biostat Flashcards

(46 cards)

1
Q

formula for RR

A

relative risk

EER/CER

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

formula for RRR

A

1 - RR

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

ARR formula

A

CER - EER

OR

EER - CER

[since this is the absolute value]

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

NNT formula for the duration of the study

A

1/ARR

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

NNT for 1 year

A

NNT x (# of years the study was conducted)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

odds ratio (OR) formula

A

[EER/ENR] / [CER/CNR]

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

discrete data

A

generally refers to discrete, countable numbers with no decimals (cannot be divided)

ex: people

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

continuous data

A

in contract, is more of a continuum in which use of decimals allows for an infinite number of possible values

e: electrolyte levels

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

discrete data subtypes

A

nominal (categorical) data

ordinal (ranked) data

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

nominal (categorical) data

A

refers to assigning data to different CATEGORIES based on the occurrence of an outcome

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

ordinal (ranked) data

A

refers to data that come in a certain order or ranking but the intervals between the values are not necessarily equal

ex: NYHA classification

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

continuous data subtypes

A

interval data

ratio data

*can be expressed using decimals

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

interval data

A

refers to measurable data with equal intervals between values. there is no absolute zero

ex: temperature (a “0” temperature has meaning)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

ratio data

A

type of data interval that has an absolute zero

ex: height, weight, hemoglobin level

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

continuous data tests

A

t - test

student’s t test

paired t test

analysis of variance (ANOVA) test

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

t-test

A

continuous data

means of two groups

ex: cholesterol levels OR HgbA1c

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

student’s t-test

A

continuous data

two groups are independent and separate

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

paired t-test

A

continuous data

test group acts as its own control (“PAIRED”)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

ANOVA test

A

continuous data

groups of >/= 3 groups are being compared (similar to t-test but there are >/= 3 groups)

CAN BE USED FOR BOTH INDEPENDENT AND PAIRED GROUPS

20
Q

nominal (categorical) data tests

DISCRETE DATA

A

chi-square test

McNemar’s test

Cochran’s Q test

21
Q

chi square test

A

nominal // discrete

two or more independent groups

ex: the risk of GI bleed is compared in ASA group vs placebo group

22
Q

McNemar’s test

A

nominal // discrete

two paired groups

23
Q

Cochran’s Q test

A

nominal // discrete

> /= 3 paired groups

23
Q

ordinal (ranked) data tests

DISCRETE DATA

A

wilcoxon rank sum test

wilcoxon signed rank test

kruskal-wallis test

friedman test

24
wilcoxon rank sum test
ordinal // discrete two independent groups
25
wilcoxon signed rank test
ordinal // discrete two paired groups
26
kruskal-wallis test
ordinal // discrete >/= 3 independent groups
27
friedman test
ordinal // discrete >/= 3 paired groups
28
randomized clinical trial
provides high quality statistical test members of two groups are chosen at random considered the gold standard
29
cohort studies
group (cohort) of people who share a common trait observed over time prospective study outcome of interest is counted or measured
30
case-control studies
want to see if drug/effect is back --> looking backwards --> looking at previous cases choose this when it is unethical to expose patients to a risk factor CONTROL is different here. means individuals who have never had the outcome or event of interest regardless of their exposure status GOING BACK IN TIME
31
cross-sectional studies
factors and status of group are studied at one specific time (one cross section of time) looking at PREVALENCE
32
cross-over studies
study participants serve as their own control treatment group will eventually become a control control group will get the treatment the two groups are "paired" or "matched"
33
ITT (intent to treat)
ALL pt data analyzed (all data that was INTENDED to be studied) pro = closer to real life
34
PP (per protocol)
counts the data only from the pts who COMPLETED the study pro = more accurate estimate of the drug
35
meta analysis
results of many relevant studies are reviewed objectively, quantitatively want RCTs but need to avoid PUBLICATION BIAS meaning including results from not fancy studies // less exciting studies // studies not published
36
type I error
false rejection of the null hypothesis there is no association but researchers THOUGHT there was an association association was imagined saw something that doesn't exist alpha = type 1 --> want <0.05 (5%) means chance of type 1 error is less than 5/100
37
type II error
false acceptance of the null hypothesis there is an association BUT IT WAS MISSED AGREE WITH NULL BUT THIS IS INCORRECT~! type II error = beta = 0.20 power = 1 - beta [power increases with increase in sample size AND/OR when the difference of interest between the two groups is more noticeable and/or when the sample size increases] dont want chance of type II error to be more than 0.20 80% power is the typical MINIMUM power in order for a study to be considered independent power = likelihood of NOT making a type II error
38
summary of statistical significance RR RRR OR
a 95% CI range for ___ that does NOT cross ___ means the study IS statistically significant: RR; 1 RRR; 0 OR; 1
39
incidence
the number of NEW individuals that develop an illness in a given time period (usually 1 year) divided by the total # of individuals at risk during that time
40
prevalence
the number of individuals in a population who HAVE an illness divided by the total population
41
relationship between incidence and prevalence
prevalence is equal to incidence multiplied by the length of the disease process if the disease is long term, prevalence is higher than incidence. e.g., HTN if the disease is short term, incidence is higher than prevalence e.g., meningitis
42
reliability
means reproducibility of the results if the test is repeated
43
validity
whether a test is assessing what it is supposed to be assessing. two components of validity are sensitivity and specificity
44
sensitivity
measures how well a test identifies truly ill people with a highly sensitive test, a negative result is used to rule OUT the disease
45
specificity
measures how well a test identifies or rules out truly well people (without the disease) a positive result in a highly specific test is used to CONFIRM the disease