Exam 2 Flashcards

(43 cards)

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
Define exchangeability
treatment group and control group are functionally the same, so if you which them in your experiment you will get the same result
26
criteria for confounding
associated with the disease among the unexposed associated with the exposure in the source population not a consequence of the exposure
27
DAG
A: Exposure Y: Outcome L: Confounder -----------------> L. ----> A. Y
28
What method exists to control for confounding in the design stage
Randomization Restriction Matching
29
What method exists to control for confounding in the analysis stage
Standardization Stratified Analysis -Mantel Haenszel Multivariate regression analysis
30
IRRmh
write out
31
RRmh
write out
32
ORmh
write out
33
Interpret an adjusted measure of association
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
Magnitude of confounding equation
(RRcrude-RRadjusted/RRadjusted) x 100%
35
Positive Confounding
biased away from the null exaggerating the association
36
Negative Confounding
biased toward the null masking the association
37
Assumptions of the Mantel-Haenszel approach
the association is constant across strata no residual confounding within strata
38
Limitation of the Mantel-Haenszel approach
computationally rigorous need a very large sample size to have sufficient information in each RxC cell if we adjusted for multiple confounders
39
residual confounding
confounding that remains even after many confounding variables have been controlled
40
strengths of regression modeling
allows to adjust for multiple exposures quantifies the relationship (strength and direction) clear interpretability of covariates in the model
41
Regression used for continuous outcome
linear regression
42
Regression used for binary outcome
logistic regression
43
Assumptions of linear regression
Linearity: constant slope Independent outcomes Normally distributed residuals Constant variance