EBM Day 4 Flashcards

(51 cards)

1
Q

Cohort Study vs Case Control

A

Cohort-know exposure, look how exposure level effects disease-start with cohort who are exposed (at risk for disease)

Case control-know disease, what factors give rise to disease-start with people with/without disease

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

Case control OR vs Cohort RR

A

Exposed CasesxNot exposed Noncases/not exposed cases x not exposed non cases

Exposed Cases x Exposed noncases/nonexposed cases x non exposed noncases

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

When OR is what, good idea of RR

A

Low-looking at disease that is not found much in population

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

Case control studies facts (that cohort can do)

A
  1. can’t yield incidence rates

2. can not give risk ratios

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Case control strengths

A
  1. rare diseases
  2. diseases with long induction time
  3. explore wide range of exposures
  4. quick/cheap/easy/yields potential hypot
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

Bonferroni Correction

A

Correcting p values over multiple studies-leads to error

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

Random error, systematic error, bias

what do they look like

A

random error has points more spread out
bias moves points toward or further away from normal
systematic looks like a pattern

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

Oversampling

A

distorts odds ratio

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

Recall bias

A

Take unexposed or exposed and place in opposite group (because for recall)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Case definition traits

A

clear, specific, but not overly restrictive
misclassification if too broad
limited sample size if too strict

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

Should cases be incident?

A

Yes stop recall bias/less effect due to prolonged exposure

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

How to select controls

A

Should have same oppurtunity to have been exposed
-should be population risk at becoming a case
Should be sampled independent of exposure
- want people who are very similar except in exposure
-if not have selection bias

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

2x2 table

A

draw

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

Diagnostic/Workup Bias

A

Case selection influenced by physicians knowledge of exposure

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

Nested Case Control Studies

A

Select cases and controls from cohort study

More eficient- already have most info

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

healthy worker effect

A

People who work are more healthy then those who do not

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

Matching

A

control selection coupled with experimental selection to reduce confounding variables

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

More controls

A

Increase power until 4, then not worth

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

Non differential misclassification vs differential misclassification and what error they lead to

A

Exposure unrelated to disease (chance)
All different groups (variables) have equal rate of being misclassified
Leads to type II error

Different groups have unequal rate of being misclassified
Leads to type 1 or t2 error

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

Information bias

A

bias due to measurement error

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

How to minimize recall bias

A

Using records of exposure to disease, use incident cases, appropriate control, blind study, etc.

22
Q

Investigator bias and how to not have

A

Investigator does something like leading questions because knows exposure status he is looking for

Standerdized protocols, objective measurements, blinding

23
Q

Adv of case control studies

A

rare disesase, new disease, outbreaks, induction period is long, inexpensive, multiple expusres

24
Q

Disadv of case control studies

A

Bias (recall and misclassifcation), singe out come, inefficient if low freq, does not calculate incidence rate directly

25
Cross sectional vs Cohort vs case control
CS-sample population , no follow up, compare disease experience among groups in present Ch-identify exposed, follow exposed through disease course CC-identify disease cases and noncases, compare histories of past exposure
26
When to use regression
Looking for trend in data between two variables Microarrays Adjusting for confounding variables
27
When to use correlatoin
When don't know IV or DV | Examine relationship between two variables
28
Variance and which kind of tests assume equal
How far numbers are spread out | Parametric
29
R^2
Correlation coeffecient Amount of variability in Y contributed by x Meaningfulness of the correlation coefficient
30
Effect of outlier
Destroys parametric correlation because variances are unequal Nonparametric tests have no problem
31
How to deal with outlier
Drop it Log transform Leave it Nonparametric test
32
Different types of regressions
Linear-DV=continuous, IV=single and continuous, Nonlinear-DV=continous, IV=1 or more and continous MV-DV=continous, IV is continous or categorical Logistic=DV=categorical, IV is continous or categorical
33
MV analysis
Looking at multople variables at a time Allows simulataneous assessment of different variables and adjust for confounders Possibly look at interaction between terms
34
Stepwise regression
chance of getting something by doing this, then this and that, then this that and other thing
35
OR
odds ratio of dead person with condition/ | odds ratio of alive person with condition
36
Multiple logistic regression
Gives odds ratio for each independent variable | Adjusts for confouding
37
Logtistic Regressions
when outcome or dv is binary adjusts for confounding good for odds ratio in case controls
38
Principal Component Analysis
Takes many variables and reduces by regression ex. 100s of diet items (put into 3 categories) Couple with logistic regression to get odds ratio ex. 3x chance of getting cancer if only eat meat and fat
39
Zero time point (and examples)
start of study now date of randomization first MI
40
Median survival time
Half of sample reaches the event (death or discharge usually)
41
MI risks for first and second
They are same, and prevented same way
42
Can you use experience to say how long someone has to have an MI?
Not really, each patient has different propensity to mi
43
Equipose
genuine lack of consensus in the medical community about a treatment or prognosis -only way to have RCT on patient
44
Case fatality
percent of of patients with disease who die due to it
45
response
percent of patients showing some improvement following an intervention
46
Kaplan Meier Survival curve and CI and how to match?
x is usually month/year y starts at 100% alive, and decreases (cum probability to survive) Can draw CI and if one curve within CI of another curve, no difference PROPENSITY
47
Truncation
Entering study Event occurred before start of study Event occurred after start of study
48
Censoring
Leaving study Incomplete followup Event occurred and left study early
49
Kaplan Meier limitations
Does not handle co-variates (use proportional hazards)
50
Cox Proportional Hazards (Regression)
Hazad Ratio=risk ratio, can control or adjust for other factors (ex BMI and sex)
51
HR1
HR1 and CI no include 1=worse off