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17 Cards in this Set

  • Front
  • Back
binary variable
a variable with only two values
continuous variable
a variable in which the numbers act as numerical values.
ex: age, birth weight
also called quantitative variable
nominal variable
categorica variable where the categories are unordered.
E.g.: gender, birth type
ordinal variable
categorical variable where the categories imply an order on some continuum (e.g., level of health problems)
Type I Error
rejecting the null hypothesis when it is true
concluding there is a difference when there is not
called alpha
level of significance of the test
probability of committing a type I error
Type II Error
accepting the null hypothesis when it is false
concluding there is no difference when there is one
called beta
power of the study
ability to detect a difference if a true difference exists
i.e., probability of rejecting null hypothesis when it is false
1 - beta
beta usu. = 0.2, so power = 80%
the probability that an observed association is due to chance
logistic regression
outcome of interest is binary
allows adjustment for confounding variables
provides direct estimate of odds ratio for each indep. variable
correlation coefficient
measures strength of association btwn 2 continuous variables
between -1 and 1
not equal to slope of line
Kaplan-Meier plot
survival vs. time
each "step" represents an outcome event
multiple regression
like linear regression but allows multiple independent variables
can be used to adjust for confounders
case fatality
(deaths due to a disease) / (number of people with that disease)
absolute risk difference
difference between risk of outcome in exposed group and risk in unexposed
number needed to treat (NNT)
Cox Proportional Hazards Regression
similar to other multiple regressions
form of survival analysis
assesses independent effect of multiple variables on survival
can predict rate at which outcomes will occur
estimates relative risk