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Epidemiology > Quiz 2 > Flashcards

Quiz 2 Flashcards

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
Q

variance

A

deviation of scores

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26
Q

standard deviation

A

square root of variance

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27
Q

what are the two aspects of statistical inference?

A
  1. hypothesis testing

2. estimation (point estimate vs. interval estimate)

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28
Q

point estimate

A

providing the best estimate of the true population value for a statistic

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29
Q

interval estimate

A

quantifying our uncertainty about how close our point estimate is to the true pop. value

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30
Q

null hypothesis

A

H0: position that you wish to test

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31
Q

alternative hypothesis

A

H1: probably alternative proposition if H0 is rejected

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32
Q

type I error

A

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)

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33
Q

type II error

A

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)

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34
Q

when would type II error occur?

A

H0 is false but you accept H0

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35
Q

when would type I error occur?

A

H0 is true but you reject H0

36
Q

probability

A

likelihood that any one event will occur, given all possible outcomes (certain=1, impossible=0)

37
Q

P value

A

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
Q

discuss what happens if alpha level is larger (p

A

greater to find significant, but greater chance for type I error (10%)

39
Q

discuss what happens if alpha level is smaller (p

A

less chance of type I error (1%), but harder to find significance

40
Q

power

A

probability that a test will detect a difference when once actually exists; probability that a test will lead to rejection of null

41
Q

what is a “good” power value?

A

power>=0.80 is good

42
Q

what three factors can increase power?

A
  1. maximizing difference between groups
  2. increasing sample size
  3. reducing variablity
43
Q

standard error

A

hypothetical quantity that indicates degree of variability among sample means

44
Q

CI

A

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
Q

how do you calculate the 95% CI?

A

point estimate +- 1.96 (standard error)

46
Q

how do you calculate the standard error?

A

STDEV/(square root of N)

47
Q

case-control study

A

observational, retrospective; patients w/ outcome of interest and control patients w/o outcome of interest, comparing to determine past exposures

48
Q

case-series study

A

observational; report on a series of patients w/ an outcome of interest

49
Q

cohort study

A

observational, prospective; involves ID of two groups, one w/ exposure, one w/o, following them to see if they develop outcomes

50
Q

meta-analysis

A

systematic review that uses quantitative methods to summarize results

51
Q

RCT

A

randomized into an experimental group and control group

52
Q

systematic review

A

article in which authors systematically searched for, appraised, and summarized all lit. for a specific subject

53
Q

best study design for therapies

A

systematic review/ RCT

54
Q

best study design for diagnosis

A

blinded comparison w/ gold standard diagnostic test or procedure

55
Q

best study design for prognosis/etiology

A

cohort/case-control

56
Q

concealed allocation

A

investigators unaware of subject’s group in RCT

57
Q

stratified

A

participants divided into homogenous groups based on possible confounding variables

58
Q

computerized

A

algorithm to randomize groups in RCT

59
Q

crossover design w/ washout period

A

one group, gets two treatments w/ washout period in between

60
Q

cross-sectional

A

snapshot in time, prevalence survey, community survey, large pop., can be used to compare communities w/ different risk factors

61
Q

phase I

A

first in human, investigation of new drug in healthy humans, testing side effects

62
Q

phase II

A

small sample, tests effectiveness of drug for indication, in affected pts.

63
Q

phase III

A

larger population than phase II, gives additional info about effectiveness and safety

64
Q

phase IIIb

A

testing in special population (example: geriatrics)

65
Q

phase IV

A

post marketing phase, not all drugs require IV, only if deemed appropriate by FDA.

66
Q

internal validity

A

integrity in the experimental design, performance of the experiment, effects are true for the study participants

67
Q

external validity

A

appropriateness and extent by which results can be applied to non-study pop.

68
Q

possible threats to internal validity

A

was the right instrument used? was it used correctly each time?

69
Q

possible threats to external validity

A

was the study relevant to the research question? is the pop. similar to the one you will be treating?

70
Q

bias

A

inaccuracy of measurements, this is a threat to internal validity

71
Q

what are 6 types of bias?

A
  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
Q

what is a cutoff for withdrawal bias?

A

dropout rate should not exceed 20%

73
Q

precision

A

consistant readings

74
Q

define accuracy

A

precise and unbiased (all arrows hitting the middle of the target consistently)

75
Q

test-retest

A

consistent repeated measures

76
Q

intra-rater

A

consistent single rater

77
Q

inter-rater

A

consistent between different raters

78
Q

intra-subject

A

consistency of a single subject

79
Q

when should you use Mann Whitney?

A

non-parametric, independent, 2 groups

80
Q

when should you use Kruskai Wallis ANOVA by Ranks?

A

non-parametric, independent, >2 groups

81
Q

when should you use Sign Test or Wilcoxson Sign Ranks?

A

non-parametric, dependent, 2 groups

82
Q

when should you use Friedman ANOVA by ranks?

A

non-parametric, dependent, >2 groups

83
Q

when should you use t-test?

A

parametric, independent, 2 groups

84
Q

when should you use ANOVA?

A

parametric, independent, >2 groups

85
Q

when should you use paired t-test?

A

parametric, dependent, 2 groups

86
Q

when should you use repeated measures ANOVA?

A

parametric, dependent, >2 groups

87
Q

which conventional P value determines when you should reject H0?

A

reject H0 if P value

Epidemiology (2 decks)

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