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