Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

Biostats > Biostats > Flashcards

Biostats Flashcards

1
Q

Post test probability
Pre test prob is 10%
liklihood ratio is 16

A

Post test probability
10% pre test porbablity = 1:9 odds
16 is lilihood ratio

16+9=25

16/25= post test proabiliy

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

Minors <18 yrs old excpetions to needing parental consent

A
  1. married
  2. preggo
  3. finacially indpendent
  4. riaisng kid
  5. living by theirself
  6. in the army
  7. OCPs or Pregnancy Tx
  8. STD
  9. Psych illness

*need parental consent for abortions

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

difference in comeptency vs capaacity

A

Competent= legal by court
-permanent is dementia and someone appointed b court ot make decisions, dleirum is temporary
capacity = medical by me

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

break of confidentiality (2)

A
  1. hurt themselves or others

2. Abuse

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

termnally ill patients make sure you prescribe

A

LOTS of opiods

Make sure to rpeort HIV aids, syph, measles all the good stuff

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

withdrawal of care anf amily disagrees with patient?

A

seek out ethics comittee

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

High sensitivity but low spec= False ____

High spec but low sens= False____

A

Positive

Negative

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q 
95% sens 95% spec 
Low prevalence of disease 1%
10%
50%
90%
A

1- not helpful
2- Kinda helpful, negative result reduces lieklihood and positve test says it needs more tests
3- Very helpful
4- not helpful- positive adds ntohign and negative is a false negative

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

Disease present Disease absent
+/exposure A B

-/no exp C D

A

Know it

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

Specificity Equation

A

D/ D+B

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

PPV equation

A

A/ A+B - liklihoood a positive result is positive
High prevelance== higher PPV\
Need cohort study not case control- incidence needs to reflect he population

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

NPV equation

A

D/ C+D - liekly hoood the negaitve result is disease free
Lower pevelance = higher NPV
Need cohort study not case control- incidence needs to reflect he population

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

Incidence define

A

new cases per year

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

prevalence define

A

total number of existing cases in a population at certin snap shot of time

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

Absolute risk define

A

PROBABILTY of n event occuring over a time frame

1% chnce of getting Disease X in 5 years

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

Relative risk define

A

COHORT porspective studies comapring groups exposed to a risk factor or not
RR<1 in event less liekly in exposed group
RR>1 exposed group more likely to have event

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

Odds ratio defined

A

CASE CONTROL studies, RETROSPECTIVE studies.

it compares rate of epxosure to those with and without disease. Less accruate than OR

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

Relative rate equation

A

a/a+b /// c/c+d

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

Odds ratio equation

A

AD/BC

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

Absolute risk reduction define and equation

A

Risk from exposure to something + Background of disease.
RANDOMIZED CONTROL TRIALS. BEST EQUATION AND WAY BETTER THAN RRR
Absolute risk (adverse events) in placebo group - absolute risk in treated patients

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

Absolute risk reduction define and equation

A

Risk from exposure to something + Background of disease.
RANDOMIZED CONTROL TRIALS. BEST EQUATION for benefit AND WAY BETTER THAN RRR

Absolute risk (adverse events) in placebo group - absolute risk in treated patients

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

RRR

A

RADNOMIZED CONTORL TRIALS
ratio between 2 risks
Ratio and can look deceptively large

event rate in control - event rate in experimental patients /// event rate in control

Or Unexposed-exposed///unexposed

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

Example of ARR AND RRR

reduction of adverse event is 0.01% to 0.004%

A

ARR 0.01-0.004 = 0.006%

RRR 0.01-0.004///0.01= 0.6 or 60%!!!

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
24
Q

NNT define and equation

A

number of patients ot be treated to prevent 1 adverse event

1/ARR

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
25
Q

p value defined

A

less than 5% chance that is was by chance or there is a5% chance the null hypothesis that there is no association holds true

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
26
Q

CI defined

A

Like p value, is this real or is it by chance. It says that there is a 95% chance that the observed risk will fall between a certain range.
It uses OR and RR
If CI includeds 1 it is not cliniclaly significant
if it is 1.9 but CI is .8-3 it is not significant

RR is 2 and CI is 1.2-3.5 then there is OBSERVED 2x risk for smoking to cause cancer but in reality the ACUTAL risk is somewhere between 1.2-3.5 X the risk if you were to smoke

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
27
Q

Prospective studies

A

future outcomes

control for biases

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
28
Q

retrospective

A

past events

less reliable

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
29
Q

cohort study

A 
population observed overtime
grouped on basis of exposure to a factor and watching for a psecifci outcome
*NOT good for rare conditions
**USE RR to intepret results
either pro or retro can work
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
30
Q

Case ocntrol study

A

Retorspective study
look at ppl with a disease and without the disease and look fro exposure to risk factors
*Good for rare diseases
**USE OR to intepret resutls

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
31
Q

Randomized contorl trials

A

prospective study to randomly assign ppl to treatment or placebo group to see if tx made a difference
DBL blindi s gold standard

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
32
Q

Crude mortality rate

A

Deaths/total population

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
33
Q

Cause specifc mortalty rate

A

Disease related deaths/total population

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
34
Q

Case fatality

A

Deaths/#ppl with disease

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
35
Q

Standard mortality ratio

A

Observed deaths over expected detahs. If it is 2 then the partcualr group is twice is high as the genreal population

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
36
Q

Crude birth rate

A

Live births/Total population size

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
37
Q

Maternal mortlaity rate

A

Deaths/live births (not fetal losses)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
38
Q

DO suicidie contracts work?

A

No get guns out of the home

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
39
Q

Alcohol abuse, anemia, old due, MCV low, no Abd pain- first step colonsicpopy or EGD?

A

Colonoscopy- cancer first then EGD if negative

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
40
Q

WHat is an Anylsis of variance used for>?

A

MEan comparison between 3 groups. Itgivee F statisitc for variation between the groups

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
41
Q

Mcnemar test

A

difference between 2 paired proportions - the patients serve as their own control (success/failure before and after the treatment in the same subjects)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
42
Q

pearson chi square

A

association between categorical variables

Success/failure in MAle/feamle

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
43
Q

fischers exact test

A

small sample size for pearson chi squared

success/failure in <20 patients etc

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
44
Q

paired t test between 2 means- patients as own control

A

Ex- mean BP before and after treatment

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
45
Q

Correlation coefficiant

A

-1 to 1 and 0 is null
NO causality
Weak association the closer it gets to 0

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
46
Q

Difference between ARR and RRR and RR

A

RRR is proportion or percentage
ARR is difference
RR is the increased likelihood of the risk in (un)exposed

.0018 and .0013
ARR is .5
RR is 1.38 or 38% more liekly
RRR is 28%

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
47
Q

smaller sample size will make what type of error?

A

type 2 error

48
Q

what makes a type 1 error

A

significane of the testing? large the alpha the mor eliekly a type 1 error

49
Q

when does erly termination happen in a study

A

overhwlimgin positive or negative result

50
Q

what is outocme musclassifcation

A

if the end point is to determine if you have hcancer but you cant accurately test for that cancer

51
Q

If you need a higher power for a study do you need a larger or smaller sampe size?

A

Larger

52
Q

If you have an alpha of .01 and you rise it .05 what does that do to sample size need?

A

nothing, just accpeting more chance becuase alpaha = p value

53
Q

If you expect a intervention to cause a 50 effect on glucose but really it oly effects it by 30 will you need more or less sampe size?

A

more, smaller effect takes more ppl to accurately detect it

54
Q

difference in 1 tail vs 2 tail test

A

1 tailed test have more power and fewer subjects needed bc it only olooks in one direction, did the intervention increase or decresae it but not both

55
Q

Prospective study to ID risk of getting a disease in a group in 1 year- RR or OR
Retrospective to see if a Pill caused a disease- OR or RR

A

RR

OR

56
Q

If there is a -.8 correlation factor what is the coeffiecient of deteremination to tell you the variablity of the outcome factor?

A

.64

-.8 *- .8

57
Q

Correlation

A

Scattered but in one direction= weak

straight line but not up or down= 0 correlation

58
Q

30 out of 60 (.5) smokers vs 10/40 (.25) non smokers
Attributable risk
PErctange of attributable risk

Percatage of entire population from smoking?

A

.5-.25= 0.25
0.5 - .25///.5 = 0.5

40/100-10/40= .15&raquo_space;».15/.4-0-.375

59
Q

NNT

NEw drug survivial is 25/50 vs old drug 25/100

A

50% mortality and 75% mortality
.75-.5-.25
1/.25= 4!!!

60
Q

Need to look at bimodal distrubtion of spe and sens curves

A

False positive an dpalse negative values is the little area around the cutoff point

61
Q

90& sensitivity means how many false negatives

A

10%

true positive and false negative for all that have the disease

62
Q

RR of 2 w/ 95% Confidence interval 1.5-2.5

A

this means that we are 95% confident that our true value lies between these value ranges and the relative risk is 2x greater for the intervention group.
p would then be <0.05
if .4-1.2 range this includes 1 and means there is a greater than 0.05 risk for this to happen and not statisically significant
if 0.74 then a decresaed risk for RR

63
Q

CI of 0.5-3.8 means

A

Sample size is too small

64
Q

95% CI equation

A

Mean * 1.96*SD//// Sq Rt of N

CI will narrow and SEM will narrow with a alrger sample size

65
Q

Look at the sens and spec curves

A

the little area over the cutoff line is the sens and spec values, tighter = better. Left is sensitive usually

66
Q

Liklihood ratio vs predictive values- test perfromance values

A

Predictvie values change based on prevalence
liklihood of 9 for a test means 9X more liekly to have a psotive result than that who do not have the disease.

Sensitivity/ (1-specificity)

67
Q

look up ROC curves

A

you want them to be close to 1 area under the curve so it to go up to the top and then over
- The higher you go on the Y axis the more sensitive it is, so the cut off value is lower to get more +s

  • do if you get a negative reuslt it is mroe liekly to be negative
68
Q

Predictive value equations

A

Used for post test probability
A/(A+B) PPV
D/(C+D) NPV

69
Q

80% have positive tests that have the disease 10% have positve test that dotn have the disease. Prevalence is 10%. what is PPV?

A

sens=.8 spec=.9
(sensitivityXprevalenc)x[sensXprevelence)+(1-spec)(1-prev)]
0.80.1 / (0.80.1) + (0.10.9)= 0.47 PPV

ORRRRRR- plug in numbers for 100 ppl
out of 100 ppl, 10 have the disease and 90 dont. Of the 10, 8 will be +s and of the 90 who dont 9 will be false negative (10% or what is leftover on specficity, if specifcity is 70 than it would be 30% of 90)
8/(8+9)

70
Q

As specifcity goes up, the false positives go down and the PPV is stronger.

A

(even if snesitivity is low)

71
Q

length time bias vs elad tiem bias

A

Lead time: you detect a disease after its course is already set and no intervantions chnages the outcome but it appears like survival is longer

Length time: you catch a less aggressive form of the disease so it appears like better surivival

72
Q

Selection bias

A

Berkson fallacy- hospitlaized patient

Referral-sampled from specifc medical centes

loss to follow up in ochort studies

non respoense bias if refuals isrealted othealth

prevalence bias or Neyman-incidence is based on prevalnce. Dm2 of MI vs no DM2 and MI. the DM2s who died with MI cant report their incidence.

suspecitpbility bias- Severity of the patients tocndition interfers with treatmnet they are offered to skew results

73
Q

Hazard ratio

A

similar to relative risk.
Null is 1 or baseline risk.

It ooks at meaure of eefecr in surival or tiem-toevent analysis.

Example- MI risk following surgery.

74
Q

Confounders

A

Must link it to the exposure! and! the outcome! of itnerest

Use statify analysis to tkae out the confounder

75
Q

ways to reduce confoudning

A

randomize
or match- Use race, age, geogaphicla location to match u the groups as best you can

you can restrict - or set inucsuoin cirteria but it reduces generalizbailtiy to the population

76
Q

effect modification

A

Basically is confoudning but it is a natural phenomena that can tbe corrected in the study design.
the exposure of interest is modfiied by another variable

no flaws in design or anayals

OCPs and breast cancer - family hx of breast cancer
Asbestos and lung cancer- smoking
estrogens and DVT- smoking

77
Q

Randomization

A
  1. minimizines confounding
  2. simialr distributions of gorups
  3. only the interbention is being looked at
78
Q

blinding

A

Lowers placebo and observer bias

79
Q

Intention to treat odel

A

Preserves randomization and prevent non complaince bias
COntorl gorup starts taking the med later or the interbention gorup stops taking the med- this model will look at thier treatment staus at the momen when randomized

80
Q

Deviations

A

68% within 1 stanrard deviation each way
95% (2.5% on each side!)
99%
Mean, median and mode are very close to eachother

81
Q

Z score

A

Subtract the mean from all values and divide by SD

“how many SD a given value is from the mean”

82
Q

Skewed distributions

A

Tail to RIght is postive skew
Mode=peak
Median= to the right of mode
Mean= to the very right because it is affected by hihg values

83
Q

Routine labs

A 
CBC
BMP
UA
Lipid
EKG
low salt
low calorie/fat
exercise
alcohol
smoking

6 months of this then HTN meds

84
Q

If group A has a relative risk 0.4 compared to B, what is the increased that gorup B has?

A

2.5 increased risk

1/0.4

85
Q

Correlation

A

-1 to 1 range

r=.99 vs .3 shows a big difference in .99 has a greater positive preidctive facotr

86
Q

You need to look at your fav photos for types of tests

A

Chi squared is fro cateogrized proportions - like high or low with CRP or No CRP.
paired t test uses same individual like BMI now, take adrug, and BMI after in that person (even tho sample size is larger than 1

87
Q

difference between smiths and colles fracture?

A

Colles FOOSH- radius shaft is volar to hand

Smith- hyperlfexion fall on it- radisu shaft is dorsal to hand (opposite mechanism)

88
Q

Difference in confoudning and effect modifere

A

Confounding will effect both exposure and outcome but when you stratify the groups there is no association now
(shoe size and IQ are linked until you stratify or age ane now there is no link)

Effect modifier- positive or negative impact from another variable, when you strafity you will see association between one group and not the other

89
Q

Survival analysis for months after chemo starting probability of making it 3 months?

A

take each interval times each other. 10% died the first monthm then 8%, then 6%
.9.92.94=

90
Q

2 year mortality is the same for tx and placebo group but the treatment gorup is said to be effective, why?

A

End poitn is too long- time to event analysis

median lifespan is still 6 months longer for tx group

91
Q

latency period

A

Talks abotu chornic diseases (time from getting it to outcome is longer so no difference and then a diffrence after years is due to latency)

92
Q

systematic error is not effected by…

A

sample size
if someone else came in and did the exact same study they would achieve the same error results
compormises the vlaidity of the study

93
Q

2/3 docotrs diagnose PNA, which means ____ is affected

A

reliability

94
Q

Type 1 vs type 2 error

A

type 1- wrongful association- Alpha or p value
pvalue of .03 menas still a 3% chance no associaiton

type 2- Syaing no association when there is - Beta or 1-B is th epower of the study (proability of detecting an aosscaiton if it exists)

95
Q

How to increase power of study (3)

A

power=odds to see treatment effect if there is one

  1. increase sample size
  2. difference in outcomes is large and nto subtle
  3. lower the alpha lower the power
96
Q

standard mortality ratio

A

interst population/standard population

if 1.75 than 75% hihger than expected

97
Q

class 2 3 4 shcok

A

1 no change
2 tachy over 100
3 blood pressure drops
4 rr and all go up, 140 Tahcy

98
Q

Whatis Q and I2?

A

Used ofr heteorgenity, MIld vs Moderate alzheimers to see if differences between them
Q=think of it as P value. Greater than .05 then no heterogeneity
and I2 of 0% is no heterogenity, 25% is low amoun t, 50% mod and 75% high amount of heterogneity

heterogenity- just looking to see if there is a difference btween the two groups (used in a big anaylsysi )

99
Q

Quality adjsuted years vs disabiltiy years

A

Quality is individual or little poutilaion
living 5 years like this is liek my revious living 1 year

Dislability is global burden- iving in perfect helthu up to stsndard life expectsncey age. Current situiotn vs ideal situaiotn

100
Q

WHat is multiple linear regression used for?

A

1 Quant dep and mutliple dep vriables while ocntorlling for many other variables

101
Q

multiple logisitc regression

A

1 dichotomus dpednent and muntliple independent

Diabetes dependent and obesity, age, BP

102
Q

2 means

>2 means

A

2 sample t test

1 way analysis of variance

103
Q

use median when…

A

skewed numbers

104
Q

Standard incidence ratio equaiton

A

observed number of cases/expected number of cases

105
Q

RRR when they give you cases and person years

A

1.8-1.3/1.8

354/196785 x1000 for 1.8 person eyars

106
Q

lenght time vs lead time bias

A

length time is rapid vs slowly progressing disease

lead time bias is a test that diagnoses the same disease earlier

107
Q

hemophilia A is X linked

A

recessive

108
Q

intetnion to treat vs as treated analysis

A

as treated is analyzing them on what they actually got vs what thye were supposed to be randomized to. Randomization is lost

Intetnion to treat= you analyze the subject basedo n whiat gorup they were randomzied to despite if they dropped out or switched groups bc it can be treated as a result form the intervention. preserves the trial and is ocnservtive to results

109
Q

1 2 3 SD Mean %s

A

68
95
99.7

110
Q 
primoridal
primary 
sec
tert
quart
preventions
A

primordial- stopping it before you even get a risk factor - lifestyle as a kid
primary- you have a risk facotr now take a statin BEFORE MI happens
sec- stopping progressions of disease (Stress test positvie for MI)
Tertiary- Cardiac caths
Quats- Sharing electronic medical records so repeat caths dont cause adverse effects

111
Q

A 25%
B20%
what is NNT for A

A

25-20=.05
1/.05
20 NNT

112
Q

one study showed good Ps and findings and the other study was rndomized and did not shows good ps… why?

A

Residucal confonfounding

113
Q

can alrge population studies shed lgiht to indiivdual results/

A

no

114
Q

OR

A

Can just divide th etwo numbers and get close or AD?BC

NNT and NNH is just minus big from small and then divide by 1

115
Q

A risk o.4 compared to B risk sooo

A

40% risk of the risk of B gorup

1/.4 or the B risk has 2.5 fold higher risk

116
Q

Odds ratio

A

AD/BC
case cotnrol stidueis

looking specifalclly at one hting and comapring it to contorl gorup

Biostats (1 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions