Biostatistics

Question 1

Cross-sectional study

Answer 1

assess frequency of disease (and related risk factors) at a PARTICULAR POINT IN TIME. Measures disease PREVALENCE. Cannot establish causality

Question 2

Case-control study (retrospective)

Answer 2

“known disease” compares a group of people with disease to a group without disease. Looks for prior exposure or risk factor Measures OR

Question 3

Cohort Study (prospective or retrospective)

Answer 3

“known exposure” compares a group with a given exposure or risk factor to a group without such experience. Looks to see if exposure increases the likelihood of disease. Measures RR

Question 4

Sensitivity

Answer 4

TP/(TP+FN) Highly SeNsitive when Negative rules disease OUT “SNNOUT” Screening test

Question 5

Specificity

Answer 5

TN/(TN+FP) Highly SPecific when Positive test rules disease IN “SPPIN” Confirmatory testing

Question 6

Positive Predictive Value (PPV)

Answer 6

proportion of positive test results that are true positive TP/(TP+FP) Varies directly with prevalence/pre-test probability

Question 7

Negative Predictive Value (NPV)

Answer 7

proportion of negative test results that are true negative TN/(TN+FN) Varies inversely with prevalence/pre-test probability

Question 8

Incidence

Answer 8

looks at new cases incidence rate = # of new cases / # of people at risk

Question 9

Prevalence

Answer 9

looks at all current cases = # of existing case / # of people at risk ~pretest probability

Question 10

Odds Ratio

Answer 10

used for case control studies

Question 11

Relative Risk

Answer 11

used in cohort studies

Question 12

Attributable Risk

Answer 12

Question 13

Relative Risk Reduction (RRR)

Answer 13

RRR = 1 - RR

proportion of risk reduction attributable to intervention

e.g. if 2% of patients who receive a flu shot develop the flu, while 8% of unvaccinated patient develop the flu, then:

RR = 2/8 = 0.25 and RRR = 0.75

Question 14

Absolute Relative Risk (ARR)

Answer 14

Difference in Risk attributable to the intervention as compared to a control

e.g. if 8% of people who receive a placebo vaccine develop the flu vs. 2% of people who receive a flu vaccine, then:

ARR = 8% - 2% = 6% = 0.06

Question 15

Number Needed to Treat (NNT)

Answer 15

NNT = 1 / ARR

(for the benefit of one patient)

Question 16

Number Needed to Harm (NNH)

Answer 16

NNH = 1 / AR

who need to be exposed for a risk factor for 1 patient to be harmed

Question 17

Selection Bias

Answer 17

Error in assigning subjects to a study group reulting in an unrepresentative sample.

Reduce bias: Randomization, correct comparison group

Question 18

Recall Bias

Answer 18

Awareness of disorder alters recall by subjects; common in retrospective studies

Reduce Bias: decrease time from exposure to follow-up

Question 19

Measurement Bias

Answer 19

Information is gathered in a way that distorts it

e.g. miscalibrated scale consistently overstates weight of subjects

reduce bias: use standardized method of data collection

Question 20

Procedure Bias

Answer 20

Subjects in different groups are not treated the same

reduce bias: blinding and use of placebo

Question 21

Observer-expectancy bias

Answer 21

Researcher’s belief in the efficacy of a treatment changes the outcome of that treatment (‘self-fulfilling prophecy’)

reduce bias: blinding and use of placebo

Question 22

Confounding bias

Answer 22

When a factor is related to both the exposure and outcome, but not on the causal pathway –> factor distorts of confuses effect of exposure on outcome

e.g. pulmonary disease more common in coal workers than general population; people who work in coal mines also smoke more frequently.

reduce bias: multiple/repeated studies; crossover studies (subjects act as own controls); matching (patients with similar characteristics in both treatment and control goup)

Question 23

Lead-time bias

Answer 23

Early detection is confused with increased survival

reduce bias: measure “back-end” survival (adjust survival according to the severity of disease at time of diagnosis)

Question 24

Mean

Answer 24

average

most affected by ouliers

Question 25

Median

Answer 25

Middle value

Question 26

Mode

Answer 26

Most common value

least affected by outliers

Question 27

Normal Distribution

Answer 27

Question 28

Type I Error (alpha)

Answer 28

Stating there is an effect or difference when non exists (null hypothesis incorrectly rejected in favor of alternative hypothesis)

alpha = probability of making a type I error.

p = judge against a preset alpha (usually <0.05)

“false positive error”

Question 29

Type II Error (beta)

Answer 29

Stating there is not an effect or difference when one exists (null hypothesis is not rejected when it is in fact false)

beta = probably of making a type II error

statistical power = 1 - beta

Increase power by: increasing sample size, expected effect size and precision of measurement

Question 30

t-test

Answer 30

“tea for 2”

Check differences between means of 2 groups

Question 31

ANOVA

Answer 31

Checks differences between means of 3 or more groups

Question 32

Chi-Square

Answer 32

“chi-tegorical”

Checks differences between 2 or more percentages (%) or proportions of categorical outcomes

(not mean, like t-test)

Question 33

Medicare

Answer 33

(medicarE for Elderly; medicaiD for Destitute)

Medicare:

Part A: hospitAl

Part B: Basic medical Bills (fees, dx testing)

Part C: (A+B) delivered by private Companies

Part D: prescription Drugs

Question 34

APGAR

Answer 34

Appearance

Pulse

Grimace

Activity

Respiration

at 1 and 5 minutes

>6 good, 4-6 assist and stimulate, <4 resuscitate