STATs to learn Flashcards

1
Q

statistics enables

A

informed decisions

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2
Q

first step of any medical stats

A

should be to get a clear understanding of the background info of the study and clarify objectives

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3
Q

second step of medical stats

A

formulate problem in statistical terms

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4
Q

how is diagnostic uncertainty qualified

A

using conditional probabilities

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5
Q

example of condition probabilities if the risk of having breast cancer is 1/10

A
  1. 9 probability that the lump is not cancer

0. 1 probability that the lump is cancer

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6
Q

RCTs require

A

active interventions by investigators

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7
Q

in observational studies, researchers play

A

a much more passive role

- treatment or exposure is not under control of the researcher due to ethical concerns of logistic complaints

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8
Q

observational studies are

A

cheaper and less time consuming then investigative studies

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9
Q

observational studies allows groups

A

which would often be excluded from clinical trials to be used

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10
Q

observational studies give a better estimate of

A

what actually happens in routine practice

i.e. patients in trials may be more compliant with treatment

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11
Q

a variable

A

is a set of characteristics which can be used to describe an aspect of a participant in a research study

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12
Q

the top of a bell curve shows

A

average- mean, median, mode

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13
Q

the width of a bell curve shows

A

variation

  • sd
  • iqr
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14
Q

histograms

A

shape of distribution , multiple modes, skewness and tail size
-outliers

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15
Q

box and whisker

A

compares location and variation in several groups e.g. outliers

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16
Q

scatter plot

A

dispels general form of relationships between 2 variables

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17
Q

bar chart

A

frequencies of categorical variable an cross tabulations of categorical variables

18
Q

correlation

A

strength of association between two variables- quantified using pearson correlation coeffieicint

-extent to which on variable relies on another

19
Q

regression

A

used to describe the relationship between a quantitative outcome and one or more predictor variables

20
Q

regressions an be usedto

A

estimate mean scores of the outcome for subjects with specific profile of score on predictor

21
Q

a=

A

intercept

  • mean value of y, when the predictor is 0
  • point on y axis which is crossed by the regression line
22
Q

b=

A

the slope

-the predicted increase in outcome for each one unit increase in the predictor

23
Q

what else can be factored into the regression equation

A

e = error/ residuals

24
Q

errors/ residuals

A

are presumed to be equally distributed

- vertical difference between outcome value and the value predicted from the regression line

25
in small samples it is important that
residuals are normal
26
normal residuals guarantees
validity of CIs and p value
27
normality is checked by
plotting histograms | -ideally would be bell shaped
28
checking for constant variance
the amount of variation of residuals are the regression line should be constant and not depend on values of the predictor variance - checked by scatter plot
29
normality checked via
scatterplot
30
goodness of fit
most subjects won't fall on the regression line "the extent to which predicted outcome scores are close to observed scores"
31
R2
the proportion of variation in one outcome that explained by predictor
32
coefficient of determination
R2
33
R2=
r x r
34
R2= 0
no variability explain
35
R2=1
100% of variability explained
36
adjusted R2=
an unbiased estimate of the fraction of variance explained, taking into account the SAMPLE SIZE AND NO. OF VARIABLES
37
confounding
where a third variable is associated with a response
38
consequences of confounding
- bias - type 1 - type2
39
bias and confounding
will be relationship stronger or weaker
40
regression assumptions
- linearity - normality - constant variance (homoscedasticity)