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Epidemiology > Unit 3 > Flashcards

Unit 3 Flashcards

1
Q

What are the 3 attributes of study variables?

A

Order / Magnitude

Consistency of scale / equal distances

Rational absolute zero

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2
Q

What are the 3 levels of data measurement?

A

Nominal
Ordinal
Interval/ Ratio

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3
Q

Nominal

A

Dichotomous/ Binary

Non-ranked (non-ordered)

Named categories

 - categorical data
 - can be more than 2 categories 
 - ex: 2 genders, 2 age groups

No order/ magnitude

No consistency of scale or equal distances

Nominal variables are simply labeled- variables without quantitative characteristics

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4
Q

Examples of Nominal variables

A

What is your gender?
- Male or Female

What is your hair color?
- brown vs black vs blonde vs grey vs other

Education level (if made binary)

Smoking vs non-smoking

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5
Q

Ordinal

A

Ordered and order-able

Rank-able categories

Non-equal distance between ranges
- technically can be equal and unequal

Unitless

Yes order/ magnitude

No consistency of scale or equal distances

 - No units or scales 
 - No even spacing between them

Data is collected in categories and can be ordered

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6
Q

Examples of Ordinal variables

A

Pain Scales
- patient decides what each value means

Strongly agree > somewhat agree > Neither > somewhat disagree > strongly disagree

SES
- unitless, broken into categories

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7
Q

Interval/ Ratio

A

Order/ magnitude

Equal Distances

  • unitless
  • equal spaces between scales

Interval

 - Arbitrary 0 value 
 - 0 doesn't mean absence 
 - Can be 0 or negative values

Ratio

- Absolute 0 value 
- 0 means absence of measurement value 
- No negative values 
- Ex: physiological parameters 
      - blood pressure
      - blood sugar
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8
Q

Examples of Interval/ Ratio variables

A

Living siblings and personal age

Height in cm

Speed in m/s

LDL in mg/ dL

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9
Q

Mean

A

Average value

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10
Q

Median

A

Middle value

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11
Q

Mode

A

Most common value

This is the most useful measurement for descriptive statistics

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12
Q

What is descriptive statistics?

A

Tells us about our population

Describes our population

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13
Q

Range

A

Maximum - minimum

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14
Q

Interquartile Range

A

Top 25% = Q3
Bottom 25% = Q1
Middle 50% = Q3 - Q1
- represented the 25% above and below the mean

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15
Q

Variance

A

The average of the squared differences in each individual measurement value and the groups mean

Describes the spread of data

Variance from the mean

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16
Q

Standard Deviation

A

Square root of variance

Restores units of mean

Describes spread of data

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17
Q

Normal Distribution

A

Symmetrical

Mean and median are (almost) equal

Equal dispersion of curve (tails) to both sides of mean

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18
Q

Statistical tests useful for normal- distributed data are known as _____

A

Parametric Tests

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19
Q

Required assumptions of interval/ ratio data for proper selection of parametric tests

A
  1. Normal distribution
  2. Equal variances
    • use Levene’s Test
  3. Randomly derived and independent
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20
Q

Levene’s Test

A

Test used to calculate if data is normally distributed and has equal variance

Used to assess if the variances are different between groups

Null Hypothesis: groups are equal

Tries to show that there is a difference between groups

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21
Q

How to handle interval data that is not normally distributed

A
  1. Use a statistical test that does not require the data to be normally distributed
    • non-parametric tests
    • step down and run ordinal test
  2. Transform data to a standardized value
    • hope that the transformation allows data to be normally distributed
    • z score or log transformation
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22
Q

Positively Skewed

A

Asymmetric distribution with one tail longer than the other

Mean > median
- mean is higher than median

Tail points to the right

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23
Q

Negatively Skewed

A

Asymmetric distribution with one tail longer than the other

Mean < median
- mean is lower than medium

Tail points to the left

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24
Q

What effect do outliers have on skewness?

A

Outliers pull the tails out farther

Contributes to skewness

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25
Q

Skewness

A

A measure of the asymmetry of a distribution

Perfectly normal distribution is symmetric
- skewness = 0

Negative skewness = negatively skewed data

Positive skewness = positively skewed data

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26
Q

Kurtosis

A

A measure of the extent to which observations cluster around the mean
- how peaked a value is

Kurtosis of normal distribution curve = 0

Positive kurtosis = more cluster (around a number)
- closer to a positive value

Negative kurtosis = less cluster (around a number)
- closer to a negative value

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27
Q

68%

A

1 standard deviation away from the mean

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28
Q

95%

A

2 standard deviations away from the mean

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29
Q

99.7%

A

3 standard deviations away from the mean

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30
Q

Null Hypothesis

A

Research perspective which states there will be no true difference between the groups being compared

Most conservative and most commonly utilized

At end of study, either need to accept or reject the null

Can take on the superiority, noninferiority, and equivalency perspectives

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31
Q

Alternative Hypothesis

A

Research perspective which states there will be a true difference between the groups being compared

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32
Q

Type 1 error

A

Alpha

Not accepting the null hypothesis when it is actually true and you should have accepted it

Rejecting the null hypothesis when you shouldn’t have

Ex: Telling a man he is pregnant

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33
Q

Type 2 error

A

Beta

Accepting the null hypothesis when it is actually false and you should not have accepted it

Not rejecting the null hypothesis when you should have

Ex: Telling a pregnant woman she is not pregnant

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34
Q

P value

A

Probability value (alpha)

Based on the probability, due to chance alone, a test statistic value as extreme or more extreme than actually observed if groups were similar (not different)

Represents your chances of being wrong

If p < 0.05, risk of experiencing a type 1 error is acceptably low

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35
Q

T/F: If p value is lower than the pre-selected alpha (5% or 0.05), it is statistically significant

A

True

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36
Q

Do you accept or reject the null hypothesis if p value < alpha?

A

REJECT

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37
Q

What is the interpretation of a p value?

A

The probability of making a type 1 error if the null hypothesis is rejected

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38
Q

If the data is statistically significant and there are 3+ groups, the p value tells you what?

A

Tells you that there is at least 1 difference present

Guaranteed difference between control and most extreme value

Lowest and highest value represent the difference

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39
Q

At baseline, do we want groups to be equal?

A

Yes

At start of study/ baseline, p value should be 1.0

Want p values to start above 0.05

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40
Q

Power

A

The statistical ability of a study to detect a true difference if only one truly exists between group comparisons and therefore the level of accuracy in correctly accepting/ not accepting the null hypothesis

If there is truly a difference between groups, study has high power

Studies are set up to have 80% power

When lose people, lose power

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41
Q

What is the mathematical representation of power?

A

1 - beta
= 1 - type 2 error rate

We allow a type 2 error rate of 20%
- accept 20% of risk of finding an error

1 represents sample size

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42
Q

The _____ people the study has, the _____ the power

A

More

Higher

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43
Q

What are the common elements utilized in determining sample size of a study?

A

Minimum difference between groups deemed significant
- the smaller the difference between groups necessary to be considered significant, the greater the sample size needed

Expected variation of measurement

Alpha and beta error rates and confidence interval

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44
Q

How does sample size affect power?

A

The larger the sample size, the greater the likelihood of detecting a difference if one truly exists

Increases power

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45
Q

What is the number one way you can ensure your study has power?

A

To show the difference between groups if a difference is really present

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46
Q

When is it okay for p values to be non-statistically significant?

A

Start of study

Levene’s Test

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47
Q

What is a caveat of p values?

A

They do not tell us about spread/ dispersion

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48
Q

Confidence Intervals

A

Precision measurement

CI around a group’s differences help reader understand where true value may lie

Calculated at an a priori percentage of confidence that statistically includes the real (yet unknown) difference or relationship being compared

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49
Q

What advantage do confidence intervals have over p values?

A

Tells us about statistical significance and spread

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50
Q

What are confidence intervals based on?

A

Variation in sample

Sample size

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51
Q

If you don’t use the same directional word when interpreting CI, is the data statistically significant?

A

NO

If CI values cross 1.0, data is not statistically significant

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52
Q

When the numbers that make up the CI are on the same side of 0, are the numbers statistically significant?

A

YES

53
Q

Why is it okay to have a high p value for a Levene’s Test?

A

We are okay with accepting the null because it means we are saying the variances are equal so we can thus use an interval test with our data.

54
Q

The bigger the CI, the ____ confidence we have in the precision of our estimate. Why do we see this?

A

Less

With 99% CI, it is harder to show significance

 - less likely to cause type 1 error 
 - more confident that true value lies in interval
55
Q

On Forest plots, the horizontal lines represent _____ and the boxes represent _____.

A

Confidence interval

Values (our data point)

56
Q

What is an important question to consider once you have determined your data is statistically significant?

A

Does statistical significance actually confer meaningful “clinical” significance?

57
Q

What questions do we ask when selecting the correct statistical test?

A
  1. What data level is being recording?
    • Does the data have order or magnitude
    • Does the data have an equal, consistent distance along the entire scale?
  2. What type of comparison/ assessment is desired?
    • Frequencies/ counts/ proportions
  3. How many groups are being compared?
    • 2 or 3 or more groups
  4. Is the data independent or related (paired)?
    • data from the same groups = paired
    • data from different groups = independent
58
Q

Correlation

A

Provides a quantitative measure of the strength and direction of a relationship between variables

Tells you if there is a relationship or not

  • direction ( + or -)
  • magnitude (strong or weak)
59
Q

Partial Correlation

A

A correlation that controls for confounding variables

60
Q

What does a correlation of - 1.0 tell us and look like graphically?

A

Perfectly negative

As 1 variable goes up, the other goes down

  • 45 degree slope
61
Q

What does a correlation of + 1.0 tell us and look like graphically?

A

Perfectly positive

As 1 variable goes up, the other goes up

+ 45 degree slope

62
Q

Nominal Correlation Test

A

Contingency Coefficient

63
Q

Ordinal Correlation Test

A

Spearman Correlation

64
Q

Interval Correlation Test

A

Pearson Correlation

p > 0.05 for a Pearson Correlation means there is no linear correlation but there could be non- linear correlations present

65
Q

Survival Tests

A

Deal with event occurrence / time-to-event

Compares the proportion of events over time or time-to-events between groups

Predict changes in event over time

Asks ‘does frequency of events between groups differ over time?’

  • amounts can be different
  • rates can be different

Used when we want to see changes over time

66
Q

Survival refers to an event that has or has not occurred yet?

A

Has NOT occurred yet

Examples: Death, hospitalization

67
Q

What kind of curve represents survival tests and what are the features of it?

A

Kaplan- Meier curves

If everyone starts at 100, means every patient is free of event

If everyone starts at 0, means no one has had the event

68
Q

Nominal Survival Test

A

Log- rank test

69
Q

Ordinal Survival Test

A

Cox- proportional hazards test

70
Q

Interval Survival Test

A

Kaplan- Meier Test

Assesses time
- time as a variable

71
Q

Regression Test

A

Used to predict the likelihood of an outcome/ association

Allows us to used multiple variables at one time to determine an outcome
- predicting an outcome given a bunch of variables

Provide a measure of the relationship between variables by allowing the prediction about the dependent (outcome) variable knowing the value/ category of independent variables

Can be used to calculate the odds ratio for a measure of association

72
Q

Nominal Regression Test

A

Logistic Regression

73
Q

Ordinal Regression Test

A

Multinomial Logistic Regression

74
Q

Interval Regression Test

A

Linear Regression

75
Q

Pearson’s Chi- square Test

A

Compares 2 groups of independent nominal data

Compares group proportions and if they are different from those expected by chance

Not powerful for small numbers
- no expected cell count of less than 5 observations

Asks “is the proportion of yes or no responses between groups any different?”

Compares real numbers with what they expect to find based on a skewed Chi-square curve

76
Q

Chi- square Test of Independence

A

Used for 3+ groups of independent nominal data

Compares group proportions and if they are different from those expected by chance

Not powerful for small numbers
- no expected cell count of less than 5 observations

Compares real numbers with what they expect to find based on a skewed Chi-square curve

77
Q

Fisher’s Exact Test

A

Used for 2+ groups with expected cell counts of less than 5

Nominal data test

Powerful for small numbers

Still Chi- square like test but handles the problem of small numbers

78
Q

Bonferroni Test of Inequality

A

Used for 3+ groups of independent nominal data

Used to find which groups are different in nominal data set

Adjusts the p-value for the number of comparisons being made
= p -value / number of comparisons you are making

Very conservative

Helps us reduce risk of type 1 error

79
Q

If have statistically significant results in 3+ comparisons, you need to perform _______ ?

A

Subsequent analysis to determine which groups are different
- subsequent analysis = post- hoc testing

Cannot use multiple chi- square/ multiple statistical tests

80
Q

Why can’t you use multiple chi-square/ multiple statistical tests to determine which groups are different?

A

Risk of type 1 error increases with each additional test

81
Q

McNemar Test

A

Used for 2 groups of paired/ related nominal data

Just tells us that there is at least 1 difference between groups
- doesn’t tell us where but can use a Bonferroni test of inequality/ correction to find where differences are

Same principles/ assumptions as Chi- square

82
Q

Cochran Q Test

A

Used for 3+ groups of paired/ related nominal data

Just tells us that there is at least 1 difference between groups
- doesn’t tell us where but can use a Bonferroni test of inequality/ correction to find where differences are

Same principles/ assumptions as Chi- square

83
Q

What are examples of key words for paired data?

A

Pre vs post
Before vs after
Baseline vs end
Start vs end

84
Q

Mann- Whitney

A

Used to compare the median values between 2 groups of independent ordinal data

Also used for interval data not meeting parametric requirements

85
Q

Kruskal- Wallis Test

A

Used to compare the median values between 3+ groups of independent ordinal data

Also used for interval data not meeting parametric requirements

If 3+ group comparison is significant, must perform a post- hoc test to determine where differences are

86
Q

Wilcoxon Signed Rank

A

Used to compare median values between 2 groups of paired/ related data

Also used for non-normally distributed interval data or data that don’t meet all parametric requirements

Neg sign = rank moved down
Pos sign = rank moved up

87
Q

Friedman Test

A

Used to compare the median values between 3+ groups of paired/ related data

Also used for non-normally distributed interval data or data that don’t meet all parametric requirements

If 3+ group comparison significant, must perform a post- hoc test to determine where differences are

88
Q

Student- Newman- Keul Test

A

Post- hoc test that is used for 3+ group comparisons of ordinal and interval data when they are statistically significant

Compares all pair-wise comparisons possible

All groups must be equal in size

89
Q

Dunnett Test

A

Post- hoc test that is used for 3+ group comparisons of ordinal and interval data when they are statistically significant

Compares all pair-wise comparisons possible against a single control

All groups must be equal in size

90
Q

Dunn Test

A

Post- hoc test that is used for 3+ group comparisons of ordinal and interval data when they are statistically significant

Compares all pairwise comparisons possible

Useful when all groups are not of equal size
- Account for lost to follow ups

91
Q

Tukey Test

A

Post- hoc test that is used for 3+ group comparisons of interval data when they are statistically significant

Compares all pairwise comparisons possible

All groups must be equal in size

More conservative than Stident- Newman- Keul test

92
Q

Scheffe Test

A

Post- hoc test that is used for 3+ group comparisons of interval data when they are statistically significant

Compares all pairwise comparisons possible

All groups must be equal in size

Less affected by violations in normality and homogeneity of variances

Most conservative

93
Q

What are the Post- hoc tests that can be used when 3+ group comparisons of ordinal data are statistically significant?

A

Student- Newman- Keul Test
Dunnett Test
Dunn Test

94
Q

What are the Post- hoc tests that can be used when 3+ group comparisons of interval data are statistically significant?

A 
Student- Newman- Keul Test
Dunnett Test
Dunn Test 
Tukey Test
Scheffe Test
95
Q

Student t- Test

A

Used to compare the means of 2 groups of independent interval data

96
Q

ANOVA (Analysis of Variance)

A

Used to compare the means of 3+ groups of independent interval data

Also compares intra- and inter-group variations against a dependent variable

If 3+ group comparisons are significant, must perform a post- hoc test to determine where the differences are

97
Q

ANCOVA (Analysis of Co- Variance)

A

Used to compare the means of 3+ groups of independent interval data with confounders

Compares intra- and inter- group variations of related data against a dependent variable while also controlling for the co-variance of confounders

98
Q

Repeated Measures ANOVA

A

Used to compare the mean values of 3+ groups of paired/ related interval data

Also compares intra- and inter-group variations of related data against a dependent variable

If 3+ group comparison is significant, must perform a post-hoc test to determine where differences are

99
Q

Repeated Measures of ANCOVA

A

Used to compare the mean of 3+ groups of paired/ related interval data with confounders

100
Q

Kappa Statistic

A

A correlation test showing relationship or agreement between evaluators

Consistency of decisions/ determinations

101
Q

K = +1.0

A

Observers perfectly classify everyone exactly the same way

Good agreement

102
Q

K = 0.0

A

No relationship at all between the observers’ classifications above the agreement that would be expected by chance

103
Q

K = -1.0

A

Observers classify everyone exactly the opposite of each other

Poor agreement

104
Q

What is the National Clinical Trials number (NCT)?

A

A unique identifier number assigned by clinicaltrials.gov once research protocol is submitted prior to study initiation

A number all studies are given once registered that allows you to track progress of study

Allows us to know what’s going on and why its going on

105
Q

What is the purpose of the NCT number?

A

Reduce publication bias

- not sharing results because they are bad/ don’t show anything

106
Q

Clinicaltrials.gov is an ___ ___ ___ ___ ___ ___ which offers what kind of information?

A

International Committee of Medical Journal Editors

Acceptable public registry that offers up to date information for locating interventional studies

107
Q

What do readers need in order to accurately assess a study?

A

Complete, clear, and transparent information on the study’s methodology and findings

108
Q

What does a checklist help when reviewing medical literature?

A

Provides a stepwise, systematic review of the published medical literature

109
Q

What type of studies is a CONSORT checklist used for?

A

Randomized interventional trials

Interventional Studies

110
Q

What does CONSORT stand for?

A

Consolidated Standards of Reporting Trials

111
Q

CONSORT can be extended and used for _____?

A

Non-inferiority Trials
Equivalence Trials
Cluster Trials
Pragmatic Trials

112
Q

Define: Pragmatic Trials

A

Randomized, controlled trial whose purpose is to inform decisions about clinical practice

Philosophy as a continuum not a dichotomy

113
Q

What type of studies is a PRISMA checklist used for?

A

Systematic reviews of multiple randomized trials

Interventional Studies

114
Q

What does PRISMA stand for?

A

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

115
Q

What type of studies is STROBE used for?

A

Observational Studies

- cohort, case-control, cross-sectional

116
Q

What does STROBE stand for?

A

Strengthening the Reporting of Observational Studies in Epidemiology

117
Q

STROBE can be extended and used for _____?

A

Molecular Epidemiology Studies (STROBE-ME)

Genetic Association Studies (STREGA)
- Strengthing the reporting of genetic association studies

118
Q

What type of studies is TREND used for?

A

Reporting evaluations with non-randomized designs of behavioral and public health interventions

Non-randomized studies

119
Q

What does TREND stand for?

A

Transparent Reporting of Evaluations with Non-randomized Designs

120
Q

What type of studies is REMARK used for?

A

Tumor marker prognostic studies

121
Q

What does REMARK stand for?

A

Reporting Recommendations for Tumor Marker Prognostic studies

122
Q

What type of studies is GRIPS used for?

A

Genetic risk prediction studies

123
Q

What does GRIPS stand for?

A

Genetic Risk Prediction Studies

124
Q

What type of studies is STARD used for?

A

Diagnostic Studies

Single diagnostic study

125
Q

What does STARD stand for?

A

Standards for the Reporting of Diagnostic Accuracy Studies

126
Q

What type of studies is QUADAS-2 used for?

A

Systematic reviews of multiple diagnostic studies

Diagnostic Studies

127
Q

What does QUADAS-2 stand for?

A

Quality Assessment of Studies of Diagnostic Accuracy in Systematic Reviews, 2nd edition

128
Q

DOI number

A

Digital Object Identifier number

Gives us location of study on internet

Tells us where we can find print version of article

Unique and specific to each article

Epidemiology (3 decks)

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