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Epidemiology > Quiz 2 > Flashcards

Quiz 2 Flashcards

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1
Q

mode

A

most commonly occurring value in a set of data

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2
Q

median

A

number that is halfway into a set of data when values arranged from least to greatest

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3
Q

mean

A

average

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4
Q

what are the three measures of central tendency?

A
  1. mode
  2. median
  3. mean
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5
Q

what are the three measures of variation?

A
  1. frequency
  2. IQR (interquartile range)
  3. SD (standard deviation)
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6
Q

IQR

A

interquartile range, middle 50 percent of a distribution

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7
Q

what are the three different types of variables?

A
  1. independent
  2. dependent
  3. confounding
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8
Q

what are the two types of scales and their subtypes?

A
  1. categorical (nominal and ordinal)

2. continuous (interval, ratio)

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9
Q

independent variables

A

variable which is manipulated by researcher

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10
Q

dependent variables

A

variable of interested, measured or observed by the researcher (O from PICO question)

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11
Q

confounding variables

A

unobserved, unmeasured variable that unknowingly influences the dependent variable

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12
Q

nominal data

A

classification or label, w/ no implied order (e.g male and female)

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13
Q

ordinal data

A

categorical, rank order of observations (e.g. bad, medium, good)

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14
Q

interval data

A

units w/ equal interval, can be negative, not representing an absolute quantity (e.g. temperature)

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15
Q

ratio data

A

units w/ equal interval, measured from true zero (e.g. height, weight)

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16
Q

categorical data

A

nominal and ordinal scales, nonparametric, use a special set of stat tests

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17
Q

continuous data

A

interval and ratio, parametric (uses math), standard stat methods

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18
Q

positively skewed distribution

A

tail points in positive direction

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19
Q

negative skewed distribution

A

tail points in negative direction

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20
Q

when should frequency distributions be used?

A

small data sets, these represent the frequency of certain responses in a data set

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21
Q

grouped frequency distribution

A

frequency distribution by category (example was PAs by gender and age)

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22
Q

central limit theorem

A

assumes normal distribution if samples is of normal population and there is a large sample size

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23
Q

regression to mean

A

expected change from natural variation (example was BP in clinic)

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24
Q

range

A

difference between the highest and lowest scores

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25
variance
deviation of scores
26
standard deviation
square root of variance
27
what are the two aspects of statistical inference?
1. hypothesis testing | 2. estimation (point estimate vs. interval estimate)
28
point estimate
providing the best estimate of the true population value for a statistic
29
interval estimate
quantifying our uncertainty about how close our point estimate is to the true pop. value
30
null hypothesis
H0: position that you wish to test
31
alternative hypothesis
H1: probably alternative proposition if H0 is rejected
32
type I error
determining statistically that a difference between two samples exist when in reality a population difference DOES NOT exist (H0 is true but we say it is false)
33
type II error
determining statistically that no difference exists between two samples when in reality a pop. difference DOES exist (H1 is true but we say H0 is true)
34
when would type II error occur?
H0 is false but you accept H0
35
when would type I error occur?
H0 is true but you reject H0
36
probability
likelihood that any one event will occur, given all possible outcomes (certain=1, impossible=0)
37
P value
probability of a value as large or larger than the observed, given that H0 is true, higher P value means a higher chance of type I error
38
discuss what happens if alpha level is larger (p
greater to find significant, but greater chance for type I error (10%)
39
discuss what happens if alpha level is smaller (p
less chance of type I error (1%), but harder to find significance
40
power
probability that a test will detect a difference when once actually exists; probability that a test will lead to rejection of null
41
what is a "good" power value?
power>=0.80 is good
42
what three factors can increase power?
1. maximizing difference between groups 2. increasing sample size 3. reducing variablity
43
standard error
hypothetical quantity that indicates degree of variability among sample means
44
CI
confidence interval; estimate of the potential range of values which are likely to include true value in real pop.; way of determining the precision in our estimate
45
how do you calculate the 95% CI?
point estimate +- 1.96 (standard error)
46
how do you calculate the standard error?
STDEV/(square root of N)
47
case-control study
observational, retrospective; patients w/ outcome of interest and control patients w/o outcome of interest, comparing to determine past exposures
48
case-series study
observational; report on a series of patients w/ an outcome of interest
49
cohort study
observational, prospective; involves ID of two groups, one w/ exposure, one w/o, following them to see if they develop outcomes
50
meta-analysis
systematic review that uses quantitative methods to summarize results
51
RCT
randomized into an experimental group and control group
52
systematic review
article in which authors systematically searched for, appraised, and summarized all lit. for a specific subject
53
best study design for therapies
systematic review/ RCT
54
best study design for diagnosis
blinded comparison w/ gold standard diagnostic test or procedure
55
best study design for prognosis/etiology
cohort/case-control
56
concealed allocation
investigators unaware of subject's group in RCT
57
stratified
participants divided into homogenous groups based on possible confounding variables
58
computerized
algorithm to randomize groups in RCT
59
crossover design w/ washout period
one group, gets two treatments w/ washout period in between
60
cross-sectional
snapshot in time, prevalence survey, community survey, large pop., can be used to compare communities w/ different risk factors
61
phase I
first in human, investigation of new drug in healthy humans, testing side effects
62
phase II
small sample, tests effectiveness of drug for indication, in affected pts.
63
phase III
larger population than phase II, gives additional info about effectiveness and safety
64
phase IIIb
testing in special population (example: geriatrics)
65
phase IV
post marketing phase, not all drugs require IV, only if deemed appropriate by FDA.
66
internal validity
integrity in the experimental design, performance of the experiment, effects are true for the study participants
67
external validity
appropriateness and extent by which results can be applied to non-study pop.
68
possible threats to internal validity
was the right instrument used? was it used correctly each time?
69
possible threats to external validity
was the study relevant to the research question? is the pop. similar to the one you will be treating?
70
bias
inaccuracy of measurements, this is a threat to internal validity
71
what are 6 types of bias?
1. selection bias (picking sicker patients) 2. observer bias (person taking measurements knows the tx group) 3. participant bias (not following instructions) 4. withdrawal bias (dropout rates should not exceed 20%) 5. recall bias (example: study respondents not remembering things that were inconsequential) 6. instrument bias
72
what is a cutoff for withdrawal bias?
dropout rate should not exceed 20%
73
precision
consistant readings
74
define accuracy
precise and unbiased (all arrows hitting the middle of the target consistently)
75
test-retest
consistent repeated measures
76
intra-rater
consistent single rater
77
inter-rater
consistent between different raters
78
intra-subject
consistency of a single subject
79
when should you use Mann Whitney?
non-parametric, independent, 2 groups
80
when should you use Kruskai Wallis ANOVA by Ranks?
non-parametric, independent, >2 groups
81
when should you use Sign Test or Wilcoxson Sign Ranks?
non-parametric, dependent, 2 groups
82
when should you use Friedman ANOVA by ranks?
non-parametric, dependent, >2 groups
83
when should you use t-test?
parametric, independent, 2 groups
84
when should you use ANOVA?
parametric, independent, >2 groups
85
when should you use paired t-test?
parametric, dependent, 2 groups
86
when should you use repeated measures ANOVA?
parametric, dependent, >2 groups
87
which conventional P value determines when you should reject H0?
reject H0 if P value

Epidemiology (2 decks)

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