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Epi/Biostats > Epi/Biostats > Flashcards

Epi/Biostats Flashcards

1
Q

Beta

A

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

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2
Q

Wald Statistic

A

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)

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3
Q

Likelihood Ratio Test

A

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)

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4
Q

Type I error

A

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

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5
Q

Type II error

A

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

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6
Q

Sensitivity

A

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

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7
Q

Specificity

A

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

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8
Q

PPV

A

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

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9
Q

NPV

A

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

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10
Q

Residual variance in regression equation

A

Error term

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11
Q

Types of bias

A
  1. Confounding
  2. Selection Bias
  3. Information Bias
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12
Q

Propensity Score

A

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

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13
Q

When to use log-binomial

A

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

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14
Q

Risk vs odds

A

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

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15
Q

P-value

A

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.

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16
Q

ICC

A

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

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17
Q

Vaccine effectiveness formula

A

(1 - adjusted OR) x 100%

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18
Q

Risk ratio formula

A

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

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19
Q

Residuals

A

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

20
Q

Kappa statistic

A

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

21
Q

Interaction coefficient

A

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

22
Q

Marginal

A

Does not include other covariates in the model

23
Q

Structural model

A

Model for counterfactual outcome

24
Q

Wilcoxon rank sum vs t-test

A

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

25
Q

Conditional / random effects model

A

Include other covariates

26
Q

Frequentist

A

Parameters are the truth

27
Q

Bayesian

A

Parameters have a distribution

28
Q

Covariance

A

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

29
Q

Correlation Coefficient

A

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

30
Q

Survey Weight

A

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

31
Q

Problems with survey weights

A

Almost always increase standard errors

32
Q

Design Effect

A

Variance from survey /
Variance estimate with SRS

33
Q

AIC

A

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

34
Q

Variance

A

Average of the squared differences of observations from the sample mean

35
Q

Normalize distribution

A

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

36
Q

Central Limit Theorem

A

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

37
Q

CI

A

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

38
Q

F-statistic

A

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

39
Q

Horvitz-Thompson estimator

A

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

40
Q

Log-linear model

A

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

41
Q

Maximum Likelihood Estimation

A

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

42
Q

Bayesian Inference

A

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)

43
Q

Poisson Offset

A

Population or person-time

44
Q

ANCOVA

A

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

45
Q

Correlation vs Covariance

A

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

46
Q

Attributes of Surveillance System

A

Simplicity
Flexibility
Acceptability
Sensitivity
Positive Predictive Value
Representativeness
Timeliness

47
Q

Sources of measurement error in surveys

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

Epi/Biostats (1 decks)

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