Week 8 Flashcards
What is epidemiology?
A study aimed at studying determinants of disease, injury or dysfunction in populations
Epidemiology is another way of saying ____
Epidemiology is another way of saying risk
Risk in PT can be expressed in terms of _____
• Experiencing an adverse outcome
• Patients not improving with treatment
• Requiring more invasive or expensive subsequent
interventions in spite of treatment
Epidemiology generally uses observational designs with ___ variables
Epidemiology generally uses observational designs with dichotomous variables
What studies are intended to study risk factors?
Case-Control & Cohort Studies
Case-Control & Cohort Studies looks at the ____ between disease & exposure
Case-Control & Cohort Studies looks at the association (“cause”) between disease &
exposure
The IV and DV in case-control & cohort studies are what kind of variables?
Dichotomous
In case-control & cohort studies, there is ___ strength in thinking something is causal of the other
In case-control & cohort studies, there is less strength in thinking something is causal of the other
How are subjects in a cohort study selected?
Subjects selected based on
exposure or not
Is a cohort study usually prospective or retrospective?
Usually prospective, but
can be prospective or retrospective
Does a cohort study work for rare conditions?
Doesn’t work well for very
rare conditions
What does a cohort study examine?
Examine if there is a different
incidence of disease
How are subjects in a case control study selected?
Subjects selected based on
whether or not they have
disorder
Where should the controls of a case control be selected from?
Controls should be selected
from same population as Cases
What does a case-control study examine?
Examine if exposure is different between cases and control
What condition does a case control work especially well for?
Works especially well for very
rare conditions
What are the primary ways to quantify risk?
- Relative Risk (RR)
* Odds Ratios (OR)
What do the primary ways to quantify risk actually quantify?
Both quantify strength of association between “exposure” and “disease”
In what study is RR used and in what study is OR used?
- RR in Cohort studies
* OR in Case-control studies
What does it mean when an RR or OR = 1 ?
- = “null value”
* No association between an exposure and a disease
What does it mean when an RR or OR > 1?
- A positive association between an exposure and a disease
* The exposure is considered to be harmful
What does it mean when an RR or OR < 1?
- A negative association between an exposure and a disease
* The exposure is protective
RR is the ratio of ___ compared to ____
Incidence of disease among
exposed individuals compared to Incidence of disease among
unexposed individuals
Since OR is selected based on whether they have disease or not, so can’t determine rate of ___
Since OR is selected based on whether they have disease or not, so can’t determine rate of “incidence”
OR is the ratio of ___ compared to ____
Odds of exposure among cases (with disease) compared to Odds of exposure among controls (w/o disease)
The computation of OR is kinda like ___
The computation of OR is kinda like kappa
____ uses relationships (correlation) as a basis for prediction
Regression uses relationships (correlation) as a basis for prediction
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)
What is the error from line/ residual in a regression line?
The distance between each data point and the line of best fit
Residuals are squared to eliminate ___ and penalize for ___
Residuals are squared to eliminate sign and penalize for worse errors
What is the line of best fit?
Line with least squared errors
Is regression a parametric or non parametric statistic?
Parametric
What are the assumptions of a linear regression analysis?
- Linear relationship = approximation of true line in population
- For every X there is a normal distribution of Y
• Sample data include random samplings from these distributions on Y - Homogeneity of variance
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