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Epi Methods > Exam 2 > Flashcards

Exam 2 Flashcards

1
Q

Define case-control study

A

A study in which cases of disease are identified, and then the sample of source population that produced the cases is identified

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2
Q

Purpose of case-control study

A

assess whether exposure is disproportionately distributed between the cases and control

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3
Q

Strength of case-control study

A

less time and less expensive
small sample size
compare multiple exposures
useful for rare exposures

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4
Q

Limitation of case-control study

A

can’t determine incidence, prevalence or causality
recall and selection bias
not useful for rare exposure

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5
Q

criteria for selecting controls in a case-control study

A
  1. controls must come from the same source population as the cases
  2. controls must be selected independently of the exposure
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6
Q

Calculate Odds

A

Pr[Y=1]/Pr[Y=0]

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7
Q

Odds of disease among exposed

A

Pr[Y=1IA=1]/Pr[Y=0IA=1]
a/b

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8
Q

odds of disease among unexposed

A

Pr[Y=1IA=0]/Pr[Y=0IA=0]
c/d

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9
Q

interpretation of odds ratio

A

the odds of death in those living in a high pollution city is 1.65 times higher than the odds of those in a low pollution city over the 15 years of follow up

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10
Q

Calculate a 95% confidence interval of an odds ratio

A

write out

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11
Q

Define a randomized control trial

A

must have a large enough population……
the control group and the active treatment group will have similar characteristics at the time of random treatment assignment, the only difference between the groups at baseline is the treatment assignment

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12
Q

Purpose of randomized control trial

A

ensures the exposed and unexposed groups are exchangeable at time of randomization in terms of measured and unmeasured variables

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13
Q

strengths of randomized controlled trials

A

Reduces sources of bias and/or internal threats to validity concerns
Can determine cause and effect

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14
Q

Limitations of randomized controlled trials

A

costly and time consuming
treatment not well defined
noncompliance
participants and investigators may not be blinded

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15
Q

Kaplan-Meier curve

A

help look at risk over smaller times chunks, which mitigate issues with estimating risk when the population has loss to follow up

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16
Q

Intention to treat analysis

A

compare the incidence of outcome in those randomly assigned to treatment vs control, regardless of the treatment they completed or received

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17
Q

Benefits of intention to treat analysis

A

gives real-world estimate on treatment effectiveness under practical conditions where people do not always comply with their treatment assignment

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18
Q

As treated analysis

A

compare the incidence of the outcome in those ACTUALLY treated with A=1 and in those actually treated WITHOUT treatment A=0

19
Q

Benefits of as treated analysis

A

provides a more realistic estimate of the effect of treatment in a real world setting

20
Q

limitation of as treated analysis

A

the treatment group is no longer exchangeable because randomization is not preserved
could introduce bias

21
Q

efficacy analysis

A

Compare incidence of disease among those actually treated with treatment who were assigned to treatment with those who were not treated with treatment and who were assigned to no treatment

22
Q

benefit of efficacy analysis

A

reduce dilution of treatment effect
reflective of efficacy in ideal conditions

23
Q

limitations of efficacy analysis

A

treatment groups are no longer exchangeable because randomization is broken
often over estimate the benefit of the treatment

24
Q

define confounding

A

systematic differences between the exposure groups being compared that distort the true association between exposure and disease because a 3rd variable is:
1) risk factor for the disease
2)is unevenly distributed across exposure levels
3)AND is not consequence of the exposure

25
Q

Define exchangeability

A

treatment group and control group are functionally the same, so if you which them in your experiment you will get the same result

26
Q

criteria for confounding

A

associated with the disease among the unexposed
associated with the exposure in the source population
not a consequence of the exposure

27
Q

DAG

A

A: Exposure
Y: Outcome
L: Confounder
—————–>
L. —-> A. Y

28
Q

What method exists to control for confounding in the design stage

A

Randomization
Restriction
Matching

29
Q

What method exists to control for confounding in the analysis stage

A

Standardization
Stratified Analysis
-Mantel Haenszel
Multivariate regression analysis

30
Q

IRRmh

A

write out

31
Q

RRmh

A

write out

32
Q

ORmh

A

write out

33
Q

Interpret an adjusted measure of association

A

the (measure of association) in the exposed group was (magnitude) times (lower/higher) than the (measure of association) in the unexposed group after adjusting for (confounder) categories

34
Q

Magnitude of confounding equation

A

(RRcrude-RRadjusted/RRadjusted) x 100%

35
Q

Positive Confounding

A

biased away from the null
exaggerating the association

36
Q

Negative Confounding

A

biased toward the null
masking the association

37
Q

Assumptions of the Mantel-Haenszel approach

A

the association is constant across strata
no residual confounding within strata

38
Q

Limitation of the Mantel-Haenszel approach

A

computationally rigorous
need a very large sample size to have sufficient information in each RxC cell if we adjusted for multiple confounders

39
Q

residual confounding

A

confounding that remains even after many confounding variables have been controlled

40
Q

strengths of regression modeling

A

allows to adjust for multiple exposures
quantifies the relationship (strength and direction)
clear interpretability of covariates in the model

41
Q

Regression used for continuous outcome

A

linear regression

42
Q

Regression used for binary outcome

A

logistic regression

43
Q

Assumptions of linear regression

A

Linearity: constant slope
Independent outcomes
Normally distributed residuals
Constant variance

Epi Methods (1 decks)

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