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EBM > EBM Exam 1 > Flashcards

EBM Exam 1 Flashcards

1
Q

Descriptive statistics vs Inferential statistics

A

Descriptive: describe and summarize data
Inferential: make inferences to larger pop beyond the data collected

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2
Q

Simple random sample

A

each person has equal prob of being selected (prob sample)

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3
Q

Stratified random sample

A

Divide into M and F and select 10% of each gender – ensure that both men and women are represented equally (prob sample)

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4
Q

Cluster sample

A

select 10 clinics in NE OH then select 50 pt from each clinic (prob sample)

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5
Q

Systematic sample

A

select every pt that walks through the door at the clinic

prob sample

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6
Q

Convenience sample

A

advertise over internet, newspapers

approach people in waiting room

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7
Q

Nominal
Ordinal
Interval
Ratio

A

Nominal: cannot be ordered (gender, race)
Ordinal: can be ordered (likert scale)
Interval: meaningful intervals (temp)
Ratio: absolute zero, ratios are possible (age)

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8
Q

Discrete vs. continuous

A

Discrete: counts, no fractions (ex. number of pts)
Continuous: infinite number of values (age)

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9
Q

Match test w/ scale of data for dependent variable

Differences in proportion

A

Chi square (nominal)

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10
Q

Match test w/ scale of data for dependent variable

One or 2 means

A

t-test (interval or ratio)

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11
Q

Match test w/ scale of data for dependent variable

More than 2 means

A

Wilcoxon rank sum test (ordinal)

ANOVA w/ F-tests (interval or ratio)

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12
Q

Match test w/ scale of data for dependent variable

Differences in variances

A

F-test (interval or ratio)

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13
Q

Match test w/ scale of data for dependent variable

Association b/w 2 variables

A 
Spearman rho (ordinal)
Pearson r (interval or ratio)
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14
Q

Match test w/ scale of data for dependent variable

Predicting the value of a variable

A 
Logistic regression (nominal)
OLS regression (interval or ratio)
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15
Q

Match test w/ scale of data for dependent variable

Predicting the value of a censored variable

A

Cox proportional hazards analysis (nominal)

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16
Q

Mode

A

value that occurs most often

nominal and ordinal

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17
Q

Median

A

value in middle of distribution, 50th percentile

ordinal or interval/ratio

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18
Q

Mean

A

average
(population and sample means)
(interval/ratio)

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19
Q

Normal distribution

A

mean, median, and mode have same value – at top of bell curve

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20
Q

Range

A

difference b/w lowest and highest scores

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21
Q

Variance

A

mean of the squares of all the deviation scores in the distribution (the mean square)

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22
Q

What percentage of the area under the curve falls w/in 1, 2, and 3 SD from the mean?

A

1 SD from mean: 68%
2 SD: 95%
3 SD: 99.7%

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23
Q

Prevalence vs incidence

A

prevalence: number of people w/ disease at given time (chronic)
incidence: number of NEW cases of a disease w/in a certain time period (acute)

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24
Q

Prevalence is affected by…

A

incidence (high incidence inc prevalence)
recovery (high recovery rate dec prevalence)
mortality (high mortality dec prevalence)

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25
Q

Maternal mortality

A

death of woman while pregnant or w/in 42 days of termination of pregnancy from any cause related to or aggravated by the pregnancy or its management

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26
Q

Neonatal mortality

A

rate of infant death during first 28 days after live birth

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27
Q

Infant mortality

A

number of infant deaths in first yr of life for every 1,000 live births

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28
Q

Under-5 mortality (child mortality)

A

probability per 1,000 that a newborn baby will die b/f reaching age 5

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29
Q

Life expectancy

A

how long a person is expected to live, based on yr of birth, current age, and other factors

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30
Q

Health-adjusted life expectancy

A

number of healthy yrs a person is expected to live at birth by subtracting the yrs of ill health

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31
Q

Yrs of potential life lost

A

estimating the avg time a person would have lived had he or she not died prematurely

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32
Q

Quality-adjusted life yrs

A

measure of the value of health outcomes

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33
Q

Disability-adjusted life yrs

A

sum of the years of life lose due to premature mortality in the pop and the yrs lost due to disability

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34
Q

Why use relative risks?

A

stable across populations with different baseline risks and
are, for instance, useful when combining the results of
different trials in a meta-analysis

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35
Q

When are relative risks used vs odds ratios?

A

Relative risks: when prospective cohort studies or RCTs are conducted
Odds ratios: used for case-control studies b/c we do not know the true incidence of a disease/outcome

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36
Q

Validity

A

IS THERE BIAS? did the study measure what it claimed to test?
how accurate is the study?
is there bias (systematic error)?

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37
Q

Internal validity

A

are the results of the study valid for the pop studied?

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38
Q

External validity

A

are the results of the study valid for the larger pop? are they generalizable?

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39
Q

Reliability

A

HOW PRECISE ARE THE RESULTS?
do you get similar results if you measure more than once?
is the study precise in measurement?

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40
Q

3 measures of reliability

A

test/retest reliability
repeatability and reproducability
precision of measure

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41
Q

Types of bias in external validity

A

Sample size too small
Volunteers used
Inclusion and exclusion criteria too select

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42
Q

Efficacy vs effectiveness

A

Efficacy: determine whether an intervention is successful under IDEAL circumstances
Effectiveness: determine whether an intervention is successful under REAL WORLD clinical settings

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43
Q

Types of bias in internal validity

A 
Measurement or info bias
--recall bias
--ascertainment bias
Intervention bias
Attrition bias
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44
Q

Types of bias in internal validity
Measurement or info bias
+ 2 types

A

were the predictor and outcome variables measured accurately?

  • -recall bias: participants may not remember past events
  • -ascertainment bias: researchers or participants have knowledge of who is receiving the intervention, lack of blinding
45
Q

Types of bias in internal validity

Intervention bias

A

did the authors select an unusually high dose for the comparison drug?

46
Q

Types of bias in internal validity

Attrition bias

A

loss to follow-up b/c too many people drop out, lack of intention-to-treat analysis

47
Q

Ecological fallacy

A

conclusions about indv are based only on analyses of group data

48
Q

Hawthorne effect

A

people who know they are being studied may modify their behavior or feelings

49
Q

Confounding varaibles

A

true effect is due to an unmeasured variable that affects the results, lack of randomization to intervention and control groups

50
Q

Case control
Cohort study
Cross-sectional
*these are all types of what kind of research?

A

Case control: outcome –> exposure
Cohort: exposure –> outcome
Cross-sectional: snapshot at one time period AKA prevalence/frequency survey
*all are analytic (observational, primary)

51
Q

Case control study: cannot calculate what measures?
Why?
What type of study allows for calculation of these?

A

Cannot calculate relative risks or attributable risks b/c no pop denominator
*cohort study: can calculate incidence rates, relative risks, attributable risks

52
Q

PICOT stands for…

A 
P: pop
I: intervention
C: comparison
O: outcome
T: time
53
Q

Health disparities

A

Health disparities are preventable differences in the burden
of disease, injury, violence, or opportunities to achieve
optimal health that are experienced by socially
disadvantaged populations

54
Q

Peer review

A

Peer review
is the critical assessment of manuscripts
submitted to journals by experts who are not
part of the editorial staff

55
Q

FRISBEE stands for

why is it important?

A 
Follow-up
Randomizaiton
Intention-to-treat
Similar baseline
Blinding
Equal treatment
Equivalence to your pt
**validity of research
56
Q

FINER stands for

A 
Feasible
Interesting
Novel
Ethical
Relevant
57
Q

PPICO stands for

A 
Problem
Patient/population
Intervention
Comparison 
Outcomes
***clear clinical question in systematic review
58
Q

3 models for Meta-Analysis

A

Fixed effects model: any difference found among study results due to chance
Random effects model: difference b/w study results due to chance and other effects
–popular when interventions thought to be more variable
Bayesian Meta-Analysis

59
Q

Forest plot

A

Quickly visualize the results of individual

studies and possibly for pooled data

60
Q

L’Abbe plot

A 
Quickly shows the
amount of contribution
of individual studies to
the outcome
sample size is proportional to circle size
61
Q

Funnel plot

bias unlikely vs likely

A

bias unlikely: dots to left and right of zero

bias likely: dots to right of zero

62
Q

Causes of publication bias

A

reporting bias
true heterogeneity
data irregularities
chance

63
Q

Qualitative sources of heterogenetiy among studies

A

Patients in studies (differences in gender, age range, disease state)
Interventions (drug vs. placebo, drug A vs drug B)
Outcomes (death, inc chance of MI)
Clinical research design of indiv studies

64
Q

Tests for heterogeneity

A

Mantel-Haentszel Chi-Square test
Breslow-Day Test
Cochran’s Q Test
I^2 statistic

65
Q

Positively skewed
Normal
Negatively skewed

A

Pos: mean and median to the RIGHT of the mode
Normal: mean, median, mode are the same
Neg: mean and median to the LEFT of the mode

66
Q

Standard normal or Z distribution has a mean of ___ and a SD of ____

A 
mean = 0
SD = 1
67
Q

For normal distribution to be fully defined, what 2 measures must be known?

A

mean

SD

68
Q

Test statistic

A

measures the degree to which observation varies from predicted

69
Q

Type of data used with these tests:
Student’s T test
Chi-squared test
ANOVA

A

Student’s T test: continuous data
Chi-squared test: categorical data and proportions
ANOVA: comparing means of 2 or more pops

70
Q

Null hypothesis

A

the intervention being studies has NO EFFECT

71
Q

Type I error

A

incorrectly concluding that an effect exists when it does not (errouneous REJECTION OF NULL HYPOTHESIS)
**worry more about type I errors

72
Q

Type II error

A

failing to recognize an effect that truly exists (erroneous ACCEPTANCE OF THE NULL HYPOTHESIS)

73
Q

Alpha

A

prob of making a type I error

74
Q

Beta

A

prob of making a type II error

75
Q

To reduce alpha and beta, need to…

A

inc sample size

76
Q

P value

A

prob of obtaining the observed results if the null hypothesis is true

77
Q

Power

A

prob of finding an effect if it truly exists (1-beta)

78
Q

Parameter estimation

A

determining the plausible range of values for a parameter of interest in a pop or experimental group

  • -point estimate
  • -CI
79
Q

To inc the degree of confidence, how do you have to change CI

A

wider CI to inc degree of confidence

80
Q

The mean of sample means should be ___ the mean of the whole pop
The SD of the sample means will be ___ the SD of the pop

A

Mean of sample means should be the SAME as the mean of the whole pop
SD of the sample means should be LESS THAN the SD of the pop

81
Q

Odds ratio

A

measures the degree to which exposure to a risk factor or a treatment changes the odds of experiencing an outcome

82
Q

Non-inferiority margin

A

maximum acceptable loss of efficacy

83
Q

Prob of type I error ___ with number of independent hypotheses tested

A

increases

84
Q

Bonferroni correction

A

adjust p values when doing multiple hypothesis testing

alpha = 0.05/# hypotheses tested rather than alpha = 0.05

85
Q

Sensitivity

A

how reliably does the test pick up disease when present?
to calculate, only consider pts known to have disease
neg result on highly sensitie test rules out disease

86
Q

Specificity

A

does this test avoid false-pos?
to calculate, only consider pts who do not have the disease
pos result on a highly specific test rules in disease

87
Q

Pre-specified hypotheses

A

based on prior research, clinical/biochemical reasoning, or other first principles
precisely defined in study protocol

88
Q

Post-hoc

A

even if highly statistically significant, not proof of association or causality
–to be convincing, findings must be both improbable and have a plausible explanatory mechanism

89
Q

ANOVA

A 
more than 2 groups
F statistic (variance b/w and within groups)
If have more variance b/w groups than within the groups, you will get a bigger F statistic which will be more likely to be stat sig
90
Q

Repeated measure ANOVA

A

when longitudinal data collected

–each person has repeated measures of the dependent variable

91
Q

Therapy vs prognosis studies

A

Therapy: involve experimental intervention by researcher
–compare groups based on treatment
Prognosis: observational
–look for associations b/w variables

92
Q

Parametric tests

A 
normally distributed dependent variable
ratio or interval level data
measures are independent
usually used to examine means (t tests, ANOVA)
--sometimes difference in variance
93
Q

Nonparametric tests

A

make NO assumptions about normal distribution
more conservative than parametric
used for analysis of medians and proportions
used for ranked data
used for nominal or ordinal data

94
Q

Wilcoxon rank sum test AKA Mann-Whitney U test

A

nonparamentric test
when assumptions for t test don’t hold
ranks scores from lowest to highest
ranks analyzed as though they were original observations
null hypothesis: means of the ranks are equal

95
Q

Independent vs dependent variable

A

independent: predictor
dependent: outcome

96
Q

Dose-response relationship

A

in drug studies, what is the largest, most effective dose w/o serious side effects?

97
Q 
Types of regression
ordinary least squares
poisson (explain)
logistic (explain)
cox proportional hazards analysis (explain)
hierarchical linear modeling
A

Poisson (count dependent variable)
Logistic (dichotomous dependent variable)
Cox proportional hazards analysis (survival analysis)

98
Q

Logistic regression

A

predicting a binary (dichotomous outcome)
predicts the probability of the outcome variable
regression coefficients can be transformed into odds ratios w/ CI
used for multivariate analysis

99
Q

First order interactions

A

the relationship b/w an independent variable and the dependent variable is conditional upon a second independent variable

100
Q

Kaplan-Meier Curve

A

used with survival analysis
x axis: exposures
y axis: percent of event-free subjects

101
Q

Relationship b/w CI width and sample size

A

CI width inc as sample size dec

–seen in survival curve as people die off

102
Q

Survival analysis

A

censored observations
Kaplan-Meier curves plot timing of events
Cox proportional hazard analysis for multivariate analysis
based on regression analysis

103
Q

What tests use multilevel modeling?

A

logistic regression

OLS regression

104
Q

Prevalence and Diagnostic tests

A

Prevalence: tells dr the prior prob of the disease

Diagnostic test: alters the disease prob estimate

105
Q 
How are these measures affected by inc in prevalence?
sensitivity
specificity
PPV
NPV
A 
sensitivity and specificity remain constant
PPV inc
NPV dec
**inc true pos
**inc false neg
106
Q

ROC curves

A

method to find best cut point
used to compare tests
–test is best when has greater area under the curve
–best cut point is at the top of the curve

107
Q

Downfall of screening tests

A

increases number of false pos

108
Q

CAGE screening questionaire

A

Cut down
Annoyed (others)
Guilty
Eye opening

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