+' from person A / total responses from person A) * ('+' from person B / total responses from person B)

Front 6

sensitivity

Back 6

the proportion of 'true' cases which the measure identifies as 'positive'

Front 7

specificity

Back 7

the proportion of 'true' non-cases which the measure identifies as 'negative'

Front 8

positive predictive value

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the proportion of positive-identified cases that are, in fact, 'true' cases

Front 9

negative predictive value

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the proportion of negative-identified cases that are, in fact, 'true' non-cases

Front 10

What do sensitivity, specificity, PPV and NPV have in common?

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they all are measures of 'criterion validity' i.e. of validity against a gold standard

Front 11

Outbreak, epidemic, pandemic

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All 3 describe 'elevated' levels of disease; outbreak is most localized geographically (i.e. county/city level), epidemic is more widespread and pandemic is global

Front 12

Epidemiologic Triad

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Vector is in the center; Environment, Agent, and Host are at vertices

Front 13

Attack rate & what influences it

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(# of exposed and sick)/(total # of exposed)

Some influencing factors: herd immunity, incubation period, virulence

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Sample, Population, Bias

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Population: everyone for whom you want to draw a conclusion

sample: representative sub-group (i.e. as congruently comprised as possible) of population

bias: when sample isn't representative of population

Front 15

prevalence, incidence, odds

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prevalence: # of cases / sample size
incidence: # of NEW cases / sample size
odds: # cases / # non-cases

Front 16

When comparing two adult groups, what is most important variable to adjust to reach sound conclusions? Why?

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Age is most important variable to adjust, because of physiological differences between people over chronological time

Front 17

Case-fatality rate, years of potential life lost, disability-adjusted life-years

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case-fatality rate = (# of dead from disease) / (# of people with disease in sample)

YPLL = Σ(65 - age of death); speaks more to early deaths

Disability-adjusted life-year: 1 year of healthy life lost to either death or disability

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Advantages of survival analysis

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1) accounts for censoring
2) addresses *time* until an event occurs

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Most Impt. thing to remember about survival analysis

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'cumulative survival' is multiplicative but the denominator of each proportion changes as people are censored/die

Front 20

Hazard Ratio

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relative risk integrated over time; used in survival analysis

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With respect to timing of measures, what are the three epidemiologic research designs?

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prospective (i.e. cohort), retrospective (i.e. case control) and concurrent

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Observational Study Vs. Experiment

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Experiment involves researcher intervention (i.e. is treatment-based); observational study only collects data

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Ecologic study

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exposures unlinked to outcome case-by-case, usually uses incidentally collected data (i.e. city-wide, country-wide, company-wide etc)

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Cohort study

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samples on *risk factor* and follows forward for outcome; no need to oversample for rare risk factors because oversampling is inherent

well-suited for relative risk because relative risk describes chance of *developing* disorder

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Case-control study

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sampled on *outcome*, tracks back through survey or records to risk factor; can oversample for rare outcomes (but is hard to deal with mathematically)

well-suited for odds ratio because OR describes odds of *having been exposed* given that you are disordered

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Relative risk

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describes relative chance of developing disorder;

( incidence in exposed) / ( incidence in unexposed)

(a / (a+b) / c / (c+d) )

Front 27

Odds ratio

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describes odds of having been exposed to risk factor

(odds of past exposure among diseased) / (odds of past exposure among healthy)

(a/c) / (b/d)

Front 28

Attributable risk; Population attributable risk

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excess cases among exposed (i.e. risk BEYOND background risk);

AR = incidence in exposed - incidence in unexposed

PAR = AR * prevalence of risk factor in a population

answers the question "how much could the incidence of this disease in pop. be reduced if risk factor eliminated?"

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Null hypothesis

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in population, there is no association or group difference

Front 30

Type I; Type II error

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Type I error: rejecting true H0 (i.e. false conclusion of difference)
Type II error: accepting false H0 (i.e. false conclusion of no difference)

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p-value

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the probability of obtaining the test statistic in question *if H0 is true*

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95% confidence interval

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interpreted as '95% probability that true value lies within bounds' -- really means that in repeated sampling value would appear within this interval 95% of the time

Front 33

determinants of statistical power

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# Effect size
# Prevalence of outcome and exposure
# Significance level (p)
# Reliability of measures
# Number of covariates (measured confounds)