Epidemiology & Biostatistics

Question 1

Define Bias

Answer 1

Systematic error in the design, management or analysis of a study that causes a mistaken estimate of the exposure’s effect on the outcome.

Question 2

Explain design bias

Answer 2

Wrongly chosen sampling strategy or study design.

Question 3

Explain conduct bias

Answer 3

case enrollment, follow-up or data collection is not carried out properly and has issues.

Question 4

Explain analysis bias

Answer 4

The chosen statistical methods are wrong, variables could be miscategorized or modelling assumptions can be wrong.

Question 5

What three types of bias are there?

Answer 5

Selection Bias, Confounders and information bias (also called measurement or misclassification bias)

Question 6

Define confounding

Answer 6

A confounder is a variable that influences both the dependent variable and independent variable causing a spurious association.

Question 7

Define sensitivity

Answer 7

The proportion of positives that are correctly identified as such. (Also called the true positive rate, the recall, or probability of detection in some fields)

Question 8

Define spesificity

Answer 8

The proportion of negatives that are correctly identified as such. (Also called the true negative rate)

Question 9

What are the four main aspects of infection control?

Answer 9

Surveillance (passive/active), patient contact, hygiene, education/awareness

Question 10

How does Normal distribution fit with standard deviation?

Answer 10

68% are within 1 SD of mean and 95.5% are in 2 SD’s and 2.3% in each tail .

Question 11

Range of P-value and the usual significance level

Answer 11

range from 0-1, significance commonly 0,05

Question 12

What is Type II error?

Answer 12

Thinking there is no difference when there in truth is difference, ie. the failure to reject null hypothesis.

Question 13

What is Type I error?

Answer 13

Thinking that there is a difference, when in fact there is none, ie. the failure to accept true null hypothesis.

Question 14

Explain power and what affects it

Answer 14

The ability for a test to find a difference when there really is difference, ie a true positive. Power is high if the outcome difference is large, when significance level is high, sampling variability is low and sample size is large

Question 15

What can linear regression be used for? How to test for significance of the test?

Answer 15

explore the linear relationship between two continuous random variables with normal distribution and equal variance. Use p-value of the slope for significance and R-squared (between 0-1, percentages) to how well it fit’s the data.

Question 16

Uses of logistic regression and type of curve, results and significance?

Answer 16

To model the log of the odds of the binary outcome we are interested in as a linear function of one or more predictors, X. Sigmoid curve. Results are either coefficients (beetas) or odds ratios ratios calculated from them (exp(coefficient) and confidence intervals or p-values.

Question 17

What tests can you use for testing or modeling means of continuous data?

Answer 17

t-test, Anova and linear regression

Question 18

What should you know to estimate correct sample size,?

Answer 18

Significance level (alpha=0,05, remember type I versus type II trade off - the lover level, the higher sample), Power (1-beta, sensitivity), Variance (ie how precise are your measurements), Effect size (smaller effect, larger sample)

Question 19

Ten Steps of an Outbreak Investigation

Answer 19

1. Determine the existence of the outbreak
2. Confirm the diagnosis
3. Define a case and count cases
4. Orient the data in terms of time, place, and person
5. Determine who is at risk of becoming ill
6. Develop a hypothesis that explains the exposure that caused disease and test this hypothesis
7. Compare the hypothesis with the established facts
8. Plan a more systematic study
9. Prepare a written report
10. Execute control and prevention measures

Question 20

For example, suppose we want to estimate the survival of premature infants that are born at 25 weeks of gestation and we create a CI that ranges from 64.3% to 89.5%. How can we interpret this value?

Answer 20

“We are 95% sure that this interval 64.3% to 89.5% contains the overall proportion of surviving infants in the population.” Or another way, “We are 95% sure that the true proportion of survival for infants born at 25 weeks of gestation is between 64.3% and 89.5%”

Question 21

Describe nominal data

Answer 21

No natural order - gender, race, blood type

Question 22

Describe ordinal data

Answer 22

Natural order - tumor scales, social class

Question 23

Describe binary data

Answer 23

0-1, disease status, diagnostic test result

Question 24

Describe categorical data and test to use with it

Answer 24

Either nominal, ordinal or binary. Summarise with counts and proportions, plot with pies and bars, analyse with confidence intervals, chi-square, mcNemar(paired), logistic regression (multiple

Question 25

What classes fall into quantitative data

Answer 25

Discrete meaning integers and continuous which can take any real number

Question 26

What methods can you use for discrete count data?

Answer 26

rates for summary, trend plots and histograms for plots and confidence intervals and poisson regression

Question 27

What methods can you use for continuous data?

Answer 27

Summarise: mean, SD, median, interquartile range. Plot: histogram, scatter plot, box plot, dot plot Analyse: confidence interval, T-test, anova, correlation, simple and multiple linear regression

Question 28

Time-to-Event methods?

Answer 28

Summary: median survival time, five-year survival, hazard ratio (HR) Plot: Kaplan-meier curves Analyse: confidence interval of HR, Long-rank test, proportional hazard regression (PHR = cox regression)

Question 29

Factors that affect estimate precision

Answer 29

variability of outcome, sample size, desired confidence level