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Biostats > Final Exam > Flashcards

Final Exam Flashcards

1
Q

evidence based medicine

A

quality revolution in healthcare

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2
Q

Demings philosophy

A

quality is about people, not products

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3
Q

Deming facts

A
  • didn’t believe in quotas
  • worked for US Census and Western Electrical
  • improved manufacturing quality during wartimes
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4
Q

kaizan

A

quality improvement requires teamwork, open communication and problem solving

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5
Q

nelson data to wisdom continuum

A

organizing data so that it can provide new insights and information

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6
Q

history of probability

A

basically people aren’t good at understanding probabilty

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7
Q

uniform distribution

A

block, each score is equally as likely

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8
Q

probability distributions

A

allows you to distribute possible outcomes and which is most common

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9
Q

normal distributions

A

bell curve, rare events are the tails

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10
Q

exponential distributions

A

rare events

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11
Q

availability bias

A

linking an event to something that happened in our past

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12
Q

Monty Hall problem

A

odds of winning go from 1/3 to 2/3 when you switch

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13
Q

categorical measurements

A

put observations into named categories (HIV status, gender)

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14
Q

ordinal measurements

A

categories that can be put in rank order (cancer stage, smoking)

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15
Q

quantitative measurements

A

numerical values that can be put on a number line (age, weight, BMI)

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16
Q

observation

A

unit upon which a measurement is made (ie. a person/row)

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17
Q

variable

A

thing we measure (ie. ID or age/column)

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18
Q

value

A

realized measurement for a variable (ie. age=27/cell)

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19
Q

objectivity

A

not making data conform to a preconceived worldview

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20
Q

reliability

A

ability to collect the same values for variables repeatedly (how close the darts are to each other)

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21
Q

validity

A

how truthful the data is (darts hitting the bullseye)

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22
Q

internal validity

A

truth within a study

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23
Q

external validity

A

if results can apply beyond the study

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24
Q

incidence

A

new cases in a population over a defined period

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25
Q

prevalence

A

total number of cases at a given point in time

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26
Q

non-experimental vs experimental

A

experimental assigns subjects to groups according to explanatory variables

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27
Q

case-control

A

subjects with a certain disease are matched to a similar group without the disease

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28
Q

cohort

A

two groups (1 exposed and 1 non-exposed) are followed to compare rates of new cases

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29
Q

James Lind Scurvy trials

A

treatment for scurvy, 6 different treatment plans, example of an RCT

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30
Q

RCT

A

group of individuals with the same condition and assigning them to interventions or control

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31
Q

convenience sampling

A

worst kind of sampling, usually biased, sampling whoever is around

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32
Q

power of sampling

A

can be effectively used a number of ways

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33
Q

frequency distributions

A

check distributions for outliers, errors, normal distribution, and if any can be combined

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34
Q

symmetry

A

balance in the pattern

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35
Q

modality

A

number of peaks

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36
Q

kurtosis

A

width of tails

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37
Q

departures

A

outliers, they skew data

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38
Q

positive skew

A

right tail is longer

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39
Q

negative skew

A

left tail is longer

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40
Q

mean

A

gravitational center

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41
Q

median

A

middle value

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42
Q

mode

A

value with the highest recurrence

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43
Q

range

A

spread of data (maximum-minimum)

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44
Q

frequency table

A

list all data values and frequency count

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45
Q

sample vs population mean

A

usually use a sample population mean to estimate the population mean

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46
Q

quartiles

A

divides data into 4 equal groups

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47
Q

variance

A

how spread out data is around the mean

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48
Q

standard deviation

A

spread of data around the mean

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49
Q

random variables

A

number that has different values depending on chance

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50
Q

population

A

set of all possible values for a random variable

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51
Q

event

A

outcome/set of outcomes

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52
Q

probabilities

A

proportion of times an event may occur in a population

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53
Q

discrete random variables

A

countable set of possible outcomes

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54
Q

continuous random variables

A

unbroken continuum of possible outcomes

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55
Q

probability mass function (pmf)

A

assigns probabilities to all possible outcomes for a discrete random variable

56
Q

area under the curve

A

probability, adds up to 1

57
Q

cumulative probability

A

probability of said value or less

58
Q

probability density function (pdf)

A

assigns probabilities to all possible outcomes for a continuous random variables

59
Q

binomial random variable

A

discrete random variable with only 2 outcomes

60
Q

normal random variable

A

most common type of continuous random variable (ie. height, weight, systolic bp)

61
Q

68-95-99.7 rule

A

68% of data is within u+-o and so on…

62
Q

SEM equation

A

SEx=s/square root of n

63
Q

statistical inference

A

how you generalize from the particular to the general

64
Q

central limit theorem

A

sampling distribution of x-bar tends towards normality

65
Q

z-scores

A

gives you the p-value

66
Q

null hypothesis

A

no difference/association

67
Q

significance levels

A

p-values and whether you should reject Ho

68
Q

one-sided vs two-sided

A

one-sided looks for values larger than the null, two-sided is for when you don’t know the direction of the alternative

69
Q

point estimation

A

single best estimate of a parameter

70
Q

confidence intervals

A

type of interval estimation

71
Q

interval estimation

A

surrounds point estimate with margin of error

72
Q

family of t-distributions

A

like a z-distribution but with more df and more uncertainty

73
Q

df

A

degrees of freedom that allow tails to be skinnier or broader

74
Q

relationship between df and distributions

A

df increases ->t-tails get skinnier->t becomes more like z

75
Q

paired data

A

get data from 2 groups and compare

76
Q

paired t-test

A

each point matches another in a different sample

77
Q

conditions for inference (using a t-test)

A

simple random sample, valid information, normal population, large sample

78
Q

normality condition for using a t-test

A

normality applies to the sampling distribution of the mean, not the population

79
Q

single sample t-test

A

one group, comparisons are made to an external population

80
Q

independent 2-sample t-test

A

two separate groups with no pairing, compare the separate groups

81
Q

Levene’s test

A

tests that the variances are equal (thats the null), f-test to determine pooled variance

82
Q

ANOVA

A

analysis of variance

83
Q

statistics used to compare 3+ means

A

use ANOVA, with a continuous variable

84
Q

family-wise error rate

A

probability of making a type 1 error

85
Q

variability between groups (MSB)

A

mean square between, variability between groups of means around the grand mean

86
Q

variability within groups (MSW)

A

mean square within, average amount of variation within groups

87
Q

post hoc comparisons

A

only if you accept the alternative hypothesis, tells you which of the means differ

88
Q

LSD vs. Bonferroni

A

bonferroni is more conservative

89
Q

if the interval doesn’t include 0…

A

it is statistically significant

90
Q

homoscedasticity

A

equal in variance

91
Q

heteroscedasticity

A

unequal in variance

92
Q

correlation

A

determines if there’s significant association

93
Q

r

A

linter relationship between -1 and 1

94
Q

r^2

A

coefficient of determination, variance in Y explained by X

95
Q

what affects correlation

A

confounding, outliers, non-linear relationships

96
Q

residuals

A

distance from data point to the line

97
Q

dummy variables

A

giving categorical variables to independent variables in a multiple regressions, k-1

98
Q

binary response variable

A

categorical variable with 2 responses

99
Q

chi-square test

A

two categorical variables, compare expected to observed, the more difference there is the more association there is

100
Q

Mantel Haenszel test

A

using another categorical to split data up more, need a large sample size

101
Q

assumptions for parametric tests

A

normal distributions, large sample sized, quantitative data

102
Q

assumptions for non-parametric tests

A

doesn’t assume normality, observations are independent

103
Q

advantages of non-parametric

A

use on non-normal data, small sample size, easier to apply

104
Q

disadvantages of non-parametric

A

loss of info, harder to reject null, decreased statistical power

105
Q

general rules for non-parametric

A

use parametric whenever possible

106
Q

univariate vs multivariate

A

testing the relationship between two variables vs testing the relationship of multiple variables

107
Q

outcome variable

A

event time, dependent, variable in question

108
Q

survival analysis

A

time to some event (ie. death, infection, hospitalization) need to define the outcome variable

109
Q

logistic regression

A

multiple regression with a binary outcome variable (ie. age vs diabetes diagnosis)

110
Q

fitted model

A

actual model that contains outcome and explanatory

111
Q

sample size rules (for general regression model)

A

n>30, 1 variable per 30-50

112
Q

Cox regression

A

survival analysis and logistic regression together, binary response

113
Q

consecutive sampling

A

used a lot in healthcare, sampling people with characteristics you like, not purely random

114
Q

simple random sampling

A

everyone has a known probability of being sampled, best kind of sampling

115
Q

stratified random sampling

A

divides populations into groups so that each group has an equal chance of being included

116
Q

systematic sampling

A

samples every nth individual

117
Q

cluster sampling

A

sampling of a natural grouping

118
Q

n

A

sample size

119
Q

x

A

variable

120
Q

xi

A

value of individual I for variable x

121
Q

u

A

population mean

122
Q

s

A

standar deviation

123
Q

x-bar

A

sample mean

124
Q

degrees of freedom calculation

A

Welsh method or conservative method

125
Q

conservative post hoc

A

makes it more difficult to detect statistical differences among the means

126
Q

Cox

A

determine what variables are most associated with outcome and time, consider multiple variables

127
Q

undercoverage bias

A

some groups are left out ur underrepresented

128
Q

volunteer bias

A

self-selected participants, tend to be atypical of the population

129
Q

nonresponse bias

A

large percentage of individuals refuse to participate/cannot be contacted

130
Q

where are proportions from?

A

categorical variable

131
Q

chi-sqaures are non-parametric which means…

A

there is a decrease in statistical power and it compares ranked data

132
Q

Pearson’s correlation is equivalent to…

A

spearman’s ranked something

133
Q

correlation doesn’t mean

A

causation

134
Q

key features of RCT

A
  • randomizatoin
  • control group for comparison
  • blinding or masking
  • ethics
135
Q

what can multiple regression analysis help you do?

A

determine confounding

Biostats (3 decks)

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