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Evaluative Science > Final Exam > Flashcards

Final Exam Flashcards

1
Q

Efficacy

A

The extent to which a specific intervention, procedure, regimen, or service produces beneficial results under IDEAL CONDITIONS.

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2
Q

Effectiveness

A

A measure of the extent to which a specific intervention, procedure, regimen, or service, when deployed in the field in ROUTINE CIRCUMSTANCES, does what it is intended to do for a specified population.

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3
Q

Hierarchy of Evidence Quality

A

1) Systemic Reviews and Meta-Analysis
2) Clinical Trials (in humans)
3) Longitudinal Cohort Studies
4) Case-Control Studies
5) Descriptive and Cross-Sectional Studies
6) Case reports and Case series
7) Personal opinion, subjective impressions, anecdotal accounts

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4
Q

Impact Factor

A

Total number of citations to articles appearing in that journal/ Total number of articles published

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5
Q

Three types of papers published in journals

A

1) Research Reports
2) Reviews of literature to summarize knowledge in a particular area
3) Commentaries

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6
Q

Components of a Research Report

A

1) Title
2) Author
3) Date of submission and acceptance
4) Abstract or Summary
5) Introduction
6) Materials and Methods
7) Results
8) Discussion
9) Conclusion

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7
Q

Scales of Measurement (3)

A

1) Nominal
2) Ordinal
3) Continuous
- Interval
- Ratio

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8
Q

Measures of Central Tendency

A

1) Mode
2) Median
3) Mean

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9
Q

Measures of Variability

A

1) Range
2) Interquartile Range
3) Variance
4) Standard Deviation
5) Coefficient of Variation
6) Standard error of the Mean

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10
Q

Standard Deviation

A

Used to measure variability of Individual subjects around a sample mean

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11
Q

Standard Error

A

Used to assess how accurately a sample mean reflects a population mean

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12
Q

Central Limit Theorem

A

In random samples of N observations, sample means will be normally distributed.

significance: allows us to make inference about the population from which our samples are drawn.

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13
Q

Confidence interval for a mean

A

Lower confidence bound and an upper confidence bound with the population mean contained within this interval (1-alpha) percent of the time.

For normal and t distributions, the confidence interval is centered around the mean. Confidence intervals get larger as you decrease the amount of error.

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14
Q

Research Hypothesis

A

Prediction based on the theory being tested on preliminary observations; on concentrations or guesses. (ex: gender affects intelligence)

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15
Q

Null Hypothesis

A

A mathematical statement, usually in population parameters, of no difference. Can be directional or non-directional.

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16
Q

Alternative Hypothesis

A

An objective hypothetical statement of the research hypothesis similar to the null hypothesis. We PROVE the alternative hypothesis by showing that the null hypothesis is not true.

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17
Q

Type I Error

A

Probability associated with rejecting the null hypothesis when it is true. (saying there are effects when there are none) ex: saying males are smarter than females when they are not.

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18
Q

Type II Error

A

The probability associated with accepting the null hypothesis when it is actually false. (saying there are no effects when there actually are)`

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19
Q

Power

A

The probability of rejecting the null hypothesis when it is false. The ability of a statistical test to detect a specified difference if that difference exists. Directly proportional to the sample size.

1-beta. Beta is Type 2 Error aka Null hypothesis is false but you accept it.

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20
Q

Alpha (Type 1) and Beta (Type 2) are _______ proportional to each other and the sample size N

A

inversely

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21
Q

Methods to increase power

A

1) Increase Type 1 Error you are willing to tolerate
2) Increase the sample size
3) Increase the deviation from the null hypothesis you are willing to tolerate (big differences are easier to prove than small differences)
4) Decrease variability
5) Use a directional alternative hypothesis if appropriate.
6) Use the most efficient (most powerful) statistical test.

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22
Q

Dependent Variable

A

The variable we measure and compare

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23
Q

Independent Variable

A

The variable we manipulate

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24
Q

Descriptive Studies

A

Descriptive patterns of disease occurrence in relation to persons, place, and time. (Data provided essential to public health administrators and epidemiologists)

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25
Q

Correlational Studies

A

Measures representing characteristics of entire populations are used to describe disease in relation to some factor of interest such as age, utilization of health services, consumption of food etc.

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26
Q

Statistics used in Correlational and Regression Studies

A

1) Pearson coefficient: r- strength of association
2) Coefficient of determination: R^2- strength of association
3) Regression Coefficient: B1: Change in the dependent variable for every one unit change in the independent variable
4) Intercept: B0: Value of the dependent variable when the independent variable is 0.

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27
Q

r

A

Person Coefficient
Ranges from -1 to 1. Unitless.
Measures the strength of a relationship.

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28
Q

R^2

A

Coefficient of Determination
Measures the strength of a relationship by explaining the % variance of the dependent variable accounted for by the independent variable
Values range from 0-1 or 0-100%.

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29
Q

B1

A

Regression Coefficient

Shows the change in the dependent variable for every one unit change in the independent variable.

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30
Q

B0

A

Intercept

Shows the value of the dependent variable when the independent variable is 0.

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31
Q

Strengths of correlational studies

A

1) Quick
2) Inexpensive
3) Usually using available data/information

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32
Q

Limitations of Correlational Studies

A

Inability to link disease and exposure in an individual, data is collapsed, not specific to a person.

33
Q

Case-Control Studies

A

Observational analytic study.
Subjects selected on the basis of whether they do or do not have a particular type of disease. (and then look back retrospectively at a risk factor and its effects)
Developed in 20th century when there was a shift from acute disease to chronic diseases.

34
Q

Case-Control Study Advantage

A
  • Allows study of diseases with ling latency periods
  • Efficient in time and cost
  • Allows for adequate numbers of diseased and non-diseased individuals to be identified.
  • Evaluation of RARE diseases
  • Can evaluate a wide range of potential etiologic (cause) exposures, and the interrelationships amongst them.
35
Q

Case-Control Study Disadvantages

A
  • Disease and exposure have already happened when the patient enters the study
  • Susceptible to bias: a) slection bias b) recall bias (differential reporting)
36
Q

Case-Control Study Uses

A
  • Test a Specific Hypothesis

- Explore a range of exposures among affected or non-affected individuals.

37
Q

Cohort Study Types

A

1) Prospective
2) Retrospective
3) Both Pro and Retro-spective

38
Q

Measures of Associations

A

Summary statistic that describes the association between exposure and risk of developing disease.
Relative Risk/Risk Ratio
Odds Ratio

39
Q

Relative Risk (RR)

A

Likelihood of developing the disease in the exposed group relative to those not exposed.

Incidence in Exposed group: Ie
Incidence in non-exposed group: Io

40
Q

Prospective Cohort Studies Advantages

A
  • Temporal sequence between exposure and disease established
  • Assessing the effects of RARE exposures
  • Adequate numbers of exposed and non exposed individuals identified
  • Allows examination of multiple effects of a single exposure
  • Minimize potential for selection bias
41
Q

Prospective Cohort Study Disadvantages

A
  • Follow up for many years
  • Time consuming
  • Expensive
  • Boas associated with loss to follow-up
42
Q

Cohort Studies: Retrospective

A
  • Exposure/Outcome Disease have already happened at the time of study
  • Quicker and cheaper
  • Efficient for diseases with a long latency period requiring many years to accrue sufficient endpoints
  • Same disadvantages as in case-control
43
Q

Difference between case-control and Retrospective Cohort

A

Case-Control: What was the risk factor/cause? (Odds ratio)

Retrospective Cohort: Already know the risk factor/cause, studies exposure to disease outcome (can use both OR and RR)

44
Q

Dependent t-test (One-sample t-test)

A

Used to see if your sample is different from a specified population

45
Q

Independent t-test (Two-sample t-test)

A

idk

46
Q

degrees of freedom (df)

A

df=N-1

df= (ncon-1)+(nexp-1)

47
Q

t

A

t- (difference of the means)/(SE of the difference of the means)

48
Q

Independent t-test assumptions

A

1) Continuos measurement scales
2) Samples are drawn from populations with normal distributions
3) Samples are drawn from populations with equal variances
4) Samples are independent

Remember: People want to be measured on the same scale, normal, equal, and independent.

49
Q

Reasons for failure to demonstrate significance

A

1) it’s the truth- they’re not significant

2) sample size is too small

50
Q

ANOVA

A

Analysis of Variance- Divides variance of the entire experiment into two or more components:

  • Variance due to treatment effects
  • Residual, Unexplained, or Error Variance
51
Q

Anova Table F

A

= MStreatment/MSerror

52
Q

ANOVA Assumptions

A

1) Interval/ratio (continuos) measurement scales
2) Populations follow a normal distribution
3) Populations have equal variance
4) Independent groups

53
Q

Pairwise Comparisons

A

Planned vs. Unplanned

Planned: theoretical basis for comparison PRIOR to study
Unplanned: Effects suggested by the data

54
Q

Single Variable Tests

A

Z test:

  • we know the population and the standard deviation
  • estimate the mean from a sample

t-test:
-estimate both SD and mean

55
Q

Two Variable Statistical Tests

A

Y dependent/Xdependent

Nominal/Nominal: Chi-Square
Nominal/Continuous: Logistic Regression

Continuous/Nominal: t-test or ANOVA
Continuous/Continuous Correlation and Regression

56
Q

Two -Variable Tests

A
  • Both variable Continuous
  • Independent nominal/ordinal, Dependent Continuous
  • Independent nominal/ordinal, Dependent nominal/ordinal
  • Independent continuous and Dependent nominal/ordinal
57
Q

Both Continuous

A
  • Correlation (r) strength of linear relationship

- Regression (linear regression): Usually assigns the dependent variable to the variable that would be estimated.

58
Q

One Nominal One Continuous
Dependent Continuous
Nominal Variable has TWO levels
Tests the null hypothesis that there’s no difference between the groups versus the alternative that the two groups differ

A
  • t-test

- anova

59
Q

Requirements for One Nominal/One Contnuous

A

-2 groups are each normally distributed
-groups have roughly same SD
(if above not met, non-parametric tests can be used)
-groups are independent of each other.

60
Q

One Nominal One Continuous
Nominal Variable has TWO OR MORE levels
Tests the null hypothesis versus the alternative that AT LEAST ONE of the groups differs from some or all of the others.

A

-ANOVA

further tests have to be done to see which groups are different.

61
Q

Both Dependent and Independent variables are nominal or ordinal

A

chi-square

62
Q

Dependent is nominal/ordinal and Independent is Continuous

A

Logistic Regression

63
Q

Parametric

A

Normal Distribution

  • Linear Regression & Correlation
  • Two sample t–test (Independent t-test)
  • ANOVA
  • One sample t-test (Paired t-test) (dependen t-test)
  • Repeated measures of ANOVA
64
Q

Non-Parametric

A

No Normal Distribution

  • Chi-Square Test
  • Wilcoxon Rank Sum Test
  • Kruskal-Wallis Test
  • Wilcoxon Sign Rank Test
  • McNemar Test
  • Friedman Test
65
Q

Wilcoxon Rank Sums Test

A

Uses Ranks: Is the average rank for each group the same?

66
Q

Wilcoxon assumptions

A
  • Independent Groups

- Dependent Variable ordinal (or stronger)

67
Q

Kruskal-Wallis Test

A
  • Extension of Wilcoxon Rank Sum Test
  • At two groups it is like the t-test and ANOVA
  • can handle more than two groups
  • some assumptions
68
Q

Dependent Group Tests

A

One Sample or Paired Data:

  • Wilcoxon Sign Rank Test:Ordinal Data
  • McNemar Test: Nominal Data

More than one Group:
-Friedman Test

69
Q

Sign-Rank Test

A 
Repeated measures (dependent observations)-ordinal data
Ex: Evaluation of 2 facult by dental students
70
Q

Assumptions of Sign-Rank Test

A 
Ordinal measures (within and between)
Dependency between measurements
71
Q

McNemar Test

A

Ex: Two anesthetics compared
McNemar Assumptions:
- Two observations are related (not independent)
-Categories are mutually exclusive
-Expected frequencies under the null hypothesis should be 5 or more ((0.5)*(A+B))

72
Q

Prevalence

A

Number of people in a population who have a disease at a GIVEN POINT IN TIME. Prevalence measure the frequency of all current diseases (old and new)

73
Q

Incidence

A

A measure f the number of lesions/PERIOD OF TIME

Limited in that they only measure the number of new or initial lesions per unit of time

74
Q

Caries Prevalence: DMFT/DMFS

A 
Prevalence of caries in an individual
D: Decayed
M: Missing
F: Filled
T/S: Teeth/Surfaces
75
Q

A surface with both caries and a restoration is scored

A

D

76
Q

Maximum value for DMFS

A

128

77
Q

Molars and Premolars have

A

5 Surfaces

78
Q

Anterior teeth have

A

4 Surfaces

Evaluative Science (1 decks)

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