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EBM > EBM > Flashcards

EBM Flashcards

1
Q

External Validity

A

The confidence with which the results can be accurately generalized to the population

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2
Q

What do Inclusion and Exclusion Criteria do to validity?

A

Improves internal validity by controlling variability

Decreases external validity by limiting generalizability

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3
Q

Probability Sampling

A

When each member of the population has a known chance to be included in the research

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4
Q

Stratified random sampling

A

To ensure that critical variables are represented in the sample, subjects are stratified by traits and then randomly sampled

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5
Q

Systematic sampling

A

A type of random sampling done when subjects are put in order. For example, the 3rd person from each letter of the alphabet can be chosen. Can incorporate stratification if there is a meaningful order (i.e. Weight or age)

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6
Q

Purposive sampling

A

Hand-picked patients. Purpose might be to gather different perspectives

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7
Q

Quota sampling

A

Convenience sampling, with specific quotas included. This is like stratification

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8
Q

Healthy-User Bias

A

May occur with convenience samples because healthier people are more likely to volunteer, or be seeking healthcare

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9
Q

Berkson’s Bias

A

Population selected from an impaired or diseased group (like hospitalized pts)

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10
Q

Exclusion Bias

A

Excluding patients with certain characteristics. I.e comorbidities, racial groups, socioeconomic groups, people who have dropped out of a study

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11
Q

Internal validity

A

Internal health of the study. How free it is from bias and chance.

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12
Q

External validity

A

The degree to which you can generalize the results of the study back to the population of interest.

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13
Q

How to prevent selection bias

A

Random or stratified random assignment

Equivalence can be double-checked for suspicious factors by obtaining relevant demographic or baseline data

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14
Q

Extraneous factors

A

Other variables in the study that may affect the relationship between the independent and dependent variables

Example: Researchers examined whether relaxation therapy was better than drug therapy for reducing the number of tension headaches in medical students.
Extraneous Factors to control or account for
1.   Diet
2.  Other medications
3.  Severity of tension headaches

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15
Q

Measurement bias

A

Occurs when measurements are made unequally between treatment groups

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16
Q

Confounding bias

A

Occurs when 2 factors are associated and the effect of one is confused with or distorted by the effect of the other

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17
Q

Investigator bias

A

When the investigator ‘knows’ the expected results, so the investigator treats groups differently

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18
Q

Allocation concealment

A

Prevents investigator bias. The ppl randomizing individuals into groups are blinded as to which subjects go into which group. E.g. Subjects are given numbers (de-identified from researcher) or a 3rd party interacts with subjects during allocation

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19
Q

Investigator blinding

A

Prevents investigator bias. E.g. Investigator who is providing treatment or making measurements is blinded as to the group.

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20
Q

Hawthorne effect

A

Subject bias where people change behavior in a study. Effects both internal and external validity

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21
Q

Subject blinding

A

Solution to hawthorne effect
Placebo group
Subjects don’t know what group they’re in
BUT blinding doesn’t work for long if there is an obvious physical change

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22
Q

Absolute risk

A 
Incidence
# of ppl with disease/total number of people at risk for getting disease
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23
Q

Relative Risk/Risk Ratio

A

‘How many times more likely are exposed persons to get the disease relative to non-exposed persons?’

  • Incidence in exposed/incidence in unexposed
  • Common metric in studies with similar risk factors but with different baseline incidence rates.
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24
Q

Cross-over trials

A

Advantages:

  1. Subjects are their own controls, which reduces inter-subject variability
  2. Randomization removes order effect
  3. Statistical advantages

Disadvantages:

  1. Inappropriate when time has an effect (e.g. Recovery from surgery)
  2. Inappropriate when order effect is lasting
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25
Q

Absolute Risk Difference/attributable risk

A

How much extra disease is actually caused by an exposure or risk factor

Subtract absolute risk of unexposed from absolute risk of exposed

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26
Q

Relative Risk

A

Ratio between absolute risk of exposed and absolute risk of non exposed

I.e. Ratio between incidence of exposed and incidence of non exposed

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27
Q

“Big Data” Studies

A

Subtype of retrospective cohort
Very large N
Data from various sources, though gathered usually without medical intent (billing)
-BEST method for looking for rare side effects or outcomes

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28
Q

Case Control Studies

A
  • generally involves interviewing
  • Cases: newly-incident (optimally) cases of target disease
  • Controls: ppl without target disease who ‘had the opportunity’ to get exposed, and also in other ways similar to pts.
  • Controls are the hardest parts of case-control design
  • Case control outcomes are usually odds ratio
  • ratio bw odds of exposure in cases/odds of exposure in controls
  • very close to relative risk if rate in unexposed people is <1%
29
Q

Odds ratio

A

Ratio between the odds of exposure in cases and the odds of exposure in controls

30
Q

Case series

A

No controls
NOT population-based
Best for describing emergent diseases, rare outcomes, odd exposures
Sometimes the best evidence available

31
Q

Misclassification bias

A

Example: Overworked medical assistants often forget to ask patients whether or not they use alternative medicines and simply check “No” in the electronic medical record. You are studying the effect of alternative medicines on the later development of liver failure.

Pts that are actually taking these medications are classified as not taking them, so somebody could be taking these meds, be misclassified, then develop liver disease, so this bias is therefore increasing likelihood for type II error, where you say there isn’t a difference, then there is one

32
Q

Compliance bias

A

People that are compliant with one thing tend to be compliant with other things that are thought to be good for them, causing confounding things

33
Q

Lead-time bias

A

Common problem in screening studies: if you LOOK for something, you’ll tend to find it sooner, than if you wait for it to cause symptoms

-pt will live longer with diagnosis even if intervention had no effect

34
Q

Prevalence

A

Number of people in a population who have a disease
at a given time
-If illness is chronic, over time prevalence will rise even if incidence is small
-Effective treatment can delay mortality and thus increase
prevalence
-Diseases with high and rapid mortality (even if disease has high
frequency) may have low prevalence because most die quickly
-High prevalence could reflect better treatment and longer survival

35
Q

Absolute risk reduction

A

Difference in disease between 2 groups; but usually used in a treatment sense as opposed to an exposure sense.

-How much disease can be prevented/reduced with a given treatment?

NNT= 1/ARR

36
Q

Number Needed to Treat

A

1/Absolute Risk Reduction OR 100/ARR

Ex: If a cancer treatment reduces mortality from 50% in untreated group to 40% in treated group, the Number needed to treat would be: 1/ (0.5 - 0.4) = 1/0.1 = 10. You need to treat 10 people with the drug to prevent 1 cancer death.

37
Q

If RR<1

A

Adverse outcome is decreased by treatment compared to control

38
Q

If RR>1

A

Adverse outcome is increased by the treatment compared to control

39
Q

If RR=0.5

A

The chance of a bad outcome is half as likely to occur with treatment as without it

40
Q

RR=1

A

Risk is same in both treatment and control groups

41
Q

Relative Risk Reduction

A

How much the treatment reduced the risk of bad outcomes relative to the control group who did not have the treatment

[[[[[
Control event rate (#not exposed with disease/total not exposed)
MINUS
Experimental event rate (#exposed w. Disease/total exposed)
]]]]]
OVER
Control event rate

42
Q

Number needed to harm

A

100/Absolute risk %

43
Q

Sensitivity

A

How well can test detect disease

-True positive/Total with disease

44
Q

Specificity

A

How well can the test detect the absence of disease

-True negative/Total people without disease

45
Q

Pre-test probability

A

Prevalence

Varies with population/patient

46
Q

Positive Predictive Value

A

Of all ppl with positive test, which % actually have disease?

47
Q

Likelihood Ratio

A

Likelihood of certain test result in pt with disease
OVER
Likelihood of same test result in pt without disease

48
Q

Likelihood ratio for a + test

A

Likelihood of + result in diseased pop/likelihood of same in non-diseased pop

=

Sensitivity/1-specificity

49
Q

Likelihood ratio for a - test

A

Likelihood of - result in diseased pop/likelihood of same in non-diseased pop

1-sensitivity/specificity

50
Q

Hazard ratios

A

Evaluate one prognostic factor at a time (i.e. How big was the primary tumor?)

  • Similar to relative risk: Ppl with prognostic factor Y are X times more likely to die than ppl without this factor
  • Assumes that ratio between 2 prognostic curves is fairly stable over time
51
Q

Primary Prevention

A
  • before exposure
  • sanitation
  • nutrition
  • immunization
  • education
52
Q

Secondary Prevention

A
  • after exposure
  • early detection (Screening)
  • early intervention
53
Q

Tertiary Prevention

A
  • after disease process begins
  • reverse course of disease
  • rehabilitation
  • treatment
54
Q

When is it okay to screen?

A
  • If disease has significant morbidity/mortality
  • Prolonged asymptomatic phase
  • Effective treatment available
  • High prevalence in particular pt population
  • If pt accepts test
  • If pt can comply to treatment after result of test
  • If test has good sensitivity/specificity
  • If test is valid and reliable
  • If it’s cost-effective
55
Q

Length-time bias

A

Slower growing tumors that are less likely to kill are more likely to be detected by one time/periodic/interval screening

56
Q

68-95-99. Rule

A

-shorthand used to remember the percentage of values that lie within a band around the mean in a normal distribution with a width of one, two and three standard deviations, respectively; more accurately, 68.27%, 95.45% and 99.73% of the values lie within one, two and three standard deviations of the mean, respectively.

57
Q

Variance and it’s relationship to standard deviation

A

Variance is the square of SD

S2= Sum of(X-M)2 / N-1

Where X=value and M=mean

58
Q

Standard Error

A

A measure of how far the sample mean is away from the population mean

SEM= SD/sqrt(N)

Used to show variability caused by experimental imprecision
WHEREAS
SD is used to show biological variability

59
Q

Relationship bw Alpha and Type 1 Error

A

As alpha decreases (say, from 0.05 to 0.01 —> 95% confidence to 99% confidence), chance of type 1 error DECREASES

60
Q

Relationship between Alpha and Type II Error

A

As alpha decreases, the chance for Type II errors increases (I.e., as you make your confidence interval wider, you’re more likely to say there isn’t a difference when there is indeed a difference.)

61
Q

Random Error

A

Caused by inherently unpredictable fluctuations in the readings of a measurement apparatus or in the experimenters interpretation of the instrumental reading. This error can occur in either direction.

62
Q

Systematic Error

A

Is predictable
Typically consistent or proportional to the true value.
Causes by imperfect calibration of measurement instruments or imperfect methods of observation.
Usually occurs only in 1 direction.

63
Q

Student T Test

A 
Assumptions:
N<30
Random sampling
Equal sample size
Most useful when comparing 2 sample means

T= M1-M2 / sqrt[(SE1)sq + (SE2)sq]

Want the highest # T as possible, because T represents difference between the means. Large T indicates small variability and high signal.

If T>critical T, then there is a clinically significant difference.

64
Q

1 Tailed T Test vs 2 Tailed T Test

A

1 Tailed: A one-tailed test will test either if the mean of exp group is significantly greater than control group (x) or if the mean is significantly less than x, but not both. The one-tailed test provides more power to detect an effect in one direction by not testing the effect in the other direction.

2 Tailed: A two-tailed test will test both if the mean is significantly
greater than x and if the mean significantly less than x. The mean is considered significantly different from x if the test statistic is in the top 2.5% or bottom 2.5% of its probability distribution, resulting in a p-value less than 0.05. 2-tailed is more robust.

65
Q

Paired vs Unpaired T Test

A

Paired
-The observed data are from the same subject or from a matched subject and are
drawn from a population with a normal distribution.
Example: Measuring glucose
concentration in diabetic patients before and after insulin injection.

Unpaired
The observed data are from two independent, random samples from a population with a normal distribution.
Example: Measuring glucose concentration of diabetic
patients versus non- diabetics.

66
Q

ANOVA

A

Used to compare 3 or more means. Analyzes by using sum of squares.
-tells us that the smallest and largest means likely differ from each other

67
Q

Mann Whitney U Test

A
  • Nonparametric alternative to 2-sample T-test
  • Actual measurements not used- instead, ranks are used
  • USE THE LOWER OF THE 2 U STATISTICS
68
Q

Kruskal-Wallis Test

A
  • Nonparametric equivalent of a 1-way ANOVA on ranks

- used for comparing 2 or more independent samples of equal or different sample sizes

69
Q

Calculating Chi Square

A

Expected value for box=
(Total in row * Total in column) / N Participants

Degrees of freedom= (rows-1)(columns-1)

Calculated X2 should be > Critical value

X2= sum of[(obs-exp)sq /expected]

EBM (1 decks)

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