Biostats

Question 1

Cross-sectional study

Answer 1

Collects data from a group of people to assess disease frequency at a particular point in time
May show risk association, but not causality
“What’s happening?”
Measures prevalence

Question 2

Case-control study

Answer 2

Compares group with disease to a group without disease
Looks for prior exposure/risk
Retrospective
“What happened?”
Measures odds ratio: OR = [(a/c)/(b/d)] = (ad)/(bc)

Question 3

Cohort study

Answer 3

Compares initially disease-free people in two groups to see who develops disease: one with exposure/risk, and one without exposure/risk
Can show if exposure/risk increases disease likelihood
Retrospective OR prospective
“Who will develop/developed disease?”
Measures relative risk: RR = [a/(a+b)]/[c/(c+d)]

Question 4

Twin concordance, adoption studies

Answer 4

Measure heritability and environmental influence
Mono- vs dizygotic twins
Siblings with biological vs adoptive parents

Question 5

Clinical trial phase goals

Answer 5

I: Is it safe?
II: Does it work?
III: Is it as good or better than current treatments?
IV: Can it stay?

Question 6

Odds ratio

Answer 6

Odds that a group with disease was exposed to a risk divided by the odds that the group without the disease was exposed
OR = (a/c)/(b/d) = (ad)/(bc)
Typically used for case-control studies

Question 7

Relative risk

Answer 7

Risk of developing disease in the exposed group divided by risk in the unexposed group
RR = [a/(a+b)]/[c/(c+d)]
Typically used in cohort studies
If prevalence is low, OR ~ RR

Question 8

Attributable risk

Answer 8

Difference in risk between exposed and unexposed groups, i.e. proportion of disease occurrences attributable to an exposure
AR = a/(a+b) - c/(c+d)

Question 9

Relative risk reduction

Answer 9

Proportion of risk reduction attributable to an intervention as compared to a control
RRR = 1 - RR = 1 - [a/(a+b)]/[c/(c+d)]

Question 10

Absolute risk reduction

Answer 10

Difference in risk attributable to the intervention as compared to the control
ARR = c/(c+d) - a/(a+b) = -AR

Question 11

Number needed to treat

Answer 11

NNT = 1/ARR (treat has more letters than harm)

Question 12

Number needed to harm

Answer 12

NNH = 1/AR

Question 13

Bias in recruiting participants

Answer 13

Selection, sampling, referral, allocation bias
E.g. Berkson bias - study population is from a hospital and less healthy than the general population
Healthy worker effect - (opposite of Berkson)
Non-response - nonrespondents differ from participants meaningfully
Randomize to reduce

Question 14

Procedure bias

Answer 14

Subjects in different groups are not treated the same
Includes detection bias: Those with a risk factor undergo greater diagnostic scrutiny than those without the risk
Use blinding and placebos to reduce

Question 15

Recall bias

Answer 15

Awareness of disorder alters recall by subjects
Common in retrospective studies
Decrease time from exposure to follow-up to reduce

Question 16

Observer-expectancy bias

Answer 16

Researcher’s belief in a treatment’s efficacy changes outcomes
AKA Pygmalion effect or self-fulfilling prophecy
Use blinding and placebos to reduce

Question 17

Confounding bias

Answer 17

Factor is related to both exposure and outcome, but not the causal pathway
Reduce with multiple/repeat studies, matching of patients with similar characteristics in both control and treatment groups, crossover studies where subjects act as their own controls

Question 18

Lead-time bias

Answer 18

Early detection is confused with increased survival
Especially important for studies of long-term chronic disease
Reduce by measuring back-end survival by controlling for disease severity at time of diagnosis

Question 19

Hawthorne effect

Answer 19

AKA observer effect

Subjects tend to change their behavior when they know they’re being observed

Question 20

alpha definition

Answer 20

Probability of making a type I error (finding a difference between control and experimental groups when one does not exist)

Question 21

beta definition

Answer 21

Probability of making a type II error (stating there is no difference between control and experimental groups when one does exist)
beta increases as alpha decreases

Question 22

Power

Answer 22

1 - b

Increases as beta decreases: Increased precision, increased effect, or INCREASED SAMPLE SIZE

Question 23

t-test

Answer 23

Checks differences between the MEANS OF 2 GROUPS

E.g. BP between males/females

Question 24

ANOVA

Answer 24

Checks differences between the MEANS OF AT LEAST 3 GROUPS

E.g. BP between members of 3 ethnic groups

Question 25

Chi-square test

Answer 25

Checks differences between 2 or more PERCENTAGES OR PROPORTIONS OF CATEGORICAL OUTCOMES
E.g. Percentage of members of 3 ethnic groups with HTN

Question 26

Ordinal data

Answer 26

Data ordered by a position on a scale
Usually categorical - cannot perform arithmetic with these
E.g. Runners finishing in 1st, 3rd, 5th places
Qualitative - Non-parametric

Question 27

Interval data

Answer 27

Data measured along a scale in which each position is equidistant
Quantitative - Parametric
Allows for distances between data points to be equivalent in a way
E.g. Happiness scale from 1-10 or Runners finishing a 5k between 18:00-18:59, 19:00-19:59, 20:00-20:59, etc.

Question 28

Nominal data

Answer 28

Data differentiated by a simple naming system
Usually categorical - E.g. “employee”
May have a number assigned, but is not ordinal (E.g. Runner’s ID number or an athlete’s jersey number)
Qualitative - Non-parametric

Question 29

Ratio data

Answer 29

Data in which numbers are multiples of each other and can be mathematically compared. Zero has a meaning on the scale used for this data
E.g. Runner’s finishing time for a race
Quantitative - Parametric

Question 30

Continuous data

Answer 30

Measured along a continuous scale allowing for infinitely fine subdivision
Vs. discrete where data falls into bins like with interval data

Question 31

Parametric data

Answer 31

Quantitative, forms predictable distributions (e.g. normal)

Can use arithmetic to gain insight into the datasets

Question 32

Non-parametric data

Answer 32

Qualitative, does not assume any distribution

Question 33

Likelihood ratio for a positive test

Answer 33

Sensitivity/(1-Specificity)

Question 34

Likelihood ratio for a negative test

Answer 34

(1-Sensitivity)/Specificity

Question 35

Sensitivity

Answer 35

Chance a test detects disease when it is present
(True-positive rate)
a/(a+c)
TP/(TP+FN)

Question 36

Specificity

Answer 36

Chance a test indicates no disease when none is present
(True-negative rate)
d/(b+d)
TN/(TN+FP)

Question 37

Positive predictive value

Answer 37

Proportion of positive test results that are true positives
a/(a+b)
TP/(TP+FP)

Question 38

Negative predictive value

Answer 38

Proportion of negative test results that are true negatives
d/(c+d)
TN/(TN+FN)

Question 39

Incidence

Answer 39

New cases occuring during a particular time period

N(new cases)/N(at risk)

Question 40

Prevalence

Answer 40

Number of people affected by a disease at a given point in time
N(w/disease)/N(population)
Increases w/ incidence
Decreases w/ death of affecteds and recovery

Question 41

Standard error of the mean

Answer 41

Used for samples of a population

SEM = s/sqrt(n), where s = stddev of sample

Question 42

Correlation coefficient

Answer 42

r
Always between -1 and 1
More negative = stronger negative correlation, etc.

Question 43

Coefficient of determination

Answer 43

r^2
Always between 0 and 1
Represents the amount of variance in the dependent variable (y) due to the independent variable (x):
y = a + bx