Epi/Biostats

Question 1

Beta

Answer 1

Regression Coefficient, expected (average) change in Y when X (explanatory variable) changes by one unit and the other explanatory variables stay the same

Question 2

Wald Statistic

Answer 2

Test whether regression coefficient of a variable is zero

Beta squared over var(B)
p-value = P(chi-squared > Wald statistic)

If p is small, the variable associated with the regression coefficient is important (statistically significant)

Question 3

Likelihood Ratio Test

Answer 3

Test to compare two models: one with q (“null”), the other with p variables with p>q (nested models)

If p < 0.05, the group of p-q variables in the extended model is important (statistically significant)

Question 4

Type I error

Answer 4

Probability of receiving a significance result (rejecting the null) when it is not true - False positive

Question 5

Type II error

Answer 5

Probability of failing to reject the null when it should be rejected - false negative

Question 6

Sensitivity

Answer 6

Probability of positive test given case (A / A+C)

Question 7

Specificity

Answer 7

Probability of a negative test given a non-case (B/B+D)

Question 8

PPV

Answer 8

Probability that the case is actually a case given that it tested positive (A/A + B)

Question 9

NPV

Answer 9

Negative Predictive Value probability that a non-case is true given a negative test result (D/C+D)

Question 10

Residual variance in regression equation

Answer 10

Error term

Question 11

Types of bias

Answer 11

1. Confounding
2. Selection Bias
3. Information Bias

Question 12

Propensity Score

Answer 12

Probability of a unit being assigned to a particular treatment or exposure given an observed set of covariates.

Used to reduce selection bias by equating groups on these covariates

Question 13

When to use log-binomial

Answer 13

When risk or prevalence is >10% risk odds ratio and prevalence odds ratio will overestimate the prevalence ratio so need to use log-binomial to directly estimate the prevalence ratio or risk ratio

Question 14

Risk vs odds

Answer 14

Risk = probability of occurrence of an event or outcome
Odds = probability of occurrence of an event or outcome / probability non-occurrence of the event or out come

Question 15

P-value

Answer 15

Probability of obtaining results as extreme as those observed under the null hypothesis. Protects from type I error or false positives, which lead us to conclude there is an association that isn’t really there.

Question 16

ICC

Answer 16

Intraclass correlation coefficient- the degree to which the variance of the cluster explains the variance of the whole. The between individual variance / the total variance

Question 17

Vaccine effectiveness formula

Answer 17

(1 - adjusted OR) x 100%

Question 18

Risk ratio formula

Answer 18

(a / (a+b)) / (c / (c+d))

Question 19

Residuals

Answer 19

The difference between the observed outcome and the mean in each group

Question 20

Kappa statistic

Answer 20

Determines percent of the inter-rater reliability agreement beyond what would be expected by chance

Po - Pc / 1 - Pc

> .8 is an almost perfect level of agreement beyond chance

Question 21

Interaction coefficient

Answer 21

Measures how much an association between Y and one predictor (X1) differs across levels of another predictor (X2)

Question 22

Marginal

Answer 22

Does not include other covariates in the model

Question 23

Structural model

Answer 23

Model for counterfactual outcome

Question 24

Wilcoxon rank sum vs t-test

Answer 24

WRS compares medians. T-test compares means. WRS more appropriate for data with outliers.

Question 25

Conditional / random effects model

Answer 25

Include other covariates

Question 26

Frequentist

Answer 26

Parameters are the truth

Question 27

Bayesian

Answer 27

Parameters have a distribution

Question 28

Covariance

Answer 28

Measure of joint probability of two random variables. If both variables are high at the same time covariance is positive. If one is high when the other is low, covariance is negative The sign of covariance thus shows the tendency of the linear relationship

Question 29

Correlation Coefficient

Answer 29

Normalized version of covariance. Shows the magnitude and thus the strength of the linear relationship

Question 30

Survey Weight

Answer 30

Value assigned to each case to indicate how much each case will count in a statistical procedure

Question 31

Problems with survey weights

Answer 31

Almost always increase standard errors

Question 32

Design Effect

Answer 32

Variance from survey / Variance estimate with SRS

Question 33

AIC

Answer 33

Chooses the best model from a set using : -2(log-likelihood) + 2K

Question 34

Variance

Answer 34

Average of the squared differences of observations from the sample mean

Question 35

Normalize distribution

Answer 35

Subtract the mean from each value and divide by the sd (Z = X - u/o)

Question 36

Central Limit Theorem

Answer 36

If you have a population with mean mu and standard deviation sigma and take a sufficiently large sample then the distribution of the means will be approximately normally distributed

Question 37

CI

Answer 37

Probability that a population parameter will fall between a set of values for a certain proportion of times

Question 38

F-statistic

Answer 38

Test if sample variances are equal. P<0.05 means they are not equal

Question 39

Horvitz-Thompson estimator

Answer 39

Inverse probability weighting applied to samples to account for differences between the sample and target population

Question 40

Log-linear model

Answer 40

Betas are the derivative of the log of expected y | derivative x -> (log(E(y|x)) Parameters are linear but the data isn’t B*100 Measures the percentage change in y when x increases by one unit keeping other variables constant

Question 41

Maximum Likelihood Estimation

Answer 41

MLE is a method that will find values of mean, u, and sd, o, that result in the curve that best fit the data

Question 42

Bayesian Inference

Answer 42

The process of deducing properties about a population or probability distribution from data using Bayes theorem: P(A|B) = P(B|A)*P(A)/ P(B)

Question 43

Poisson Offset

Answer 43

Population or person-time

Question 44

ANCOVA

Answer 44

Analysis of Covariance - used to test for interaction or effect measure modification -> whether means of a dependent variable are equal across levels of a categorical independent variable Generate an interaction term for the model Test if the interaction term = 0 If the interaction term is not significant, reduce to MLR/simpler model

Question 45

Correlation vs Covariance

Answer 45

Two terms that are opposed but related. Correlation shows how two variables are related, covariance shows how two variables differ

Question 46

Attributes of Surveillance System

Answer 46

Simplicity Flexibility Acceptability Sensitivity Positive Predictive Value Representativeness Timeliness

Question 47

Sources of measurement error in surveys

Answer 47

1. The tool 2. The method of data collection 3. The interviewer 4. The respondent