STATs to learn

Question 1

statistics enables

Answer 1

informed decisions

Question 2

first step of any medical stats

Answer 2

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

Question 3

second step of medical stats

Answer 3

formulate problem in statistical terms

Question 4

how is diagnostic uncertainty qualified

Answer 4

using conditional probabilities

Question 5

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

Answer 5

0. 9 probability that the lump is not cancer

0. 1 probability that the lump is cancer

Question 6

RCTs require

Answer 6

active interventions by investigators

Question 7

in observational studies, researchers play

Answer 7

a much more passive role

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

Question 8

observational studies are

Answer 8

cheaper and less time consuming then investigative studies

Question 9

observational studies allows groups

Answer 9

which would often be excluded from clinical trials to be used

Question 10

observational studies give a better estimate of

Answer 10

what actually happens in routine practice

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

Question 11

a variable

Answer 11

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

Question 12

the top of a bell curve shows

Answer 12

average- mean, median, mode

Question 13

the width of a bell curve shows

Answer 13

variation

• sd
• iqr

Question 14

histograms

Answer 14

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

Question 15

box and whisker

Answer 15

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

Question 16

scatter plot

Answer 16

dispels general form of relationships between 2 variables

Question 17

bar chart

Answer 17

frequencies of categorical variable an cross tabulations of categorical variables

Question 18

correlation

Answer 18

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

-extent to which on variable relies on another

Question 19

regression

Answer 19

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

Question 20

regressions an be usedto

Answer 20

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

Question 21

a=

Answer 21

intercept

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

Question 22

b=

Answer 22

the slope

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

Question 23

what else can be factored into the regression equation

Answer 23

e = error/ residuals

Question 24

errors/ residuals

Answer 24

are presumed to be equally distributed

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

Question 25

in small samples it is important that

Answer 25

residuals are normal

Question 26

normal residuals guarantees

Answer 26

validity of CIs and p value

Question 27

normality is checked by

Answer 27

plotting histograms

-ideally would be bell shaped

Question 28

checking for constant variance

Answer 28

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

Question 29

normality checked via

Answer 29

scatterplot

Question 30

goodness of fit

Answer 30

most subjects won’t fall on the regression line

“the extent to which predicted outcome scores are close to observed scores”

Question 31

R2

Answer 31

the proportion of variation in one outcome that explained by predictor

Question 32

coefficient of determination

Answer 32

R2

Question 33

R2=

Answer 33

r x r

Question 34

R2= 0

Answer 34

no variability explain

Question 35

R2=1

Answer 35

100% of variability explained

Question 36

adjusted R2=

Answer 36

an unbiased estimate of the fraction of variance explained, taking into account the SAMPLE SIZE AND NO. OF VARIABLES

Question 37

confounding

Answer 37

where a third variable is associated with a response

Question 38

consequences of confounding

Answer 38

• bias
• type 1
• type2

Question 39

bias and confounding

Answer 39

will be relationship stronger or weaker

Question 40

regression assumptions

Answer 40

• linearity
• normality
• constant variance (homoscedasticity)