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ibi test 3 > All the biostat stuff > Flashcards

All the biostat stuff Flashcards

1
Q

class of statistical methods for studying the occurrence and timing of events

A

survival analysis

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2
Q

survival analysis can also be applied to

A

development of disease
response of treatment
relapse of disease
rehospitalization
quitting smoking

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3
Q

percent of patient alive 5 years alfter treatment begins or 5 years after diagnosis

A

5-year survival

measure of prognosis

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4
Q

probability of remaining alive for a specific length of time

A

survival

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5
Q

subjects who are sensored when the information about survival time is incomplete

A

-lost to follow up
-still alive at the study termination date

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6
Q

the set is said to be complete when there are

A

no censored survival times

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7
Q

probability that an individual survives longer than some time

A

survival function
S(t)=Pr

kaplan-meier method estimates S(t)

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8
Q

the hazard function describes

A

the conditional failure rate

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9
Q

the probability of dying during a very small time interval, assuming the individual has survived to the beginning of the interval

A

hazard time

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10
Q

hazard function

A

h(t)= # of patient dying near t/
total # of patients surviving near t

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11
Q

describes the probability that an event has occurred by time t

A

cumulative hazard

the greater the value at time t, the greater risk of death by time t

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12
Q

represent how much more or less likely the event is to occur in one group relative to a control

A

hazard ratio

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13
Q

methods for comparing (S)t

A

log rank test
cox regression

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14
Q

the time at which 50% of the subjects have had the event

A

median survival

estimated using Kaplan meier curve

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15
Q

generated by even analyis

A

risk ratio

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16
Q

risk ratio

A

probability of event in treatment group/probability of event in control group

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17
Q

HR=1

A

rate of the event is the same in both groups

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18
Q

HR>1

A

rate is higher in treatment group

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19
Q

HR<1

A

rate is lower in treatment group

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20
Q

RR=1

A

probability of the even is the same in both groups

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21
Q

RR>1

A

probability is higher in treatment group

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22
Q

RR<1

A

probability is lower in treatment group

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23
Q

alternative hypothesis

A

H1 is generally a statement of effect, association, or difference

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24
Q

null hypothesis

A

H0 is generally a statement of no effect, no association, no difference

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25
Q

provide standardized evidence of an effect, association, or difference

A

test statistics

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26
Q

t tests are appropriate for

A

comparing means when the outcome is continuous

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27
Q

chi-square tests are appropriate for

A

comparing frequencies between two groups when the outcome is categorical

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28
Q

measure of premature mortality

A

years of potential life lost

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29
Q

combines quantity + quality of life

A

quality adjusted life years

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30
Q

average number of additional years of life gained from intervention, multiplied by quality of life “weight” in each of those years

A

quality adjusted life years

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31
Q

QALYs: weight of 1

A

perfect health

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32
Q

QALYs: weight of 0

A

equivalent to death

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33
Q

used for evaluating new therapy/intervention on population basis

A

QALYs

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34
Q

measure of overall disease burden, expressed a cumulative number of years lost due to ill health, disability, early death

A

disability adjusted life years

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35
Q

risk factors for hypertension

A

increased age
race (black)
increased risk for men <45 over women

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36
Q

review study data at pre set time points to ensure participant safety

A

data safety and monitoring boards

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37
Q

when might the data safety and monitoring board stop a study early?

A

intervention too risky
benefit of intervention is clearly demonstrated

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38
Q

factors to minimize selection bias

A

clearly defined inclusion/exclusion criteria
randomized to study assignment groups
little loss to follow up

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39
Q

ways to minimize confunding bias

A

randomization
intention-to-treat analysis

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40
Q

determin whether a clinically relevant difference exists between two interventions

A

superiority study

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41
Q

determined whether a new treatment is neither worse nor better than another established treatment

A

equivalence trial

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42
Q

determine whether a new treatment is not inferior to another established treatment

A

non-inferiority study

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43
Q

points to consider for study generalizability

A

inclusion/exclusion criteria
biologic mechanism
strength of findings

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44
Q

results/data made publically available prior to author analysis and peer review

A

early termination

45
Q

type I error

A

when you reject the null hypothesis but you shouldnt have

major reason for replication

46
Q

probability of making type I error

A

Alpha

47
Q

type II error

A

when you dont reject the null hypothesis but you should have

often occurs due to inadequate study power

48
Q

Beta

A

probability of making type II error

49
Q

how to avoid type I and II errors

A

careful study design and adherence

50
Q

alpha=0.05

A

investigator is willing to accept a 5% risk of falsely concluding groups differ

type I error

51
Q

beta = 0.20

A

investigator accepts a 1 in 5 chance of missing a true difference between groups

type II error

52
Q

probability that a study can detect a true difference between groups

A

power

1-beta

53
Q

how to calculate sample size

A
  1. determine study design
  2. set acceptable levels of type I and II error
  3. determine magnitude of difference in outcomes between 2 groups
  4. calculate size requirement
54
Q

larger differences require [larger/smaller] sample sizes in order to be detected

A

smaller

55
Q

reduced risk of type I error requires [larger/smaller] sample size

A

larger

56
Q

reduced power results in [larger/smaller] sample size requirement

A

smaller

57
Q

therapeutic goal of warfarin

A

prolongation of PT time

58
Q

INR 1.5-2

A

low intensity anticoagulation

long term

59
Q

INR 2-3

A

moderate intensity anticoagulation

intitally

60
Q

INR 2.3-3.5

A

high intensity anticoagulation

mechanical prosthetic heart valves

61
Q

therapeutic goal of warfarin is acheived in

A

about one week

62
Q

factors affecting warfarin PK

A

diet
GI status
other drugs
genetics

63
Q

warfarin dosing has

A

wide inter-individual and intra-individual variability

64
Q

results show a difference between the intervention and control groups, but in reality, the two groups are the same with respect to the outcome of interest

A

type I error

65
Q

researchers conclude the groups are the same, but in reality they are different

A

type 2 error

66
Q

to calculate relative risk improvement

A

risk in the intervention group - risk in the control group

then, divide by risk in the control group

67
Q

NNT calculation

A

1/ARR

ARR= absolute risk reduction

68
Q

calculate ARR

A

risk of event in control group - risk of event in treatment group

event can be negative or positive

69
Q

calculate ARR

A

risk of event in control group - risk of event in treatment group

event can be negative or positive

70
Q

p value <0.001 would indicate

A

if the null hypothesis is true, there is less than 0.1% probability of obtaining a test statistic equal to or more extreme than the one obtained

71
Q

if the confidence interval contains 1

A

finding is non significant
corresponds to p value >.05

72
Q

p value >.05

A

non significant

73
Q

phase of trialing that examines efficacy as primary standpoint

A

phase 2

74
Q

phase of trialing that focuses on efficacy AND safety

A

phase 3

75
Q

why is human testing still required for new drugs?

A

differences in disease processes and stages between animals and humans

intervariability in humans that is not seen in inbred testing animals

76
Q

if the confidence interval contains 1

A

no statistical significance between groups

77
Q

if the CI does not contain 1

A

statistically significant

78
Q

a hazard ratio of 1 means

A

no difference in the rate of outcome between 2 groups

79
Q

a chi square test is used

A

to evaluate if two proportions are different

80
Q

an ANOVA is used

A

to compare mean values of more than two groups

81
Q

a t test is used

A

to compare means of continous variables between two groups

82
Q

occurs when sample selected is not representative of target population

A

sampling error

83
Q

when the results of the intervention are not different, but researchers conclude that they are

A

type I error

84
Q

when the results of interventions are different, but researches conclude that they are not

A

type II error

85
Q

ensures equal distribution of possible confunding variables between intervention and control groups

A

randomization

86
Q

deviation from results of inferences from the truth or processess leading to such deviation

A

bias

87
Q

any trend in the collection, analysis, interpretation, publication, or review of data that can lead to conclusions that are systematically different from the truth

A

bias

88
Q

results from flawed procedures in collecting data or imperfect definitions of study variables

A

information bias

89
Q

distortion of the estimated of the effect of an exposure of interest on an outcome because it is mixed with the effect of an extraneous factor

A

confunding

90
Q

resemblence of study population to the larger population from which it was drawn

A

external validity

91
Q

this is present when the study results are obtained in an unbiased manner

A

internal validity

92
Q

individual studies are at risk of inaccurately estimating the exposure-outcome relationship due to

A

bias and sampling variability

93
Q

individual studies may not generalize across

A

heterogeneous populations and settings

94
Q

how can we determin the current state of evidence?

A
  1. define a question
  2. search literature
  3. assess the studies
  4. combine the results
  5. put findings in context
95
Q

heterogeneity

A

how consistent is the effect across studies?

96
Q

in the absence of bias, funnel plots should

A

resemble a symmentrical, inverted funnel

97
Q

sampling variety [increases/decreases] as the sample size [increase/decreases]

A

decreases; increases

98
Q

smaller studies showing no statistically significant effects remain unpublished, contributing to

A

publication bias

99
Q

small differences in inclusion/exclusion cirteria can produce large differences in the set of studies included in the analysis

A

selection bias

100
Q

informations produced where publishing is not the primary activity of the producing body

A

grey literature

101
Q

some examples of grey literature

A

thesis, disseratations
conferences papers/posters
clinical trial data
government documents

102
Q

why use grey literature?

A

more complete review of topic
good for data
may include negative results
faster

103
Q

Agency for Healthcare Research and Quality
NICHSR ONESearch
The Health and Medicine Division Reports
Agency for Healthcare research and quality

provide what info?

A

grey literature database resources

104
Q

BMC
Web of Science

provide what info?

A

BMC
Web of Science

conference abstracts and proceedings

105
Q

clinicaltrials.gov
cochran central registrar of controlled trials
IRSCTN registry

provide what info?

A

clinical trial info, policy info, regulatory data, health stats

106
Q

what study design can demontrate a causal relationship?

A

cohort

107
Q

framework for developing a clinical question

A

PICO

Patient/problem
Intervention
Comparison
Outcome

108
Q

optimal study design for impacts of treatments and therapies

A

randomized control trial

109
Q

framework for QI projects

A

Plan Study Do Act

ibi test 3 (20 decks)

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