Midterm Flashcards

1
Q

p-value

A

is the probability of obtaining a result as or more extreme (more imbalance) than you did by chance. P-value tells you if the observed result can be explained by chance.

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2
Q

Relative risk

A

P1/P2 used ONLY in cohort studies.The relative risk tells you the magnitude of the disease-exposure association. A big relative risk does not necessarily mean that the p-value is small.

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3
Q

p-value depends

A

both on the magnitude of the relaitve risk as well as the sample size.

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4
Q

Relative Risk Interpretation

A

OC users are nearly 5 times more likely to have circulatory disease than non-users.

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5
Q

Relative Risk CI

A

If the 95% CI for the relative risk (RR) does not include 1 then you would reject the null which is RR=1 adn that there is evidence of a disease-exposure evidence.

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6
Q

Odds Ratio can be done in which study

A

case-control or cohort studies.

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7
Q

Odds Ratio Confidence Interval

A

CI intervalsl can be calculated. If the 96% condifence interal for the odds ratio does not include 1, it suggests that there is a significant association.

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8
Q

P-VALUE

A

A P value that is very small indicates that the observed effect is very unlikely to have arisen purely by chance, and therefore provides evidence against the null hypothesis. P values less than 0.05 are often reported as “statistically significant”, and interpreted as being small enough to justify rejection of the null hypothesis.

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9
Q

Sensitivity

A

Proportions of those with disease who are postive on the new diagnostic test.

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10
Q

Specificity

A

Proportions of those with out disease who are negative

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11
Q

Positve predictive value

A

The propotion of all individuals w/ a postive test who actually have disease.

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12
Q

Negative Predictive Value

A

The proportion of all individuals w/ a negative test who do not have the disease.

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13
Q

Simple Linear Regression

A

Y is the dependent variable.
X is the independent variable –>Predictor, Regressor, Covariate. The linear regresssion line minimizes the sum of squares of vertical deviations.

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14
Q

R2

A

is a measure of association, it represents the percent of the variance in the values of y that can be explained by nowing the value of x. between 0-1

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15
Q

R or the correlation coefficient

A

is a measure of the degree of linear relationship between two varaibles, usually labeled X & Y.

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16
Q

Slope

A

b i sthe expected change in y per unit increase in X. Larger slopes does not necessarliy mean stronger linear association.

17
Q

Limitation of R2

A

Measuring only linear relationships, senstitive to outliers. Correlation is not causation.