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Midterm CDM > Statistical Tests > Flashcards

Statistical Tests Flashcards

1
Q

What does Relative Risk/Risk Ratio do?

A

● Measure of risk based on comparison of disease (or other health outcome)
incidence in two distinct groups

● Ratio of probability (percentage) of event occurring (or not occurring) in exposed
group vs. non-exposed group

● Negative exposure (toxin)= incident rate exposed/unexposed

● Positive exposure (prenatal care)= incident unexposed/exposed
Compares subgroup vs. entire population

● Commonly used in RCT’s and cohort studies

● RR of 1 means there were not differences between groups

● RR <1 means risk of bad outcome decreased
● RR >1 means risk of bad outcome is increased w/ intervention

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2
Q

What are Odds Ratios?

A

Odds of disease between two groups (happening vs. not happening)

Estimate of association (estimates the RR)

Gambling

Compares odds of event in one group to odds in comparison group

Overestimates (this number is always higher than relative risk!!!)

Commonly used in case control studies in epidemiological research

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3
Q

What do both RR and OR Ratios do?

A

Both compare the likelihood of an event to occur between two distinct groups

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4
Q

How are RR and OR Ratios different?

A

RR is easier to interpret & is consistent with the general intuition; Comparison b/w subgroup & entire population.. rather than… subgroup & remained population

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5
Q

Case-Control Studies in context of RR Ratios:

A

Case-control designs: limit RR calculation due to cases being selected on basis
of disease rather than exposure (RR compares exposed to unexposed)

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6
Q

When is RR & OR Comparable?

A

When the studied is rare

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7
Q

When does OR overestimate RR?

A

OR overestimates RR when the disease is more common → should be avoided if
RR can be used

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8
Q

Hypothesis Testing:

What is Hypothesis?

A

Assumed proposition

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9
Q

Hypothesis Testing:

What is Alternative Hypothesis?

A

Alternative Hypothesis is a Prediction that some observed difference is significant & due to knowable cause

~Statement there is a difference between groups not attributed to chance alone

~CAN accept the alternative hypothesis!

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10
Q

Hypothesis Testing:

Null Hypothesis:

A

● Reject null hypothesis or fail to reject null hypothesis
YOU CANNOT ACCEPT A
NULL HYPOTHESIS (you can only find evidence against it)

● Prediction that observed difference is due to chance alone and not due to
systematic cause

● Statement of “no difference”

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11
Q

How to Test the Null:

A

Type I and II Errors (used to test Null)

Type I: False positive (alpha)

Reality: no difference between groups

Study shows difference: error!

Study shows no difference: correct!

Type II: False negative (beta)

Reality: difference exist between groups

Study shows difference: correct!

Study shows no difference: error

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12
Q

What is False Positive and False Negative?

A

False Positive = good quality item gets rejected

False Negative = poor quality item gets accepted

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13
Q

What is Nominal?

A

Nominal: label or category without rank or order (mutually exclusive)

Ex. Male/female/dead/alive/pass/fail

Uses chi-square test

Tests difference in proportions

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14
Q

What is Ordinal?

A

Ordinal: label or category with some meaningful order or sequence

Not measured- without definite boundaries/levels

Ex. Severity of disease

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15
Q

What is Linkert Scale?

A

Linkert scale: strongly agree, agree, disagree, & strongly disagree

Uses Mann/Whitney

Tests differences in rank order

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16
Q

What is Interval?

A

Interval: scaled measure with an arbitrary zero point (temperature)

Difference between levels is meaningful

T-test or ANOVA

Tests differences in means

Used for chemists i.e. so irrelevant (sorry chemists)

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17
Q

What is Ratio?

A

Ratio: scaled measure with an absolute/true zero point(test score)

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18
Q

Chi Square =

A

Nominal Data

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19
Q

Mann-Whitney =

A

Ordinal Data

20
Q

ANOVA =

A

Interval/ratio with 3+ independent groups

21
Q

T-Test/paired T-Test =

A

Interval/Ratio w/ 2 Independent Groups

22
Q

Inferential Statistics:

A

….?

23
Q

Confidence Intervals:

A

Tells you most likely range of unknown population average

Most likely range within which the true size of effect lays

Confidence that if anyone reproduced this study they would have same results
due to independent variable(s)

24
Q

What 3 things Impact the width of CI (Confidence Interval)?

A

Confidence level: typically 95%

Variability: standard deviation

Sample size: smaller sample sizes generate wider intervals
P values do not affect

If the CI includes 1 the effect isn’t significant

25
Q

What is P Value?

A

-Tests likelihood of differences occurring by chance alone

26
Q

How is P level determined?

A
  • P-level is predetermined probability researcher is willing to make a type I error
  • AKA “Alpha level”
27
Q

What does P < 0.5 mean?

A

-P < .05 means a 5% chance observed difference was due to chance & a 95%
confidence that results were due to independent variable(s)

28
Q

What does P < 0.001 mean?

A

-P < .001 means a .1% chance observed difference was due to chance & a 99.9%
confidence that results were due to the independent variable(s)

29
Q

What do we do with a P-Score equal to or less than 0.05?

A

P-SCORE EQUAL TO OR LESS THAN .05 MEANS WE REJECT NULL &

ACCEPT ALTERNATIVE

30
Q

What are the effects of Size in Statistics?

A

● Statistical significance (p-value) indicates difference between two groups
● Effect size describes magnitude of difference
How much more effective?

● We can standardize difference and compare it to 0

● Standard measure that can be calculated from various statistical outputs

● Standardized mean effect, expresses the mean difference between two groups in
standard deviation units (Cohen’s d)
Need mean and standard deviation for each group
Values calculated for effect size range from -3 to 3 just like the standard
deviation
Standard interpretation: .8= large .5=moderate .2= small

31
Q

What is Experimental Event Rate (EER)?

A

EER = event rate in the treated/affected group

32
Q

What is Control Event Rate (CER)?

A

CER = event rate in the control/unaffected group

33
Q

What does Absolute Risk Reduction (ARR) do?

A

ARR:
Compares treatment effectiveness (CER-EER)

Difference in outcome rates between control and experimental groups

Inverse of NNT

How much does the treatment reduce the risk?

34
Q

What does Attributable Risk do?

A

measure of the prevalence of a condition or disease.

Given a group of people exposed to a risk, it’s the fraction who develop a disease or condition.

Put another way, AR is the cases that would be eliminated if the exposure were also eliminated.

Opposite of ARR(EER-CER)«

35
Q

What is Relative Risk Reduction (RRR)?

A

RRR = Percentage of original risk removed; Reduced Risk (ARR/CER)

Not a good number/test (can inflate numbers or findings)

“More or less likely to happen”

36
Q

Describe Number Needed to Treat-Prevent Bad Outcome:

A

Patients to treat for 1 to have a benefit

Number of patients needed to prevent one additional bad outcome

Ideal NNT=1 (this means everyone improves with treatment- higher is less
effective and negative # indicates harmful)
1/ARR (inverse of absolute risk)

37
Q

CER =

A

control event rate: event rate in the control/unaffected group

38
Q

EER =

A

experiment event rate: event rate in treated/affected group

39
Q

AAR =

A

absolute risk reduction: compares treatment effectiveness (CER-EER) → How
much does the treatment reduce the risk

40
Q

Sensitivity =

A

of people who have the disease and test +/

of people who have the disease

Highly sensitive tests catch the disease every time:
sometimes they are wrong (false +)

Good at detecting/screening

41
Q

SnOUT =

A

(sensitive test if it is negative rules disease OUT)

Probability that subject with the disease will screen positive for that disease ?? what do they mean exactly????

42
Q

Highly sensitive tests can catch the disease nearly every time-

A

rules OUT
disease when the result is negative

NOT dependent on populations

Sometimes they are wrong (false +): say you have the disease and you really
don’t

Good at detecting & screening

If you test negative you can be almost 100% sure you don’t have the disease

43
Q

Specificity =

A

of people who do not have the disease and test -/

of people who do not have the disease

Highly specific tests are rarely wrong

But sometimes they miss the diagnosis (false -)

Good at being right/confirming the diagnosis

Spin (specific test if it is positive rules a disease IN)

Sometimes miss the diagnosis (false -): say you don’t have the disease and you
really do (bummer)

Good at being right/confirming the diagnosis

*CANNOT HAVE A TEST THAT IS HIGH IN BOTH SENSITIVITY & SPECIFICITY;
INVERSELY RELATED

44
Q

What is Positive Predictive Value?

A

Chance to have disease when test was positive

Chance that when test is + you actually have the disease

PV+: true positive/ (true positive + false positive)

45
Q

What is Negative Predictive Value?

A

Chance to NOT have disease when test was negative

Chance that when test is – you actually do NOT have disease

PV-: true negatives/ (true negatives + false negatives)

46
Q

Statistical Significance vs. Clinical Significance

A

Statistical- used in hypothesis testing

Clinical- practical importance of a treatment effect- whether it has a real, palpable,
noticeable effect ton daily life

47
Q

Describe Critical Thinking Competency:

A

ASK: convert information needs into a searchable question

ACQUIRE: search with efficiency for best evidence to answer 


APPRAISE: critically
appraise evidence 


APPLY: results in clinical practice


ASSESS: evaluate clinical
decision making skills

Midterm CDM (8 decks)

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