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IPE - Winter OD2 > Midterm 2 > Flashcards

Midterm 2 Flashcards

(120 cards)

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1
Q

What is the purpose of t-test?

A

to compare 2 groups of scores
(does not require a large sample size but it does help)

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2
Q

What are the appropriate types of variables for a t-test?

A

Independent variable: dichotomous
Dependent variable: continuous

Null hypothesis: no difference between 2 groups being compared

Alternate hypothesis: 2 groups of scores are different

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3
Q

What is the purpose of an UNPAIRED t-test?

A

when comparing 2 groups of scores, the 2 groups of scores are INDEPENDENT of each other

AKA 2 group t test, 2 sample t test, independent group t test

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4
Q

For an Unpaired t test, you are (adding/subtracting) the average score of one group to the score of another group

A

subtracting

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5
Q

For an unpaired/paired t test, how does t get bigger?

A

get more participants

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6
Q

What are the degrees of freedom of an unpaired t test?

A

N (total number of observations of the study) - 2

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7
Q

What are degrees of freedom used for?

A

t(#) where # = degrees of freedom

It is a statistic that is a shorthand indicating sample size. It tells us the shape of the distribution

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8
Q

If the t test was t(698) what are the degrees of freedom of this example?

A

df = N - 2
698 = N -2
N = 700

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9
Q

How do you get closer to the normal distribution graph with t distribution?

A

obtain MORE observations

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10
Q

What is the critical value of t?

A

the smallest absolute value of t needed for the observations to be within the alpha level of statistical significance

unique for every freedom

You would need the t value to be greater than the critical value to be statistically significant

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11
Q

Ex: If 2 tailed t test t(698) = 0.75 and p = 0.57 and the critical value is 1.96, does this meet critical value? Is it significant?

A

No. 0.75 < 1.96 so does not fall in statistically significance

It is not statistically significant because p > 0.025

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12
Q

What is a paired t-test?

A

when comparing two groups of scores that are related in pairs (AKA matched groups t test, dependent t test)

scores are paired/linked with one another in some way

ex: for every subject, left eye receives the drug and right eye receives placebo, compare dryness of each eye

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13
Q

Paired t test involves (adding/subtracting) 2 values in a pair and then averaging that difference.

A

subtracting

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14
Q

For the degrees of freedom of a paired t test what is the formula?

A

df = N - 1
N (total number of observations)

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15
Q

How does the graph for t distribution look?

A

it is infinite on a graph and NEVER touches the x axis

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16
Q

What is ANOVA?

A

Analysis of Variance

compare average score of 2 or more groups of scores

independent variable: categorical
dependent variable: continuous
(similar to t test but more groups compared)

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17
Q

ANOVA uses what size of groups and what is the statistic called?

A

small # of groups

F statistic

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18
Q

What is F in ANOVA?

A

variation between groups/ variation WITHIN groups

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19
Q

How is variation measured?

A

mean squares (MS)

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20
Q

What does variation between groups mean?

A

how different are the group means compared to the grand mean?

GRAND mean: average score of all observations from all groups

ex: hospital A vs all the hospital means

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21
Q

What does variation within groups mean?

A

not everyone in a group is identical

ex: scores within hospital A

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22
Q

If the groups are very different in an ANOVA test?

A

MS between < MS within

F>1

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23
Q

If the average score of each group is the same (or very similar?

A

MS between ~ MS within

F~1

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24
Q

What is the F distribution?

A

infinite number of f distributions

shape of graph depends on degrees of freedom

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25
What is the critical value of F?
the value for which 5% of the area is under the curve and larger than that value
26
What is the formula for degrees of freedom of NUMERATOR for F?
df = #groups - 1
27
What is the formula for degrees of freedom of the DENOMINATOR for F?
univariate ANOVA (1IV, 1DV) df = N - # groups
28
What does the numerator of df in F mean?
how many groups are being compared ex: if comparing 4 groups, df between = 3
29
What does the denominator of df in F mean?
total sample size ex: if you have 250 participants distributed among 4 groups, df within = 246
30
What does the univariate ANOVA F(3, 246) mean?
4 total groups being compared with 250 sample size/observations
31
When interpreting ANOVA with 3 or more groups, what conclusion can you draw?
you can reject the null hypothesis and there is SOME difference between the groups that you cannot exactly determine. You just know that they are not all equal (statistically significant)
32
How would you find out how the 3 or more groups are different in an ANOVA test?
perform post hoc tests follow up tests/ pairwise comparisons
33
What are the 3 post hoc tests we need to know?
1. Tukey's test 2. Fishers Least Significant Difference (LSD) 3.Scheffe's Method
34
What are descriptive statistics?
statistics that help you describe characteristics of your sample. Primarily measures the central tendency and variability
35
What are some descriptive statistics we have learned so far?
raw scores arithmetic mean median mode st deviation number of participants
36
What are inferential statistics?
They describe the likelihood of your results occurring by chance or generalizing beyond your sample you are inferring things beyond just your sample AKA statistical inference
37
What are some inferential statistics we have learned so far?
students t point estimate confidence interval std error beta F (ANOVA)
38
What is absolute risk?
measure of likelihood of a certain event happening ex: a smoked has 3% overall chance of dying of lung cancer
39
What is relative risk? (risk ratio)
the likelihood of disease among "exposed" compared to the likelihood of disease among "unexposed" does not provide any info to absolute risk ex: a smoked is 7x more likely to die of lung cancer than a non-smoker
40
What is the equation of absolute risk?
(participants with disease present in exposed or unexposed) / (total participants in unexposed)
41
What is the equation of relative risk? (ratio)
(absolute risk of disease in exposed) / (absolute risk of disease in unexposed)
42
What is attributable risk and how is it determined?
the amount of risk that can be attributed to the risk factor absolute risk (exposed) - absolute risk (unexposed/baseline risk)
43
How do we interpret Risk ratio?
RR > 1 = positive association of risk factor and disease RR < 1 = negative association of risk factor and disease (protective factor) RR = 1 baseline risk or no association
44
If RR = 5, what does this mean?
5x risk of disease for those exposed to the risk factor vs those that are unexposed
45
If you have a 2x more likely of getting a disease then the change in risk increased to?
100% increase in risk (or 2x the risk) **1 = baseline so if you add 1 more to that you have a 100% increase because you doubled 1**
46
What is the interpretation of RR of 0.80?
risk of outcome in the exposed group was reduced by 20% relative to the unexposed group
47
What is the interpretation of RR of 3.30?
risk of outcome in the exposed group was increased by 230% relative to the unexposed group OR the outcome was 3.3 times more likely to occur in the exposed group than in the unexposed group
48
A study finds that RR = 1.7 and 95% CI: 0.9-2.7. Is there a significant association?
NO because 1.0 is within the range of the CI
49
What is the confidence interval?
The range of values, with 95% confidence that is likely to contain the true effect
50
How does the relative risk relate to the CI?
if the CI contains a value of 1.0 then the null hypothesis is not rejected = not significant statistical association between risk factor and disease
51
How would you interpret a wide CI?
true value lies within a large range of possible values = less precise
52
How would you interpret a narrow CI?
true value lies within a small range of possibilities = more precise
53
A study finds RR = 1.7 and CI = 1.02-2.6. Is there a significant association?
YES, because 1 lies in the range of the CI
54
What is the definition of risk?
chance of outcome of interest out of all possible outcomes
55
What is the definition of odds?
the ratio of the change of outcome of interest occurring to the change of the outcome not occuring
56
Where does relative risk come from?
Prospective cohort studies = follow forward in time finding people who will develop the disease
57
Where does the odds ratio come from?
case control studies = follow backward in time already determine who has the disease
58
What is the equation for odds ratio?
odds of case (disease) / odds of control (no disease)
59
How do you interpret odds ratio?
similar to relative risk (in terms of CI)
60
What is number needed to treat?
number of people treated to have impact on one person (how likely is it that a therapy will help an individual person?)
61
What is the number needed to harm?
number of people treated to harm one person
62
What is the EER (experimental event rate) formula?
probability in treatment group that ended up with an event out of the total #
63
What is the CER (control event rate) formula?
how many individuals in your control group ended up with the event out of the total number in the control group
64
What is the absolute risk reduction (ARR)?
CER - EER (in absolute value)
65
NNT is in decimal form?
1/ARR
66
NNT is in percent form?
100/ARR
67
What are some considerations for NNT?
in clinical endpoint is devastating (death, heart attack), drugs with high NNT may still be indicated
68
NNT values are time specific so you must
- compare studies with similar time frames - must think about timeframe in treatment decisions
69
In an ideal world what would you consider a good NNT?
1 because every patient with treatment benefits
70
What is a good range for an NNT?
2-5 = effective treatment
71
What is a good NNT for prophylactic treatments (especially those devastating ones)?
20-100
72
NNH is calculated for?
every side effect/adverse effect ex: death, blood clot, bleeding
73
Do you want a high NNH or low?
high!
74
What is statistical power?
the probability that you will find statistical significance with a given sample size, if the alternate hypothesis is true
75
What is statistical power analysis?
a statistical analysis conducted BEFORE you begin a study that estimates the necessary sample size to detect a statistically significant relationship (in percentage)
76
As you have larger between group differences, you need a (smaller/larger) number of observations?
smaller
77
As you have smaller within group differences, you need (smaller/larger) number of observations?
smaller
78
A type 1 error is what?
reject the null hypothesis BUT there is NOT statistical significance in the test so you have a false POSITIVE this is also known as statistical alpha
79
A type II error is?
you know that there IS statistical significance in the variables (falling under statistical beta) but you fail to reject the null hypothesis you you have a false NEGATIVE you should have found something but you didnt
80
Are statistical beta and regression beta the same thing?
NO
81
For statistical alpha of 0.5%, what type of error does this fall under?
type I
82
What statistical power do we ideally want?
80% (higher scores=better outcome)
83
Most studies want to avoid what type of error?
type I (statistical alpha) FP - don't want to claim something works but it actually doesnt
84
Risk of type I error and type II error are (inversely/directly) proportional
inversely
85
What is power analysis?
a procedure that estimates an appropriate sample size to find an effect usually performed with computer programs
86
What is the purpose of Pearson's correlation?
measures how closely related 2 CONTINUOUS variables are (linear relationship between 2 numerical measurements)
87
What are the ranges for Pearson's Correlation?
Range: -1 to +1 (+/-) indicate slope r=-1 perfect negative correlation r=1 perfect positive correlation r=0 no correlation
88
The smallest correlation would be a Pearson's coefficient of?
something closest to 0
89
How do you interpret Pearson's r? r(698) = -0.09
there is a negative linear correlation (-)0.09 698 = degrees of freedom df = N - 2 N = 700 observations
90
What is considered a small correlation?
r=0.10 or -0.10
91
What is considered a medium correlation?
r=0.30 or -0.30
92
What is considered a large correlation?
r=0.50 or -0.50
93
What is considered a stronger correlation?
more clustering around the line of best fit (r closer to -/+1)
94
How would you interpret a straight horizontal or vertical line for Pearsons correlation?
r = 0 (non zero slope)
95
What Pearsons coefficient would be a graph with a NONlinear graph?
nonzero slope r=0
96
What is a validation study?
comparing a new test(experimental test) to the gold standard
97
For correlation analysis and linear regression analysis where you are comparing 2 variables you report (r/r^2)?
r
98
For multiple regression analysis you report?
r^2
99
What is the effect size of an r^2?
r^2=0.01 small r^2=0.10 medium r^2=0.25 large range = 0 to 1
100
What is the chi squared test?
counting things and trying to see if one group has more of something than of something else IV = categorical DV = frequency (converted into a percent)
101
Chi squared in common in what type of research?
adverse events
102
What is analysis of covariance? (ANCOVA)
compares the scores of 2+ groups using statistic F while CONTROLLING a potential confound (COVARIATE) IV: categorical DV: continuous covariate: continuous
103
When can you assume they used ANCOVA data?
analysis was adjusted, analysis was estimated, analysis was corrected for
104
What is the difference between clinical and statistical significance?
clinical significance - importance of a research result in terms of the symptom relief you can expect for your patient statistical significance - how likely something occured to chance
105
What are observational studies?
hypothesis generating Ex: case reports/studies, cross section, case control, cohort studies
106
What studies are level I (HIGH)?
well designed randomized controlled trials
107
What studies are level II (Good)?
well designed controlled un-randomized trials, cohort or case control analytic studies, multiple time series with or without intervention
108
What studies are level III (POOR)?
case reports/series cross sectional studies reports of expert committee/organizations
109
Random subject selection underpins what?
statistical inference which helps ensure external validity
110
Random assignment (prevents/promotes) selection bias, which means that differences in study outcomes are due to study treatments and not from confounding factors
prevents
111
Random selection refers to
sampling
112
Random assignment refers to
group assignment
113
What is sensitivity?
accuracy of test to correctly identify all individuals in a population who have a particular disease true positive/detection rate sensitive to disease
114
What is specificity?
accuracy of the screening procedure to correctly identify those who do no have the disorder true negative specific to health
115
What is considered good range to have specificity and sensitivity?
70% sensitivity and specificity
116
What are the basic concepts in epidemiology?
morbidity mortality indidence prevalence
117
What is incidence?
probably that healthy people will develop a disease over a specific period of time rate at which new disease occurs in a group of people who are disease free AKA attack rate, risk, probability of developing a disease come from prospective cohort studies
118
How do you calculate incidence?
number of new cases of disease during specific period (1yr)/size of population at risk during specific period denominator is usually standardized (100,1000,10000)
119
What is prevalence?
probability of people having a specific disease at a given time (coming from cross sectional studies) these people already have the disease from the past at a given time
120
How do you calculate prevalence?
number of existing cases during specified point or period/ size of population at risk during specified point or period expressed as percentage expressed relative age, gender, race, geographic regions

IPE - Winter OD2 (2 decks)

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