Stat Flashcards

1
Q

odds ratio<1

A

decreased risk

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2
Q

odds ratio=1

A

equal risk

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3
Q

odds ratio>1

A

increased risk

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4
Q

odds ratio= relative ratio

A

outcome is rare

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5
Q

to strengthen the argument for causality, consider (7)

A

consistency, plausibility, dose-response, temporality, strength of relationship, reversibility, lack of alternative explanations

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6
Q

types of descriptive studies (3)

A

detail one observation

ecologic study, case reports, case series

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7
Q

case reports

A

○ One or few patients
○ Link clinical medicine and public health
○ Publications and rounds
○ Rare disease/cases

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8
Q

case series

A
More than a few patients
Good details
CONS
Small, highly selected group
	No hypothesis
	No comparison
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9
Q

Ecologic Study

A

Ecologic Study
○ Goal: comparing disease rates between population groups
○ Exposure (predictor or risk factor) —> disease (outcome or response)
○ “ecological correlation” or “aggregate risk” = exposure-outcome relationship
○ Suggests a link associated with a group
○ Ex) countries with higher fat diets = higher breast cancer rates

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10
Q

Ecologic Study: pros

A

Etiological hypothesis
Use to set research priorities
Low cost
Study large population

Studies hard to study environmental health questions

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11
Q

Ecologic Study: cons

A

No individual data
“ecological fallacy”
One could infer inappropriate individual relationship
**be careful not to over-interpret results

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12
Q

Analytical Study types (3)

A

cohort, case-control, cross-sectional study

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13
Q

Case-control study

A

*rare diseases
outcome –> exposure
○ Moves backwards in time
○ Find those with disease and look back at their exposure
○ Controls: from at risk population (had opportunity for exposure/disease), but free of disease at time
○ Odds ratio: estimates risk

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14
Q

Case-control study: pros

A

Study rare or long latency diseases

Requires few subjects

Faster, Less time

Evaluate multiple exposures (risks) as potential causes of disease

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15
Q

Case-control study: cons

A

Relies on subject’s recall for past exposures; biases

Difficult to select appropriate control group

Odds ratio only estimates relative risk

Cannot calculate incidence rates

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16
Q

Cross-sectional study

A

*quick measure
exposure and outcome at same time
○ “prevalence study”
○ Ex) who is more dissatisfied with weight: male or females?

Prevalence ratio

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17
Q

Cross-sectional study: pros

A
Good measure of disease prevalence
What to expect in clinical setting
Evaluate screening and diagnostic tests
Help plan health services
Quick- ask one question
Easy
Inexpensive
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18
Q

Cross-sectional study: cons

A
Measure disease/exposure at same time
Cannot determine causality
Cannot determine temporal relationship of exposure and disease
Limited: study prevalence only
Cannot determine disease incidence
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19
Q

Cohort study

A

*rare exposures
exposure—> outcome
○ Moves forward in time
○ Follow patients over time to see if they develop disease
○ Compare incidence of new development of disease
○ Ex)is physical fitness related to respiratory illness risk

assesses relative risk, attributable risk

20
Q

Cohort study: pros

A

An evaluate multiple outcomes

Provide actual measure of risk of outcome

Can extract incidence and relative risk

Approximates Random control design

21
Q

Cohort study: cons

A

Potential loss for follow-up

Needs large number of subjects

Takes a long time- not efficient to wait for outcome

Expensive, lots of staff

22
Q

Randomized clinical trial

A
*best evidence
Experimental Study
• Randomly assign participants to one or two treatments
• Produce comparable, similar study groups (equal known/unknown risk factors)
• Removes investigator bias by allocating participants  randomly
• Valid statistical tests
• Comparison groups:
	○ No intervention
	○ Observation- Hawthorne effect
	○ Placebo
	○ Usual care
• Blinding/ masking
23
Q

define: correlation

A

measures strength of association btwn 2 variables

24
Q

define: regression

A

method for relating predictor to outcome

25
Q

Q: “does an association exist”

“quantify the strength of the association”

A

find correlation

26
Q

Q: “use the relationship to predict”
“does the observed relationship agree with this theory”
“estimate the parameters of this model

A

find regression

27
Q

how to measure linear association

A

correlation coefficient

28
Q

steps in using correlation coefficient (4)

A

1 observe (x,y) variables for random sample
2 plot pairs of points in scatter plot
3 find pattern of association
4 estimate population correlation coefficient (p)

29
Q

correlation coefficient: range

A

-1 < r < 1

30
Q

correlation coefficient: r=0

A

no linear association

loose clustering

31
Q

correlation coefficient: r= 1

A

perfect positive linear association

tight cluster

32
Q

correlation coefficient: r= -1

A

perfect negative linear association

tight cluster

33
Q

when to use standard pearson correlation

A

random sample

normal distribution

34
Q

when to use spearman rank correlation

A

decrease influence of outliers

ranks variables low to high and recalculates

35
Q

“least squares” regression line

A

minimizes the sum of squared differences from best fit line

y= A+ Bx
y= (intercept) + (slope)x
36
Q

when to use multiple linear regression

A

adjust for cofounders

when outcome is continuous

37
Q

simple logistic regression: use

A

estimate odds ratio

when dependent variable is categorical (binary)

38
Q

multiple logistic regression: use

A

estimate “adjusted” odds ratio

for multiple predictors, binary outcome

39
Q

to assess association between 2 continuous variables use…..

A

correlation or linear regression

40
Q

to assess:

  • association between continuous (or categorical) predictor variables
  • estimate odds ratios for categorical (binary) outcome
  • odds ratios after adjusting for other variables
A

logistic regression

ex) BMI & High BP
ex) age & anemia

41
Q

estimate a regression line for a curve shaped scatter diagram?

A

linear relationship is unlikely
correlation coefficient ~0
computation of simple linear regression is contraindicated

42
Q

What kind of study is appropriate for an outcome that is rare?

A

Case-control

43
Q

Stratification

A

divide total sample into subgroups to deteremine odds ratios

44
Q

Matching

A

manipulate study to directly compare factors you think are biasing
e.g., exposed old ppl to control old ppl

45
Q

Adjustment

A

use regression models (odds ratios)