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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% 

pvalue

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 

KaplanMeier 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)

1/ARD


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 