Flashcards in Introduction to BIostatistics in Epi Deck (34):

1

## 3 key attributes of data variables

###
Magnitude(biger is more, lower is less)

Consistency of scale(or fixed intervals)

-equal, measurable spacing btw units

rational zero

Each can be answered with yes or no repsonse

2

## Nominal

### Dichotomouse/binary; no order or rank; non ranked categories.... NO magnitude, NO consistency of scale, NO rationonal zero..... examples.... gender, occupationsoal class, party affiliation (ALL dichotomous and non-ranked categories)

3

## Ordinal

### Ranked Categories; non-equal-distance... YES magnitude, NO consitency of scale, NO rational zero... Rank of canidates in order of preference from worse to best...( all ranked categories)

4

## Interval/ Ratio

### YES magnitude, YES consitency of scale, NO or YES rational zero (no-interval; yes-ratio) ex... number of living sibilings and age... (all numerical scales with ture units)

5

## What type of data is being collected or evaluated?

### ...

6

## What type of comparison/assessment is desired?

### Correlation!!!! Correlations test.

7

## Correlation

### Provides a quantitative measure of the strength and direction of a relationship btw variables.... creates a 45 degree angle.... X goes down, Y goes up....

8

## Correlation..... Is the correlation linear? if not significanlty significan then there is NOT linear correlation

###
ordinal= spearman correlation

nominal= contingency coefficent

interval= pearson correlation

9

## Contingency coefficent

### if more than .05 it is not significantly significant

10

## Time-to event... event-occurrence

### survival test... can all be represented by kaplan meier curve

11

## Suvival Test

###
ordinal=cox proportional hazards

nominal= log-rank

interval= kaplan-meier product-limit estimate

12

## survial tests....

### compares the proprtion of, or time to event occurence btw groups....

13

## Outcome prediction/association

### Regression

14

## Regression (THE WORD PREDICT!!!0

### Provide a measure of the relationship btw variables by allowing the prediction about the dependent, or outcome, variable (DV) knowing the value/rank of others independent variables..... able to calculate measerue of associateions

15

## Regression tests

###
Nominal=logistic regression

ordnial= multinominal logistic regression

interval= linear regression

16

## Frequencies/ counts/ proportions

### HOW MAN GROUPS!

17

## is the datat independent or related

### Data from the same( paired) ore different groups(independent)?

18

## Nominal 2 groups independent data

### (Pearsons) Chi-square test

19

## nominal greater than 3 independent data

### Chi-square test of independence... no cell with expected count of <5.... to predeict 3 more comparisons, one must perform subsequent analysis(post-hoc testing) to determine which groups are different.... Multiple Chi tests NEVER acceptable.... Bonferroni test of Ineqaulity, ver conservative

20

## nominal >2 groups with expected cell count of <5

### fisher's exact test

21

## Nominal 2 groups of paired/related data

### McNemar test.... key words... pre vs post... before vs after, basline vs end

22

## nominal >3 groups of paired related data

### cochran ..... must do bonferroni test of inequality

23

## Ordinal 2 groups of independent data

### Mann-whitney test

24

## Ordinal data >3 groups of independent data

### Kruskal wallis test.... compares the mdeian values btw groups.... mujst perform post hoc

25

## If related data

### before and after... beginning and end... baseline vs end

26

## Ordnial data 2 groups of Paired data

### Wilcoxon SIgned Rank test

27

## Ordinal >3 groups of paired data

### Friedman test... do post hoc after

28

## Post hoc tests for 3 or more group comparisons

###
Student-Newman Keul test - comapres all pairwise comparisons possilbe

Dunnett Test- compares agains single.. equal in size

Dunn Test- Compares all possilbe comparisons

29

## Interval 2 grousp of independent data

### student t test... compares mean values btw groups

30

## interval data >3 groups of independent data

###
ANaysis of Varainace (ANOVA) (1DV)- comapres menas of all groups

Multiple Analysis of Variance (Manova) (>2 DVs) must do post hoc

31

## Interval data >3 groups of independent data w cofounders

###
ANCOVA

MANCOVA

32

## Interval 2 groups of data paired

### Paired t test

33

## interval data >3 groups of paired data

###
repeated ANOVA

Repeated MANOVA(>2)

34