Statistics

Question 1

Cohort study

Answer 1

Population followed over time- longitudinal study
- provides estimates of risk associated with a suspected causative factor
- rare exposure vs people without exposure and see in future what outcomes emerge
EX: Group split into people who have risk factor and who do not for certain disease and then followed up later to see who gets that disease and who doesn’t
- can calculate odds ratio and risk ratio

Question 2

Case-control

Answer 2

• Retrospective study
• Have disease (rare) and don’t have disease then look back in time and try to identify exposure that may have led to condition
• can only calculate odds ratio

Question 3

Relative risk

Answer 3

probability of an event occurring to all possible events (risk of developing lung cancer in people exposed or not exposed to smoke)

1= no difference between the two groups, no increased risk no association
>1= positive association, increased risk
<1= negative association, decreased risk
further from 1 the stronger the association
—If P value is not less than 0.05 or if confidence interval includes 0 the RR is not significant

RR= probability that an exposed person gets disease/probability that an unexposed person gets disease

Question 4

Odds ratio

Answer 4

OR=Odds that a case was exposed/odds that a control was exposed
OR=1, exposure not associated with disease
OR >1, exposure is positive associated with disease
OR<1, expose is negatively associated with disease
further OR from 1 the stronger the association

Question 5

Odds ratio

Question 6

When is odds ratio similar to relative risk?

Answer 6

When the disease does not occur frequently among the exposed (disease is rare)

Question 7

When does odds ratio overestimate risk?

Answer 7

When the outcome is more common such as in hyperlipidemia

Question 8

Type 1 error

Answer 8

Occurs when the null hypothesis is rejected when it should have been maintained. Which means difference only due to chance

Question 9

Power

Answer 9

the probability of finding the difference between two samples
- probability of rejecting the null hypothesis when it should be rejected

Question 10

A probability of 1 means it will occur, a probability of 0 means what?

Answer 10

it will not

Question 11

Regression analysis

Answer 11

method of predicting the value of one variable in relation to anther variable based on observed data

Question 12

Incidence

Answer 12

the number of new cases/total number of people at risk

Question 13

central tendency

Answer 13

central value in a distribution around which other values are arranged (mean, median, mode)

Question 14

ANOVA

Answer 14

• set of statistical procedures that compares two groups and determines if the differences are due to experimental influence or chance

Question 15

Regression analysis

Answer 15

using data to predict how the value of one variable in relation to another

Question 16

null hypothesis

Answer 16

assumption that there are no differences between two samples of polulation

Question 17

correlation coefficient

Answer 17

measurement of the direction and strength of the relationship between two variables
- -1 to +1
- closer to +1 or -1 stronger relationship
- shows nothing about cause and effect

Question 18

attributable risk

Answer 18

absolute incidence of the illness in patients exposed to the condition that can be attributed to the expose

Question 19

predictive validity

Answer 19

diagnosis allows the doctor to predict clinical course and treatment response

Question 20

construct validity

Answer 20

the diagnosis is based on underlying pathophysiology and use of biomarkers to confirm disease

Question 21

analysis of variance

Answer 21

set of statistical procedures that compares two groups and determines if the differences are due to experimental influence or chance

Question 22

x^2

Answer 22

• binary predictor variable and one binary outcome variable

Question 23

binary variable

Answer 23

two possible values, yes or no

Question 24

continuous varialbe

Answer 24

will fall on range- height or weight

Question 25

independent variables

Answer 25

manipulated by the experimenter

Question 26

dependent variable

Answer 26

variables not manipulated by experimenter

Question 27

T test

Answer 27

one binary predictor variable and one continuous outcome variable

Question 28

ANOVA

Answer 28

• two or more binary predictor variable and one continuous outcome variable

Question 29

correlation

Answer 29

one continous predictor variable and one continous outcome variable

Question 30

validity

Answer 30

the degree to which an instrument measures what it is intended to measure

Question 31

face validity

Answer 31

diagnosis based on a general consensus among experts

Question 32

descriptive validity

Answer 32

based on characteristic features that distinguish it from other disorders

Question 33

predictive validity

Answer 33

a diagnosis will allow clinicians to accurately predict treatment response and clinical course

Question 34

construct validity

Answer 34

diagnosis is based on an understanding of the underlying pathophysiology

Question 35

positive predictive power

Answer 35

ability of a positive test to predict a positive disease
true positives/(true positive+false positives)

Question 36

kappa

Answer 36

number used for binary data and tells whether a given procedure or test produces reliable or reproducible results

Question 37

correlation coefficient

Answer 37

reliability for nonbinary data such as continuous measurement

Question 38

period prevalence

Answer 38

looks at the number of cases both existing and new during a specific time period

Question 39

lifetime prevalence

Answer 39

proportion of people who have ever had a specific condition during their lifetime