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55 Cards in this Set
 Front
 Back
Mean

average


Median:

middle value


Mode:

most frequent value


Endemic:

localized epidemic


Pandemic:

world·wide epidemic


which of the following central tendency measures is sensitive to outliers?

mean


which of the following central tendency measures is used in a skewed population?

median


Variability:
what are the different standard deviations and their percentage? 
1 STD: 68%
2 STD: 95% 3 STD: 99% 

Chi square

compare percentages


T test

compare 2 things


ANOVA

compare 3+ things


Skewing:

shift to right (mean> median > mode)


Standard Error of Mean

= S/ (square root) of N => precision of mean
S = standard deviation, N = sample size 

1 > 2 Question Survey:
what will happen to sensitivity? NPV? specificity? and PPV? 
Decrease: Sn, NPV
Increase: Sp, PPV 

PPV
equation effect on prevalence example 
= TP / all positives
(increases w / prevalence) Ex: ( +) ELISA > Do you have HIV? 

NPV
equation define 
= TN/ all negatives
(probability of not having a dz if have negative test) 

Sensitivity

= a/ a+c = TP /all diseased (people that have dz)


Specificity

= d/ b +d = TN/ all nondiseased (people that don't have dz) "


Incidence
equation 
= new cases/ total population


Prevalence= equation
example 
= all diseased/ total population
(Ex: improved quality of care) 

Odds Ratio= equation
what does it mean in layman's terms • OR <1 => • OR >1 => 
Odds Ratio= (ad+ bc) cross product
(diseased are x times more likely to see risk factor) • OR <1 => protective factor • OR >1 => risk factor 

Correlation Coefficient:
• 0: • +: • : 
• 0: nothing
• +:correlation • : negative correlation 

CI

= 95% => 95% sure it lies within the interval (cannot include 1.0)


RR= equation
explain in layman's terms 
= exposed/ unexposed
(risk of getting dz w/ known exposure) 

define NNT
p value < 0.5 
= number needed to treat to change 1 life
p value < 0.5 = random chance that you will be wrong 1 time out of 20 

Null hypothesis

=> nothing's happening


Power:
equation explain in layman's terms 
1β = probability of detecting a true intervention


what is improved by ⇧size of study

power


Effect size

= how different two groups are


α type I error

(FN), P value error "too optimistic''


β: type II error

(FP), Power error "too pessimistic"


what is the cause of type β: type II error?

small samples


Prevention:
1°: 2°: 3°: 
Prevention:
1°: ⇩Incidence 2°: ⇩Prevalence 3°: Slows disease progression 

Accuracy:

validity "truth"


Precision:

reliability "keep making the same mistake"


Admission rate:

hospitals have certain populations


Confounding bias:
define what are the 3 things that should be done to prevent this 
forgotten variable => use matching, restriction, randomization


Lead time:

time between diagnosis and treatment


Hawthorne effect:

the "watched" change their behavior


Unacceptability:

subjects lie


Observer bias:

the observers have knowledge about control and study samples


Recall bias:

inaccurate recall of past events


Respondant:

subjective diagnosis


Sample distortion:

sample does not represent population


Selection

who is in or out


I) Clinical Trials: experimental
Phase I: Phase II: Phase III: Phase IV: 
Phase I: Toxicity "burt pt?"
Phase II: Efficacy "help pt?" Phase III: Comparison "any better?" Phase IV: Postmarketing surveillance "can they screw it up?" 

Cohort study:
define what happens in a cohort study? what does it determine? 
Prospective
Observe a "cohort" (group of people with similar characteristics) to see how many develop a specific disease after exposure to a risk factor. • Determines incidence: new cases of disease 

Cohort study:
what risk it uses occurs where ex 
• Uses Relative Risk
• Occurs in community • Ex: finds new cases of common disease 

Case control:
type of study what happens in a case control what does it use? where does it occur? bias examples 
Retrospective
• Select subjects with a disease, compare to controls, and study the differences • Uses Odds Ratio • Occurs in hospital • Has more selection bias • Ex: finds risk of developing rare disease 

Cross sectional:
define what does it determine example 
snaphot in time
• Determines prevalence: total cases of diseased • Ex: polls/ surveys 

Case report

describe an unusual pt


Case series report

describe several unusual pts


Consensus panel

panel of experts provides a recommendation


Clinical wisdom

"I think .... "


MetaAnalysis

tries to combine data from many trials
