Appraising Pronostic Research Flashcards
Which research design is MOST COMMONLY associated with prognostic studies?
Observational studies
In cohort studies, Subjects who are likely to develop a certain condition or outcome, are recruited _____ the condition has occurred. For what?
Before,
To see if they develop the condition
How are groups in cohort studies separated?
Based on exposure
Prospective cohort study
Gather cohort> Determine exposure status> Followup to determine outcome.
Retrospective Cohort
Identify Cohort> Determine exposure status> Determine outcome
Which study is conducted after an outcome has occurred?
Case-Control Study
When subjects are studied over time, it is considered
Longitudinal
When subject data collected at one point in time?
Cross-sectional
Factors other than exposure that could account for the results in a study are
Cofounders
The greater the number of factors in a study, ______
The greater the sample must be
Prognostic studies are of _____ not ______
Association
Causality
What are the most appropriate statistics for analysis of Prognostic Studies?>
Association statistics
Most prognostic studies are
Multi-factorial
A measure of the extent to which two variables are associated, a measure of how these variables change together
Correlation Statistics
mathematical statement regarding the extent of the association between the two variables (-1 to 1)
Correlation coefficient r
What happens with negative correlations?
As one variable increases, the other decreases
What happens with Positive correlation?
As one variable increases, so does the other
An “r” of 0 means
There is no correlation
0-.25 .26-.49 .5-.69 .7-.89 .9-1
Strengths
Little to none Low Moderate High Very high
expresses the percentage of variance that is shared by the two variables
Coefficient of determination r^2
R^2 of 0 means
Of 1 means
Y cannot be predicted from X
Y can be predicted from X w/o error
used to make prediction from 1+ variables to a variable of interest
Regression analysis
the process of determining a regression equation to predict Y values based on a linear relationship with X values
Simple linear regression
Y=a+bX
Y= outcome
A=y intercept
B+slope