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NURA 621 Chem/Phys > Week 1 Day 1 - Mathematics > Flashcards

Week 1 Day 1 - Mathematics Flashcards

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1
Q

Descriptive statistics

A

Use to organize, summarize, and present the values
Draws NO consclusions

“The data is the data”

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2
Q

Inferential statistics

A

Used to draw conclusions about data

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3
Q

Categorical variable

A

variable with discrete or qualitative value

male/female
liking tofu 1-5 scale
shirt (4 types)
quarantine activity is qualitative, but is infinite, not discrete

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4
Q

Continuous variable

A

variable that can measured along a continuum

age
temp
height
years as a nurse

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5
Q

nominal

A

categorical variable

no intrinsic order - shirt, quarantine activity

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6
Q

ordinal

A

categorical variables

have order - tofu (1,2,3,4,5)

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7
Q

dichotomous

A

categorical variable

only 2 values - m/f (order doesn’t matter)

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8
Q

interval

A

continuous variable

numeric value and is measured

i.e. age, temp, height, years as a nurse

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9
Q

ratio

A

continuous variable

like interval, but value of ‘0’ indicates there is nothing

i.e. age, height, years as a nurse

temp not ratio variable, nothing meaningful or valuable about my favorite temp being 70F and yours 75F

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10
Q

mean

as it relates to variables

A

advantage: easy to calc
disadvantage: affected by outliers

ratio (height, age): yes
interval (temp): yes
ordinal (tofu): maybe, possible mathematically, but you shouldn’t
nominal (shirt): no

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11
Q

median

as it relates to variables

A

advantage: outlier insensitive

ratio (age, height): yes
interval (temp): yes
ordinal (tofu): yes
nominal (shirt): no

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12
Q

mode

as it relates to variables

A

ratio (age, height): yes
interval (temp): yes
ordinal (tofu): yes
nominal (shirt): yes

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13
Q

measures of central tendency

A

mean, median, mode

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14
Q

measures of variability/spread

A

describes the manner in which data are scattered around a specific value (such as the mean)

range 
interquartile range
standard deviation
standard error of the mean
percentile
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15
Q

range

definition + as it relates to variables

A

highest value to lowest value

ratio (age, heigh)t: yes
interval (temp): yes
ordinal (tofu): yes
nominal (shirt): no

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16
Q

interquartile range

definition + as it relates to variables

A

refers to the upper and lower boundary defining the middle percent of observations

75th percentile-25th percentile
commonly used- 90th percentile-10th percentile

ratio (age, height): yes
interval (temp): yes
ordinal (tofu): yes
nominal (shirt): no

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17
Q

standard deviation

definition + as it relates to variables

A

measure of variability
how much people/subject differ from the the average (mean)

ratio (age, height): yes
interval (temp): yes
ordinal (tofu): maybe (we can, but we shouldn’t)
nominal (shirt): no

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18
Q

standard error the of the mean

definition + as it relates to variables

A

how well does the mean represent the sample

error of the mean gets smaller as the sample gets bigger

describes the amount of variability in the measurement of the population mean from several different samples

ratio (age, height): yes
interval (temp): yes
ordinal (tofu): maybe (we can, but we shouldn’t)
nominal (shirt): no

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19
Q

inferential statistics

A

trying to reach conclusion that extend beyond the immediate data alone

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20
Q

Null hypothesis

A

There is no difference

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21
Q

T test

A

simplest test for difference between 2 groups

the greater the magnitude of “t”, the more likely the groups are different (statistically different)

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22
Q

Reasons research may not be valid

A

bias
chance
confounders

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23
Q

chance

A

caused by random variations in subjects and measurements

larger sample size will reduce chance errors

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24
Q

bias

A

systematic variation

larger sample size WILL NOT help

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25
Q

Types of bias

A

selection bias
measurement bias
analysis bias

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26
Q

selection bias

A

biased sampling of population

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27
Q

measurement bias

A

systematic bias-poor measurement technique

Spanish vs Portuguese men height

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28
Q

analysis bias

A

using analysis that favors one conclusion over another

“torture the data until you get the conclusion that you want”

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29
Q

confounding

A

similar to bias

misinterpretation of accurate variables

occurs when an investigator falsely concludes that a particular exposure is causally related to a disease without adjusting for other factors that are known risk factors for the disease and are associated with the exposure.

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30
Q

POEM

A

Patient Oriented Evidence that Matters

What patient’s really care about: mortality and morbidity

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31
Q

DOE

A

Disease oriented evidence

The stuff that patients don’t care about, but is related to disease
blood pressure, cholesterol, blood glucose

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32
Q

percentile

A

percentage of a distribution that is below a specific value

i.e. a child in the 80th percentile for height if only 20% of children of the same age are taller than he is

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33
Q

experimental study

A

researcher assigns exposure

can’t assign BAD exposures usually

34
Q

randomized controlled trial

A

experimental study

assignment to exposure is determined purely by chance
(allocation is random)

usually double blind, has controls

randomizing helps (but does not guarantee) to get rid of confounding and bias

35
Q

observational study

A

researcher did not assign exposure

36
Q

cohort study

A

observational study

subjects with an exposure of interest (i.e. HTN) and subjects without the exposure are identified and then followed forward in time to determine outcomes (i.e. stroke)

exposure—–>outcome

disadvantage: longitudinal study - take a long time
i. e. Framingham

37
Q

case-control study

A

observational study

first identified a group of subjects with a certain disease and a control group without the disease, and then look back in time to find exposure to risk factors for the disease

advantages: wells suited for rare diseases, doesn’t take a long time

outcome—–>exposure

disadvantage: much more likely to have biases because it’s hard to recruit a bunch of controls who are just like your cases, except they don’t have the disease

38
Q

cross-sectional study

A

observational study

examines presence or absence of a disease or presence or absence of an exposure at a particular time.

disadvantage: Since exposure and outcome are ascertained at the same time, it is often unclear if the exposure preceded the outcome.

39
Q

case report or case series

A

Descriptive study

reports on a single or a series of patients with a certain disease.

disadvantage: usually generates a hypothesis but cannot test a hypothesis because it does not include an appropriate comparison group.

40
Q

measures of frequency of events

A

incidence
incidence rate
prevalence

41
Q

incidence

A

number of NEW events that occur during a specified period of time in a population at risk for develop the events

new events per unit of time

42
Q

incidence rate

A

incidence that reports the number of new events that occur over the sum of time individuals in the population were at risk for having the event (i.e. events/person-years).

new cases per year (or other time frame) per population

43
Q

prevalence

A

number of persons in the population affected by a disease at a specific time/number of persons in the population at that time

cummulative incidences (when someone dies, they fall out of the prevalence pool)

44
Q

How close the average of measured values are to the true value

A

accuracy

45
Q

how close measured values are to each other

A

precision

standard deviation is a measure of precision! not accuracy

46
Q

%error

A

100% * (measured value - “true” value) / “true value”

47
Q

population

A

group from which data is to be collected

48
Q

sample

A

subset of a population

49
Q

1 in —> ? cm

A

2.54 cm

50
Q

peta (P)

A

10^15

1E+15

quadrillion

51
Q

tera (T)

A

10^12

1E+12

trillion

52
Q

giga (G)

A

10^9

1E+9

billion

53
Q

mega (M)

A

10^6

1E+6

million

54
Q

kilo (k)

A

10^3

1000

thousand

55
Q

hecto (h)

A

10^2

100

hundred

56
Q

deca (da)

A

10^1

10

ten

57
Q

deci (d)

A

10^-1

0.1

tenth

58
Q

centi (c)

A

10^-2

.01

hundredth

59
Q

milli (m)

A

10^-3

.001

thousandth

60
Q

micro (μ)

A

10^-6

1E-6

millionth

61
Q

nano (n)

A

1^10-9

1E-9

billionth

62
Q

pico (p)

A

1^10-12

trillionth

63
Q

exact number

A

number NOT obtained using a measuring device

easily countable, absolutely no question of value

small number

can be reproducibly determined by counting

64
Q

How can we improve accuracy?

A

making replicate measurements and taking the average

65
Q

How can we improve precision?

A

careful lab technique and/or using instruments capable of yielding greater precision

66
Q

measures of association

A 
relative risk 
odds ration
absolute risk
attributable risk
population attributable risk
NNT (number needed to treat)
67
Q

Relative risk

A

ratio of the incidence of disease in the exposed group divided by the corresponding incidence of disease in the unexposed group

used in cohort studies

RR–> across rows on chart

68
Q

Odds ratio

A

odds of exposure in the group with disease divided by the odds of exposure in the control group

used in case control studies

OR - down columns on chart

69
Q

Number needed to treat (NNT)

A

number of patients who would need to be treated to prevent one adverse outcome

considers cost effectiveness
considers what is being cured

70
Q

absolute risk

A

relative risk and odds ratio provide a measure of risk compared with a standard

However, 40% increase in risk of heart disease because of a particular exposure does not provide insight into the likelihood that exposure is an individual patient will lead to heart disease.

71
Q

attributable risk or risk difference

A

measure of absolute risk

difference between the incidence rates in the exposed and non exposed groups

72
Q

population attributable risk

A

describes the excess rate of disease in the total study population of exposed and non exposed individuals that is attributable to the exposure

calculated by multiplying the attributable risk by the proportion fo exposed individuals in the population

73
Q

measures of diagnostic test accuracy

A

sensitivity
specificity
positive predictive value
negative predictive value

74
Q

positive predictive value

A

probability of disease in a patient with a positive test

75
Q

negative predictive value

A

probability that the patient does not have disease if he has a negative result

76
Q

sensitivity

A

ability of the test to identify correctly those who have the disease

test with high sensitivity has few false negative results

sensitivity rules out, specificity rules in

77
Q

specificity

A

ability of the test to identify correctly those who do not have the disease

high specificity has few false positive results

how specific is this test for this disease

sensitivity rules out, specificity rules in

78
Q

Probability of incorrectly concluding there is a statistically significant difference in the population when none exists.

A

Type 1 error (alpha)

79
Q

Probability of incorrectly concluding that there is no statistically significant difference in a population when one exists.

A

Type II error (beta)

80
Q

Measure of the ability of a study to detect a true difference

A

Power

81
Q

Confidence intervals

A

gives a range of values within which there is a high probability (95% by convention) that the true population value can be found

CI narrows as the # of observations increases or SD decreases

82
Q

Kaplan-Meier Analysis

A

Survival analysis

ration of surviving subjects (those without an event)/total number of subjects at risk for the event

NURA 621 Chem/Phys (11 decks)

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