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EBP 2 > Week 8 > Flashcards

Week 8 Flashcards

1
Q

What is epidemiology?

A

A study aimed at studying determinants of disease, injury or dysfunction in populations

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2
Q

Epidemiology is another way of saying ____

A

Epidemiology is another way of saying risk

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3
Q

Risk in PT can be expressed in terms of _____

A

• Experiencing an adverse outcome
• Patients not improving with treatment
• Requiring more invasive or expensive subsequent
interventions in spite of treatment

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4
Q

Epidemiology generally uses observational designs with ___ variables

A

Epidemiology generally uses observational designs with dichotomous variables

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5
Q

What studies are intended to study risk factors?

A

Case-Control & Cohort Studies

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6
Q

Case-Control & Cohort Studies looks at the ____ between disease & exposure

A

Case-Control & Cohort Studies looks at the association (“cause”) between disease &
exposure

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7
Q

The IV and DV in case-control & cohort studies are what kind of variables?

A

Dichotomous

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8
Q

In case-control & cohort studies, there is ___ strength in thinking something is causal of the other

A

In case-control & cohort studies, there is less strength in thinking something is causal of the other

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9
Q

How are subjects in a cohort study selected?

A

Subjects selected based on

exposure or not

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10
Q

Is a cohort study usually prospective or retrospective?

A

Usually prospective, but

can be prospective or retrospective

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11
Q

Does a cohort study work for rare conditions?

A

Doesn’t work well for very

rare conditions

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12
Q

What does a cohort study examine?

A

Examine if there is a different

incidence of disease

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13
Q

How are subjects in a case control study selected?

A

Subjects selected based on
whether or not they have
disorder

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14
Q

Where should the controls of a case control be selected from?

A

Controls should be selected

from same population as Cases

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15
Q

What does a case-control study examine?

A

Examine if exposure is different between cases and control

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16
Q

What condition does a case control work especially well for?

A

Works especially well for very

rare conditions

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17
Q

What are the primary ways to quantify risk?

A
  • Relative Risk (RR)

* Odds Ratios (OR)

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18
Q

What do the primary ways to quantify risk actually quantify?

A

Both quantify strength of association between “exposure” and “disease”

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19
Q

In what study is RR used and in what study is OR used?

A
  • RR in Cohort studies

* OR in Case-control studies

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20
Q

What does it mean when an RR or OR = 1 ?

A
  • = “null value”

* No association between an exposure and a disease

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21
Q

What does it mean when an RR or OR > 1?

A
  • A positive association between an exposure and a disease

* The exposure is considered to be harmful

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22
Q

What does it mean when an RR or OR < 1?

A
  • A negative association between an exposure and a disease

* The exposure is protective

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23
Q

RR is the ratio of ___ compared to ____

A

Incidence of disease among
exposed individuals compared to Incidence of disease among
unexposed individuals

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24
Q

Since OR is selected based on whether they have disease or not, so can’t determine rate of ___

A

Since OR is selected based on whether they have disease or not, so can’t determine rate of “incidence”

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25
OR is the ratio of ___ compared to ____
Odds of exposure among cases (with disease) compared to Odds of exposure among controls (w/o disease)
26
The computation of OR is kinda like ___
The computation of OR is kinda like *kappa*
27
____ uses relationships (correlation) as a basis for prediction
*Regression* uses relationships (correlation) as a basis for prediction
28
What are the characteristics of a linear regression?
``` X and Y are correlated • X = independent variable (= predictor variable) • Y = dependent (or criterion) variable • We use X to predict Y • The value of Y depends on X • (Thats why Y is called the dependent variable) ```
29
What is the error from line/ residual in a regression line?
The distance between each data point and the line of best fit
30
Residuals are squared to eliminate ___ and penalize for ___
Residuals are squared to eliminate *sign and penalize for worse errors*
31
What is the line of best fit?
Line with least squared errors
32
Is regression a parametric or non parametric statistic?
Parametric
33
What are the assumptions of a linear regression analysis?
1. Linear relationship = approximation of true line in population 2. For every X there is a normal distribution of Y • Sample data include random samplings from these distributions on Y 3. Homogeneity of variance
34
What is a way to test the assumptions of a linear regression?
Analysis of residuals by: Plot Residuals on Y-axis, vs predicted values on x-axis
35
What assumption of linear regression does the analysis of residuals test the most?
Homogeneity of variance
36
What are you looking for in the analysis of residuals to test linear regression assumptions (assumptions are met)?
Looking for the residual's distance between the predictive value and the actual value be symmetric and consistent throughout
37
What does the analysis f residuals graph look like when the assumptions of linear regression are not met?
- The graph starts to get wider the further it goes(data is further away from the line, the higher you go) - Data is not symmetric
38
What happens if the linear regressions assumptions are not met?
Use a non linear regression
39
What are the thing that helps a researcher determine whether to retain or discard a data with an outlier?
• Due to peculiar circumstances? • Can discard if error identified • Generally not justified on statistical grounds alone
40
What are the peculiar circumstances that have to be taken into consideration when determining whether to retain or discard a data?
* Measurement error * Recording error * Equipment malfunction * Miscalculation * Aberrant subject (should have been excluded)
41
What are the things that looks a the accuracy of prediction of the regression equation?
• Correlation coefficient (R) Coefficient of determination (R2) • ANOVA of Regression
42
What are the characteristics of a correlation coefficient as it relates to the accuracy of prediction?
* Rough indicator of goodness of fit for regression line | * Same as correlation coefficient (r)
43
What does the coefficient of determination represent?
Proportion of variance in Y scores that can be explained by X scores
44
What does the ANOVA of regression test?
Tests hypothesis that predictive relationship occurred by chance (Ho: b = 0)
45
What does it mean when b=0 in an ANOVA of regression?
If b (slope) = 0, line is horizontal = no relationship
46
What happens when p< than alpha in an ANOVA of regression?
If p < than alpha, reject the null and conclude the predictive relationship is significant
47
How many predictors are in a simple linear regression model and how many are in a multiple linear regression model?
There is only 1 predictor in a simple model and there are multiple predictors in a multiple linear regression model
48
What are the assumptions of a multiple linear regression analysis?
1. Linear relationship = approximation of true line in population 2. For every X there is a normal distribution of Y • Sample data include random samplings from these distributions on Y 3. Homogeneity of variance 4. DV = continuous measure
49
Coefficient of determination is the square of ____
Coefficient of determination is the square of *correlation coefficient*
50
What is an adjusted R squared and what do you get punished for?
Chance corrected R2, get punished for having more predictor variables
51
What is the goal of a linear regression?
The more you can predict with fewer variables, the better
52
What is a regression coefficient?
* The value/slope in the linear equation | * The rate of change in Y for each unit change of X
53
What is a standardized beta weight helpful for?
Helpful to know relative contribution of each predictor | variable
54
Which will always be higher or the same, out of an R square or an adjusted R square?
The R square will always be higher than or equal to the adjusted R square
55
What is multicolinearity?
When the Xs in the model are substantially correlated with each other
56
What does multicolinearity create a problem with?
Creates problems with interpretations of b weights
57
What is the risk of the force entry of all possible predictors in a multiple regression method?
* Risk of multicolinearity (correlation between predictors) * Risk of retaining non-contributing predictors * Risk of more predictors than justified by sample size
58
How is the criteria in a stepwise procedure set?
Criteria set to retain or reject predictors
59
Which predictor is entered first in a stepwise procedure?
Predictor with highest partial correlation entered first
60
What does a stepwise procedure result in?
Should result in model with greatest parsimony and | least multicolinearity
61
What is a parsimony model?
A model that is the most predictive, with the least amount of variables
62
What is a simple correlation?
The overlap between 2 variables
63
What is a partial correlation?
The unique correlation between 2 variables
64
What is a forward stepwise regression method?
A method that starts with no predictors, then adds them, starting with the strongest
65
What is a backward stepwise regression method?
A method that starts with all predictors, then removes them, starting with the weakest
66
What is a stepwise stepwise regression method?
A method that starts with no predictors, then add, | but can also remove
67
What is the level of measurement for predictors/ IV in a stepwise multiple linear regression model?
* Most predictors are continuous scales * Can also use dichotomous or ordinal scale predictors * But not multicategory nominal (e.g. race)
68
A large number of predictors is needed in a stepwise multiple linear regression hence it requires ___
A large number of predictors in a regression requires *a very large sample size*
69
What is the rule of thumb for the predictors of a stepwise multiple linear regression model?
At least 10-15 subjects per predictor in model
70
What happens if there are too many or too few predictors in a stepwise multiple linear regression model?
Become susceptible to “model overfit” (chance associations, i.e. type 1 error).
71
What is a logistic regression?
When you are trying to predict a dichotomous variable
72
What is the DV level of measurement of a logistic regression?
Dichotomous
73
What is the predictor/ IV level of measurement of a logistic regression?
Continuous, ordinal, or dichotomous
74
What are the pros MANOVA?
• MANOVA gets around multiplicity problem (familywise alpha: increased Type I error risk) • MANOVA can be more powerful if DVs related
75
What are the cons MANOVA?
• “Combo DV” is not directly interpretable • If statistically significant, then must follow up with post-hoc ANOVAs
76
What is a factor analysis?
Method of simplifying & organizing large sets of variable into fewer abstract components
77
What is a path analysis?
Visual modeling of both direct & indirect relationships
78
Path analysis is an extension of ____
Path analysis is an extension of *multiple regression*
79
Compared to a multiple regression, a path analysis is more __ and ____
Compared to a multiple regression, a path analysis is more *flexible and comprehensive*
80
What can a path analysis analyze?
Can analyze both direct and indirect relationships between 1 or more exogenous variables (IVs) and 1 or more endogenous variables (DVs)
81
What is a hierarchical linear modeling also known as?
* Multilevel linear modeling | * Linear mixed modeling
82
A hierarchical linear modeling comes from what type of analysis?
The type of analysis where you have some variables nested within other variables (students nested in a classroom when studying schools)
83
A hierarchical linear modeling, has far fewer __ and is highly ___
A hierarchical linear modeling, has far *fewer assumption and highly flexible*
84
What is the Number Needed to Treat (NNT)?
How many patients you have to provide treatment to in order to prevent one bad outcome
85
What is Control Event Rate (CER)?
Percent of patients in control group with bad outcome
86
What is Experimental Event Rate (EER)?
Percent of patients in experimental group with bad outcome
87
What is the equation for RR?
EER/CER

EBP 2 (7 decks)

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