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AU13 - Evaluative Science > Exam 1 > Flashcards

Exam 1 Flashcards

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1
Q

Efficacy

A

How beneficial a specific intervention, procedure, regimen, or service is under ideal conditions (lab)

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2
Q

Effectiveness

A

How beneficial a specific intervention, procedure, regimen, or service is when deployed in the field in routine circumstances (real world)

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3
Q

Heirarchy of evidence quality

A
  1. Systematic reviews and meta-analysis
  2. Clinical trial in humans (all criteria met)
  3. Longitudinal cohort studies
  4. Case-control studies
  5. Human trial without concurrent controls
  6. Descriptive and cross-sectional studies
  7. Case reports & case series
  8. Personal opinion, subjective impressions, anecdotal accounts
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4
Q

Criteria for clinical trial in humans

A
  • Sufficient & appropriate subjects
  • Subjects randomly allocated
  • Use placebo in double-blind
  • Tested agent is closely assessed
  • Reliability of measurements (calibrate machines)
  • Sufficient duration
  • Minimal loss to follow-up (Has to be random loss)
  • Specific endpoints have to be clearly defined in advance
  • Statistical analysis is appropriate
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5
Q

Prospective study

A

Before disease occurs in subjects

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6
Q

Retrospective study

A

After disease has occurred in subjects

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7
Q

Cohort study

A

Separate subjects based on exposure, then find the prevalence of disease in each group

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8
Q

Case-Control study

A

Separate subjects based on whether they have a disease or not, then find exposures of each group

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9
Q

Cross-sectional survey

A

Disease and exposure assessed at the same time in a very large population (Snapshot)
Not always possible to distinguish whether exposure preceded or followed disease

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10
Q

Case report

A

Describes experience of a single patient or groups of patients with similar diagnosis

May lead to formulation of a hypothesis

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11
Q

Case series

A

A collection of individual case reports

Investigating activities of infected individual leads to hypothesis which is tested by comparing those with and w/o disease in a later case-control study

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12
Q

Nominal variables

A

A scale based on categories

Ex: gender, political party, marital status

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13
Q

Ordinal variables

A

A scale based on classification of an observation according to its relationship to other observations
Ex: Poor-fair-good rating scale

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14
Q

Crossover study

A

A longitudinal study in which subjects receive a sequence of different treatments (or exposures).

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15
Q

Interval variables

A

A scale based on equal units of measurement; distance between any 2 numbers is of known size
Zero point is arbitrary
Ex: Fahrenheit and centigrade temperature scales

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16
Q

Ratio variables

A

A scale based on equal units of measurement and a true zero point at its origin
Ex: Mass, time

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17
Q

Population vs Sample

A 
Population = Whole group of people (mean=µ, SD-σ)
Sample = Part of the population (mean=x̅, SD=S or SD)
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18
Q

Mode

A

Most frequent measurement

Most useful with nominal scale, but may be used with any

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19
Q

Median

A

Measurement right in the middle when put in order

Most useful with ordinal, but may be used with higher order scales
Insensitive to extreme values

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20
Q

Mean

A

Arithmetical average: sum of measurements divided by total # of measurements

Most useful with interval or ratio measurement scales

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21
Q

Range

A

Difference between largest and smallest measurements

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22
Q

Interquartile range

A

X25th percentile - X75th percentile

23
Q

Variance (s^2, MS)

A

Unbiased estimate of population variance

Sum of squares/total # in sample
Subtract 1 (degrees of freedom)
24
Q

Sum of squares

A

Subtracting sample mean from each of the measurements, squaring the result to eliminate negative #s, then add all of these

25
Q

Degrees of freedom (df)

A 
N - 1 (N = # of variables)
# of independent observations (# of observations that are "free to vary")
26
Q

Standard deviation (SD)

A

Positive square root of variance

27
Q

Coefficient of variation (CV)

A

100 x SD/Mean

The comparison of the size of the standard deviation to that of the mean

28
Q

Standard error of the mean (SE or SEM)

A

Measurement of how good we think mean is

SD/Square root of sample size (N)

Bigger sample size = lower standard error

29
Q

Difference between SD and SE

A

SD is used to measure variability of individual subjects/entities around a sample mean (variability of individual measurements from the mean)

SE is used to assess how accurately a sample mean represents a population mean (variability of the mean if you repeated the experiment many times)

30
Q

Bar charts

A 
Categorical data (Nominal, ordinal)
Labels under each bar
31
Q

Histograms

A

Bar charts for continuous data

Axis labels not necessarily centered under bars

32
Q

Box-whiskers plots

A

Box represents middle 50% of data
Line represents median
Whiskers extend to remaining data (circles/asterisks represent outliers)

33
Q

Dot plots

A

Continuous data in groups

Shows relative location & spread of each group

34
Q

Funnel effect

A

Only see half the data in journals because funders will not publish negative findings that go against their products

35
Q

Impact factor

A

Total # citations to articles in journal / Total # of articles published

36
Q

Types of papers published in journals

A

(1) Research reports
(2) Reviews of the literature to summarize knowledge in a particular area
(3) Commentaries

37
Q

Difference between statistical and clinical significance

A

1% change in plaque index is statistically significant but not clinically significant at all

38
Q

Prevalence of a disease

A

How many people had it at a specific point in time

39
Q

Booleans

A

AND - searches for another word in addition (narrows)
NOT - doesn’t include a certain word (narrows)
OR - search 2 words separately (broadens)
“” - search exact phrase (narrows)

40
Q

PICO

A

P- Patient problem
I- Intervention, prognostic factor or exposure
C- Comparison
O- Outcome

41
Q

Types of hypotheses

A

Research - What we are trying to prove; prediction
Null - a mathematical statement of no difference
Alternate - covers all that the null doesn’t

42
Q

Directional (one-tailed) hypothesis

A

Trying to prove one scenario (guys smarter than girls)

43
Q

Non-directional (two-tailed) hypothesis

A

Allowing either outcome to be possible (guys or girls could be smarter) This is what we will use in class

44
Q

Dependent variable

A

• The variable we measure and compare
(intelligence)
• Sometimes called the outcome variable or
response variable

45
Q

Independent variable

A

• The variable we manipulate or the “grouping
variable” (gender)
• Sometimes called a predictor variable

46
Q

Type I Error (α or p-value)

A

Probability of rejecting null hypothesis when it is true

47
Q

Type II Error (ß)

A

Probability of accepting null hypothesis when it is false

48
Q

Methods to increase power

A
  • Increase type I error willing to tolerate (ß + power = 1)
  • Increase sample size
  • Increase deviation from null hypothesis willing to tolerate
  • Decrease variability
  • Use a directional alternate hypothesis if appropriate
  • Use most efficient/powerful statistical test
49
Q

Reject H0 if and only if

A

p ≤ α

50
Q

Central limit theorem

A

In random samples of N observations drawn from a population, the sample means will be approximately normally distributed

51
Q

Z score

A

z = x̅i - µ
—————-
σ / SqRt(N)

52
Q

Z score distances

A 
68% = within 1 SD of mean
95% = within 2 SD of mean
99% = within 3 SD of mean
53
Q

t-distribution

A

Used when sample size is small (<30)
Calculation is exact same as z score
Has smaller mound & thicker tails

54
Q

Confidence interval for a mean

A

Consists of lower confidence bound & upper confidence bound with the population mean contained in the interval (1-α)% of the time

AU13 - Evaluative Science (1 decks)

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