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BMS I > EBM Day 4 > Flashcards

EBM Day 4 Flashcards

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

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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

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3
Q

When OR is what, good idea of RR

A

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

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4
Q

Case control studies facts (that cohort can do)

A
  1. can’t yield incidence rates

2. can not give risk ratios

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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
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6
Q

Bonferroni Correction

A

Correcting p values over multiple studies-leads to error

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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

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8
Q

Oversampling

A

distorts odds ratio

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9
Q

Recall bias

A

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

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10
Q

Case definition traits

A

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

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11
Q

Should cases be incident?

A

Yes stop recall bias/less effect due to prolonged exposure

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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

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13
Q

2x2 table

A

draw

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14
Q

Diagnostic/Workup Bias

A

Case selection influenced by physicians knowledge of exposure

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15
Q

Nested Case Control Studies

A

Select cases and controls from cohort study

More eficient- already have most info

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16
Q

healthy worker effect

A

People who work are more healthy then those who do not

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17
Q

Matching

A

control selection coupled with experimental selection to reduce confounding variables

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18
Q

More controls

A

Increase power until 4, then not worth

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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

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20
Q

Information bias

A

bias due to measurement error

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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
Q

Cross sectional vs Cohort vs case control

A

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
Q

When to use regression

A

Looking for trend in data between two variables
Microarrays
Adjusting for confounding variables

27
Q

When to use correlatoin

A

When don’t know IV or DV

Examine relationship between two variables

28
Q

Variance and which kind of tests assume equal

A

How far numbers are spread out

Parametric

29
Q

R^2

A

Correlation coeffecient
Amount of variability in Y contributed by x
Meaningfulness of the correlation coefficient

30
Q

Effect of outlier

A

Destroys parametric correlation because variances are unequal
Nonparametric tests have no problem

31
Q

How to deal with outlier

A

Drop it
Log transform
Leave it
Nonparametric test

32
Q

Different types of regressions

A

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
Q

MV analysis

A

Looking at multople variables at a time
Allows simulataneous assessment of different variables and adjust for confounders
Possibly look at interaction between terms

34
Q

Stepwise regression

A

chance of getting something by doing this, then this and that, then this that and other thing

35
Q

OR

A

odds ratio of dead person with condition/

odds ratio of alive person with condition

36
Q

Multiple logistic regression

A

Gives odds ratio for each independent variable

Adjusts for confouding

37
Q

Logtistic Regressions

A

when outcome or dv is binary
adjusts for confounding
good for odds ratio in case controls

38
Q

Principal Component Analysis

A

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
Q

Zero time point (and examples)

A

start of study
now
date of randomization
first MI

40
Q

Median survival time

A

Half of sample reaches the event (death or discharge usually)

41
Q

MI risks for first and second

A

They are same, and prevented same way

42
Q

Can you use experience to say how long someone has to have an MI?

A

Not really, each patient has different propensity to mi

43
Q

Equipose

A

genuine lack of consensus in the medical community about a treatment or prognosis
-only way to have RCT on patient

44
Q

Case fatality

A

percent of of patients with disease who die due to it

45
Q

response

A

percent of patients showing some improvement following an intervention

46
Q

Kaplan Meier Survival curve and CI and how to match?

A

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
Q

Truncation

A

Entering study
Event occurred before start of study
Event occurred after start of study

48
Q

Censoring

A

Leaving study
Incomplete followup
Event occurred and left study early

49
Q

Kaplan Meier limitations

A

Does not handle co-variates (use proportional hazards)

50
Q

Cox Proportional Hazards (Regression)

A

Hazad Ratio=risk ratio, can control or adjust for other factors
(ex BMI and sex)

51
Q

HR1

A

HR1 and CI no include 1=worse off

BMS I (30 decks)

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