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EBM > Test 10/24 > Flashcards

Test 10/24 Flashcards

1
Q

Dependent variable

A

what you think the effect of the independent variables will be seen in

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2
Q

Independent variable

A

YOU vary this in the experiment…. want to see effect on dependent variable

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3
Q

Null hypothesis

A

states there is NO relationship between the proposed independent and dependent variables

STUDY NEEDS TO PROVE THIS IS NOT TRUE and reject the null hypothesis

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4
Q

Ecologic study

A

Looks at POPULATIONS only

understand relationship between outcome and exposure at the population level

…. analyses in which the presence of a suspected risk factor is measured in different populations and compared with the frequency of disease onset

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5
Q

Ecologic fallacy

A

when incorrect conclusions are drawn from ecologic data due to an association at the group level that does NOT persist to the individual level

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6
Q

Association is NOT

A

causation

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7
Q

Normal distribution– relationship of mean, median, mode

A

they are all equal

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8
Q

standard deviation

A

measure of how tightly different data points gather around the mean

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9
Q

The number of standard deviations away from the mean a value lies in a normal distribution tells you…..

A

how likely that value is to occur

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10
Q

Standard deviation deals with

A

members of a population

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11
Q

standard error

A

standard deviation/ square root of the number of all possible samples

expected variability in measurement of a population mean seen in multiple trials

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12
Q

standard error deals with

A

samples (groups of individuals, aka sample means)

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13
Q

If you have a bigger sample size, the standard error is

A

LESS and the estimate of the population mean is MORE precise

narrower curve

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14
Q

if you have a smaller sample size, the standard error is

A

MORE and the estimate of the population mean is LESS precise

wider curve

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15
Q

Prevalance

A

the number of EXISTING cases of a condition in a population at a MOMENT of time

expressed as a percent

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16
Q

Incidence

A

the number of NEW cases of a disease that develop in a population over a specified period of time

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17
Q

Incidence requires what 3 things

A

1) new events
2) population at risk
3) passage of time

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18
Q

What are the two ways to calculate incidence?

A

Cumulative incidence (risk)
incidence rate

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19
Q

Cumulative incidence (RISK)

A

= new cases of disease/ total population at risk

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20
Q

Biggest flaw of cumulative incidence

A

best for fixed populations… does not account for people moving away/ dying etc

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21
Q

Incidence rate

A

= new cases of disease / total person-time at risk

expressed as a round number…. ie. 1.6 cases per 1000 person-years (usually, multiple number to get in terms of 1000 person-years)

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22
Q

Prevalance of disease (entrance and exits)

A

incidence ENTERS
cure, death, moving away EXITS

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23
Q

What are three ways to compare the risk in the expose and unexposed groups?

A

relative risk
absolute risk difference
number needed to treat

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24
Q

Relative risk

A

risk exposed/ risk unexposed

the probability an event will happen in an exposed group vs. probability an event will happen in a non-exposed group

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25
Q

absolute risk difference (ARD)

A

risk exposed - risk unexposed

Represents the chance in the risk of an outcome, given a particular exposed

Means “there is a __ % increase in frequency of (outcome) with (intervention)”

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26
Q

number needed to treat (NNT)

A

1/ absolute risk difference

estimates the # of patients who are exposed to something who will need to receive a certain treatment in order to prevent ONE unfavorable outcome

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27
Q

Can you calculate NNT if you only have RR?

A

NO, need the ARD

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28
Q

Accuracy

A

correct diagnoses/ total # of diagnoses

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29
Q

Prevalance Equation

A

diseased / total population at a specific POINT in time

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30
Q

False positive (FP)

A

positive test result when a patient does NOT have a disease

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31
Q

True positive (TP)

A

a positive test result when a patient does have the disease

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32
Q

False Negative (FN)

A

a negative test result when the patient has the disease

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33
Q

True Negative (TN)

A

a negative test result when a patient does NOT have a disease

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34
Q

Sensitivity definition (acronym too)

A

proportion of individuals with the disease that are TRUE POSITIVES

if a patient DOES have a disease, what are the chances they will have a positive result

SnOUT…. Sensitive test that is negative rules OUT a disease (good for screening)

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35
Q

Sensitivity equation / location on chart

A

= TP / (TP + FN )

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36
Q

Specificity (definition + acronym)

A

SpIN… specific test that when positive rules IN disease

aka if a patient does NOT have a disease, what are the chances they will have a negative test result

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37
Q

Chart for specificity, sensitivity, PPV, NPV… draw in head

A
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38
Q

Specificity equation

A

= TN/ (FP + TN)

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39
Q

Positive predictive Value (PPV) definition + equation

A

if a test is positive, the probability that a patient actually has the disease
i. Proportion of positive tests that are true positives

=TP / (TP + FP)

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40
Q

Negative predictive value (NPV) definition + equation

A

Proportion of negative tests that are true negatives

TN / (FN + TN)

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41
Q

Gold standard

A

the benchmark test that is considered the best available

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42
Q

ROC curve

A

plot of sensitivity (true positive rate) vs. 1- sensitivity (false positive rate) across a range of values to determine the cutoff

Goal: choose cutoff with high true positive and low false positives …. so that is where the ideal spot is under the curve (so farther left and up is GOOD)

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43
Q

T/ F: Specificity and Sensitivity are affected by prevalance

A

NO… they are characteristics of the tests themselves

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44
Q

What is affected by prevalance?

A

PPV and NPV

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45
Q

As prevalance increases

A

PPV increases
NPV decreases

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46
Q

As prevalance decreases

A

PPV decreases
NPV increases

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47
Q

When do you use a sensitive test?

A

first stage

SnOUT…. because a negative rules out a disease (b/c LOW false negative rate)

If the test is negative, then we are confident the patient does NOT have the disease

at first stage you want to be confident in who you are excluding

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48
Q

When do you use a specific test?

A

second stage

SpIN… a specific test is a positive test that rules in a disease because it has a low false positive rate

if the test is positive, we are confident the patient has the disease

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49
Q

In serious conditions (ie. patient could have a serious condition like a heart attack), then do you prefer a sensitive or specific test?

A

Sensitive… SnOUT… you want to rule out very serious disease

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50
Q

Pretest probability

A

patient’s likelihood of having illness BEFORE diagnostic testing is performed

51
Q

Posttest probability

A

patient’s likelihood of having a disease AFTER those test results are considered

52
Q

Likelihood ratios (LR) (definition + equation)

A

magnitude by which + or - test results alters post test probability

do NOT change disease prevalance

LR + = sensitivity / 1 - specificity

LR - = 1- sensitivity/ specificity

53
Q

What is the relationship between relative risk and absolute risk difference in a
study in which the null hypothesis is demonstrated to be true?

A

RR> ARD

because if null hypothesis = true, then risk 1 = risk 2…. so RR = 1

and ARD = risk 1- risk 2 = 0

1> 0

54
Q

COhOrt Study

A

stratify based on expOsure first, then track to see if each group developed disease OR not

can be prospective or retrospective

55
Q

Strengths of cohort studies

A

efficient for rare exposures
little exposure misclassification
calculate incidence rates and risk
can assess multiple outcomes

56
Q

Weaknesses of cohort studies + what is the biggest one?

A

loss to follow up –> BIGGEST ONE
inefficient for rare outcomes or long latencies
expensive

57
Q

How to recruit groups for a cohort study

A

1) recruit everyone from ONE pool and divide into groups after based on what you evaluate about them
2) recruit TWO separate groups using subjects with and without a risk factor (can be helpful for rare disease)(need exposed vs. non exposed group to be very similar in other ways ie. live in same area, same diet, SES)

58
Q

Narrow inclusion criteria

A

you can be more certain the results work in the group you studied, but may not be generalizable to the other groups you didn’t study

59
Q

Broad inclusion criteria

A

you can be certain the result works in the overall population BUT you may not be able to detect if the association is different to a specific subgroup

60
Q

Reasons you may exclude people from study

A

already have the condition
inclusion may bias results
hard to follow over time
they may not complete parts of the study

61
Q

Misclassification bias

A

bias about what you are classified as in the study;

systematic or random differences in the way data
are obtained on exposure or outcome → distortion in estimation of effect

can under/over estimate the effect you are looking for

can be easier to misclassify in long trials, because harder to keep up w ppl

62
Q

Counterfactual

A

as you recruit cohorts, it is necessary for the comparison to be JUST as similar with respects to all factors except the exposure

ie. think about if there are other players that might explain the relationship between exposure and outcome

63
Q

Loss to follow up

A

if you cannot establish contact with a participant during a study

if people are lost to follow up, try to make sure the # people lost in both groups = same–> non differential loss to follow up

64
Q

Methods to minimize loss

A

collect info at intake to track (address, email, phone #, relative’s contact info)

use subjects more likely to follow up

regular contact

multiple requests if they do not respond

contact info for friends/ families

65
Q

In cohort studies you can use what measurement…..

A

relative risk (risk group exposed/ risk group unexposed)

66
Q

In case control studies you can use what measurement…..

A

odds ratio

because you are starting with a sample of controls = don’t have data on entire population

67
Q

Case control study

A

start with DISEASE (case vs. control), then stratify based on exposure

ALL of the disease cases and a FRACTION of the controls… so you start with cases, then select controls which may be matched to cases

68
Q

Weaknesses of case control study

A

not good for RARE EXPOSURES
relative risk cannot be measured –> use odds ration instead
validity can be affected by SELECTION bias and RECALL bias

69
Q

How to get an appropriate source of controls in a case control study? Sources?

A

select from individuals who would have been the case group if they had developed the disease

sources: population controls ie. driver’s license, random numbers, voter registration (hard)
hospital/ clinic
family controls

70
Q

odds ratio (definition + equation)

A

measure of association between exposure and an outcome (how much higher case patients are affected by exposure than controls)

= ad/ bc

71
Q

If your odds ratio >1 vs. <1

A

> 1: positive association… ie. Cases are 1.2 times more likely to be affected by exposure x than the controls

<1: negative association… exposure is protective

72
Q

Does odds ratio change with sample size?

A

No

73
Q

Recall bias

A

participants may not remember if they were exposed or not

the information is collected AFTER disease status is known, which may affect recall differently

74
Q

Non differential recall bias

A

Non-differential: proportions misclassified are about equal among study groups → things are generally messed up, no particular direction, not
always easy to detect that it is happening or detect association between exposure and diseases– blurs difference between groups. May occur due to
“unacceptability bias.” Results in bias towards null

if the errors are the same in both groups…
biases you TOWARDS the null hypothesis = towards odds ratio or RR of 1

ie. recording/ coding errors in databases
defective measurement devices
non specific or broad definition of exposure or outcome

75
Q

Differential recall bias

A

Differential: proportions misclassified differ between study groups– misclassification based on exposure or disease; primarily one direction. Info on exposure is dependent on disease status or vice versa. Can result in bias in EITHER DIRECTION from the null

if information is better in one group over other–> association is over/ under estimated

may move RR or OR either towards or away from the null value

76
Q

Between non-differential and differential bias, what would be prefer?

A

non-differential… because at least you know what direction it is going to pull you

77
Q

Selection bias and what it mainly limits

A

error in choosing the individuals to take part in a study such that the sample obtained is NOT representative of the population you want to study (if too strict definition of cases, may miss mild/ non-classic cases)

LIMITS generalizability

78
Q

Non-response bias

A

type of selection bias

think of the people who don’t pick up the phone if you randomly dial numbers for recruitment
also think about how controls do not get as much of a benefit as cases in terms of responding

the people who choose to respond are SPECIFIC types of people and may NOT be representative of the whole population

79
Q

Random error

A

error inherent to a study that cannot be avoided

leads to imprecise results

can be QUANTIFIED not prevented

80
Q

Bias

A

systematic error caused by investigator or subjects that causes incorrect estimate of association

systematic error can be prevented but HARD to quantify

81
Q

Confounding

A

distortion of the true relationship between an exposure and outcome due to the design and analysis that fail to properly account for additional variables (confounders) that is associated with both exposure and outcome

ELIMINATE from the study

82
Q

A confounder must be

A

unbalance between the exposure groups (more or less common in exposure groups)
unbalanced between the outcome groups (more or less common in outcome groups)

83
Q

Interaction

A

when the magnitude of a measure of an association between exposure and disease meaningfully differs according to the value of some 3rd variable….

more detailed description of the true relationship between the exposure and the disease

REPORT in a study because it matters

shows what may be affecting a relationship

84
Q

Examples of selection bias

A

Non-response
survivorship
volunteer
healthy worker effect

85
Q

Volunteer bias

A

those who join studies may be different from the non-participants from the get go

86
Q

Healthy worker effect

A

happens in cohort studies

those who are employed are more likely to be healthy than the general population since general population has healthy and sick people

87
Q

What in an epidemiological study can cause bias?

A

1) differing memory of subjects (recall bias)
2) choosing from a population (selection bias)
3) other biases… on other cards

88
Q

3 ways to improve studies to minimize bias and confounding

A

1) restriction
2) matching
3) randomization

89
Q

Restriction

A

limiting study enrollment to people who fall within a specific category of the confounder (ie. age, sex)

need to know confounders in advance

90
Q

Matching

A

for every person deployed to one group, person in other group chosen who matches them on specific factors (ie. matched for everything except disease)

91
Q

Randomization is the only way to….

A

deal with unidentified confounders

92
Q

Finding confounding vs. interaction

A
93
Q

In a clinical trial, what is made by the investigator?

A

the exposure of interest!!
(in cohort study, the exposure is determined by the subject)

94
Q

What three factors are SO important to a clinical trial?

A

Randomization
double blinding
placebo control

95
Q

Randomization definition

A

way of assigning each participant to treatment so that each participant has same chance of receiving any of the options

96
Q

Benefits of randomization

A

minimizes bias

ONLY WAY TO CONTROL FOR UNKNOWN CONFOUNDERS

97
Q

Blinding

A

study is blinded if subjects do not know what therapy they are receiving (prevents subjects from having expectations)

98
Q

Double bind

A

NEITHER the subjects or investigators know what therapy the subjects are receiving

99
Q

Placebo Use

A

can cause side effects
critical that a patient does NOT know what intervention they are receiving

if you give on group a pill and other nothing, even if the pill does not work, it will likely show an affect due to the placebo effect…. thats why control group gets a placebo (still taking a pill)

100
Q

Intention to treat analysis

A

GOLD STANDARD

standard practice to analyze subjects in the group to which they were randomized …. even if they violate protocol, or don’t thake their meds or drop out

101
Q

Once randomized, subjects are always….

A

analyzed

102
Q

benefits of ITT

A

reflects real world (includes non compliance and protocol deviation)
preserves sample size
maintains study power

103
Q

cons of ITT

A

conservative estimate of treatment effect (dilution d/t noncompliance and false negatives)
does not assess efficacy accurately unless violations are negligible

104
Q

Study power

A

likelihood of seeing a difference between two groups, assuming there is a difference to be seen (probability of detecting a REAL effect

think telescope analogy — if I point a powerful telescope up into the sky, no
matter how powerful it is I will not see anything if it is cloudy

Power = 1 - beta (type II)

105
Q

What is the affect of a large sample size on power?

A

more power! which means you are likely to see a difference between two arms, if there is a difference to be seen

106
Q

Matching only works if you know what?

A

what the confounding variables are!!!
matching fails if you find out confounders later!

107
Q

Cross over clinical trials

A

each study participant serves as OWN counterfactual, getting ALL possible study interventions in a random order

switch study arms

108
Q

Confidence intervals

A

estimate the effect of size and precision (variability of the effect)

In 95% CI… if we were to conduct this experiment 100 times, 95 out of those 100 times, we would see this range of results

does NOT include bias

If our 95% CI does not include null hypothesis, we can reject it

109
Q

statistical significance (p-value)

A

if p <0.05, then you reject the null hypothesis and there is statistical significance at the 5% level

110
Q

does statistical significance = clinical significance?

A

NO

111
Q

Type I error = alpha error

A

false positive detection of an association

you see something that is NOT real

if the null hypothesis is TRUE but you mistakenly reject it

112
Q

Type II error = beta error

A

False negative

When there is something to see, but you do NOT see it

mistakenly think two things are the same

113
Q

P-value

A

probability that a result as strong or stronger might
be observed if the null is true
-Reject null when p < α→ statistically significant (results not
due to chance)

AKA assuming H0 is true, what is the probability of getting a result that is at least as extreme as the one we got?

114
Q

What do we evaluate P-value at? What does above and below that value mean?

A

set alpha at 0.05

P<0.05… reject the null
P>0.05 then cannot reject the null

115
Q

Define P-value

A

if the null hypothesis is true, then how likely is the result I got

116
Q

Effect of variability (noise) on power

A

decreased noise = increased power

increased noise= decreased power

(Waldo example: the other people in the Waldo picture add noise, make it harder to find him)

117
Q

Effect size (signal/ outcome trying to observe) impact on power

A

smaller effect size –> less power

(Waldo example: Waldo is the effect (signal))

118
Q

Effect of alpha on power

A

decrease alpha = decrease power

119
Q

Effect of sample size on Type I and Type II error

A

increase sample size, type I and type II errors go down

increasing sample size makes it EASIER not to miss the difference between the two groups

120
Q

Clinical research 2 x 2 table

A
121
Q

Example relative risk calculation from LO doc

A
122
Q

If you lower the cutoff for a test it….

A

increases false positives, lower false negatives
= increased sens, increased NPV, decreased spec, decreased PPV

123
Q

If you raise the cutoff for a test it…

A

decreases false positives, increases false negatives
= decreased sens, decreased NPV, increased spec, increased PPV

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