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Eval Science Midterm 2 > Lecture 12: Biostatics > Flashcards

Lecture 12: Biostatics Flashcards

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Biology 101 flashcards
1
Q

biostatistics

A

interpreting the results from dental dental research studies and publications

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2
Q

Types of data

A
  • scientific journals
  • clinical study reports
  • product manufacturers/representatives
  • presentations at dental conferences
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3
Q

statistics allow us:

A

to understand information and make clinical decisions based on data.

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4
Q

How to describe data

A

use quantitative data

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5
Q

Quantitative data:

A

mean
median
mode
SD

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6
Q

mean

A

Average of the data. Sensitive to extreme values.

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7
Q

Median

A

Middle point of the data. Less sensitive to extreme values.

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8
Q

Mode

A

Most frequent occurring value in the data.

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9
Q

Standard Deviation (SD)

A

Measure of how much the individual data points vary around the mean.

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10
Q

frequency

A

count of a given outcome or in each category

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11
Q

percentage

A

count of a given outcome per hundred showing proportion of each category out of the total

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12
Q

bar chart

A

(you know what it looks like)

shows categorical data

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13
Q

histogram

A

normal curve

shows quantitative data

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14
Q

X

A

independent variable

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15
Q

Y

A

dependent variable

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16
Q

correlation coefficient

A

r - can lie between -1 and +1

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17
Q

(+) r value

A

as X increases, Y increases

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18
Q

(-) r value

A

as X increases, Y decreases

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19
Q

the closer (r) is to +1 or -1 …

A

the stronger the relationship

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20
Q

Square of Correlation

A

r^2

is the fraction of variation in Y explained by X

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21
Q

the higher r^2 …

A

the better the fit of the regression line

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22
Q

hypothesis

A

an explanation for certain observations

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23
Q

Ho:

A

tests the hypothesis (null)

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24
Q

Null hypothesis states:

A

there is no difference between two groups being compared or no effect of a product or intervention

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25
Q

result of hypothesis testing

A

data will either “fail to reject” or will “reject” the null hypothesis

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26
Q

Ha:

A

often the one researcher thinks is the “truth”

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27
Q

Ha states:

A

there is a difference between two groups being compared or an effect of a product or intervention

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28
Q

directional

A

u1 > u2

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29
Q

non-directional

A

u1 does not equal u2

30
Q

Ho: u1 = u2

interpretation? (example)

A

the population mean for group 1 (men) is the same as the population mean for group 2 (women)

31
Q

Ha: u1 does not equal u2

interpretation? (example)

A

the population mean for group 1 (men) is different than the population mean for group 2 (women)

32
Q

Type I Error:

A

rejecting the null hypothesis that is actually true in the population

33
Q

alpha

A

level of statistical significance in Type I Error

34
Q

alpha is commonly set to:

A

0.05

maximum chance of 5% of incorrectly rejecting the null hypotheses when it is actually true

35
Q

Type II Error:

A

failing to reject (accept) the null hypotheses that is actually false in the population

36
Q

beta

A

probability of a Type II Error

37
Q

Power

A

calculated as (1-Beta) and is related to the sample size used in the study

38
Q

P-value

A

the probability, assuming that the null hypothesis is true, of seeing an effect as extreme or more extreme than that in the study by chance.

39
Q

reject the null hypothesis if:

A

P-value is less than or equal to alpha

40
Q

fail to reject the null hypothesis if:

A

P-value is greater than alpha

41
Q

interpretation:

if a p-value is 0.007, this means that:

A

the probability of obtaining data different from the null hypothesis as those obtained in the experiment is 0.007.

42
Q

Confidence intervals

A

a range of values about a sample statistic that we are confident that the true population parameter lies.
(Commonly 95%)

43
Q

t-test

A

Statistical test that can be used to determine whether the mean value of a continuous outcome variable differs significantly between two independent groups.

44
Q

t-test assumes:

A

approximate normal distribution of the variable of interest in the groups being compared.

45
Q

one-sample t-test

A

can be used when the outcome variable of interest is only being examined in one group.
(testing difference from 0 or some given value)

46
Q

matched-pair t-test

A

can be used when subjects are matched pairs and their outcomes are compared within each matched pair
(including where observations are taken on the same subjects before and after a given intervention)

47
Q

Chi-squared test

A

can be used to compare the proportion of subjects in each of two groups who have a dichotomous outcome

48
Q

chi-squared example

A

comparing the presence of periodontitis in diabetics vs. non-diabetics

49
Q

Ho (for chi-squared):

A

there is no association between row and column variables in a two-way table
(i.e. no association between having diabetes and periodontitis)

50
Q

Ha (for chi-squared):

A

there is an association between row and column variables in a two-way table
(i.e. there is an association between having diabetes and having periodontitis)

51
Q

Analysis of Variance (ANOVA)

A

a statistical method that allows for comparison of several population means

52
Q

ANOVA uses:

A

F-statistic

53
Q

F-statistic

A

reject null hypothesis that the population means of all groups are equal if P-value of F-statistic is less than or equal to alpha (0.05)

54
Q

F-statistic example

A

want to compare the strengths of composite A, composite B, and composite C to see if they are significantly different

55
Q

clinical significance

A

are findings important from a clinical standpoint?

56
Q

statistical significance

A

probability that chance is responsible of an observed difference
- p-values and/or confidence intervals
- sample size is important
(p-value says nothing about clinical relevance or quality of the study)

57
Q

fundamental issue

A

quantifying our confidence on how well the findings reflect the truth (given that there is always a role of chance)

58
Q

Two main approaches to the Fundamental Issue

A
  • Hypothesis testing and p-values.

- Confidence Interval Estimation

59
Q

Limitations of statistical inference

A

only tells about the role of chance or random error in making inference from your study population to the source population.

60
Q

Statistical inference do/do not tell you about the role of bias or confounding?

A

do not

61
Q

statistics do/do not tell you about causality?

A

do not

62
Q

Bias

A

systematic error in the design, conduct, or analysis of a study that results in a mistaken estimate of an exposure’s effect on disease

63
Q

Selection Bias

A

Systematic error in selecting subject into one or more of the study groups, such as cases and controls, or exposed and unexposed.

64
Q

Information Bias

A

Errors in procedures for gathering relevant information

65
Q

Examples of information bias

A

bias in recall
in collecting data
in interview
in reporting

66
Q

Confounding

A

Situation is which non=casual association between a given exposure and an outcome is observed as a result of the influence of a third variable usually designated a confounding variable or confounder.

67
Q

A variable is a confounder if:

A
  1. It is a known risk factor of the outcome

2. It is associated with the exposure but is not the result of the exposure

68
Q

When evaluating confounding:
Is a covariate a confounder?
(ask what two questions?)

A
  1. is it associated with exposure?

2. is it causally associated with outcome?

69
Q

if answered YES to evaluation of confounding questions:

A

step 1: calculate crude association

step 2: calculate stratum specific association

70
Q

confounding is/is not an “all or none” phenomenon?

A

is not

Eval Science Midterm 2 (3 decks)

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