Usmle Step 1 Fa Behavioral Sciences-Epidemiology Biostats

Front 1

Case Control:
(1) Study type
(2) Design
(3) Measures

Back 1

(1) Observational and retrospective
(2) Compares a group of people w/disease to a group without. Asks, “What happened?”
(3) Odds ratio

Front 2

Cohort:
(1) Study type
(2) Design
(3) measures

Back 2

(1) Observational and prospective
(2) Compares a group with a given risk factor to a group without to assess whether the risk factor increases likelihood of disease. Asks, “What will happen?”
(3) Relative risk

Front 3

Cross Sectional:
(1) Type of study
(2) Design
(3) Measures

Back 3

(1) Observational
(2) Collects data from a group of people to assess frequency of disease (and related risk factors) at a particular point in time. Asks “What is happening?”
(3) Disease prevalence. Can show risk factor association with disease but does not establish causality.

Front 4

Twin concordance study

Back 4

Compares the frequency with which both monozygotic twins or both dizygotic twins develop a disease. Measures heritability.

Front 5

Adoption study.

Back 5

Compares siblings raised by biologic vs adoptive parents. Measures heritability and influence of environmental factors.

Front 6

Define clinical trial.

Back 6

Experimental study involving humans. Compres therapeutic benefits of 2 or more treatments, or of treatment and placebo. Highest quality study when randomized controlled, and double blinded.

Front 7

Describe the study sample and purpose of each phase of clinical trials.

Back 7

(A) Phase I: Small number of patients, usually healthy; assess safety, toxicity, and pharmacokinetics.
(B) Phase II: Small number of patients with disease of interest; assesses treatment efficacy, optimal dosing, and adverse effects.
(C) Phase III: Large numbers of patients randomly assigned either to treatment under investigation or to the best available treatment (or placebo); compares the new treatment and the current standard of care. More convincing if double blinded.

Front 8

Meta analysis.

Back 8

Pools data from several studies to come to an overall conclusions. Achieves greater statistical power and integrates results of similar studies. Highest echelon of clinical evidence. May be limited by quality of individual studies or bias in study selection.

Front 9

Sensitivity

Back 9

Proportion of all people with disease who test positive. Value approaching 1 is desirable for ruling out disease and indicates low false negative rate. Used for screening diseases with low prevalence.
Sensitivity=TP/(TP+FN)

Front 10

Specificity

Back 10

Proportion of all people without disease who test negative.
Value approaching 1 is desirable for ruling IN disease and indicates a lot false positive rate.
Used as a confirmatory test after a positive screening test.
Specificity=TN/(TN+FP)

Front 11

Positive predictive value

Back 11

Proportion of positive tests results that are true positive.
Probability that person actually has the disease given a positive test result.
PPV=TP/(TP+FP)
NOTE: If prevalence is low, even tests with high specificity or high sensitivity will have low PPV’s)

Front 12

Negative predictive value

Back 12

Proportion of negative test results that are true negative.
Probability that person actually is disease free given a negative test result.
NPV=TN/(FN+TN)

Front 13

Prevalence

Back 13

Point prevalence=(total cases in population in given time)/(total population at risk at a given time)
Prevalance is approximately incidence x disease duration.
Prevalence is>incidence for chronic diseases.
Prevalance=incidence or acute disease.

Front 14

Incidence

Back 14

Incidence=(new cases in population over given time)/(total population at risk during that time)
When calculating incidence don’t forget that people previously positive for a disease are no longer considered at risk.

Front 15

Odds ratio for case control studies

Back 15

OR=(a/b)/(c/d)=ad/bc
Odds of having a disease in exposed group divided by odds of having disease in unexposed group. Approximates relative risk if prevalence of disease is not too high.

Front 16

Relative risk (RR) for cohort studies

Back 16

RR=[a/(a+b)]/[c/(c+d)]
Relative probability of getting a disease in the exposed group compared to the unexposed group.
Calculated as percent with disease in exposed group divided by percent with disease in unexposed group.

Front 17

Attributable risk

Back 17

AR=[a/(a+b)]-[c/(c+d)]
The difference in risk between exposed and unexposed groups, or the proportion of disease occurrences that are attributable to the exposure.

Front 18

Precision

Back 18

1. The consistency and reproducibility of a test (reliability)
2. The absence of random variation in the test.
Random error reduces precision in a test.

Front 19

Accuracy

Back 19

Accuracy is the trueness of test measurements (validity).
Systematic error reduces accuracy in test.

Front 20

Bias

Back 20

Occurs when 1 outcome is systematically favored over another. Systematic errors.

Front 21

Selection bias

Back 21

Nonrandom assignment to study group

Front 22

Recall bias

Back 22

Knowledge of presence of disorder alters recall by subjects

Front 23

Sampling bias

Back 23

Subjects are not representative relative to general population; therefore, results are not generalizable

Front 24

Late-look bias

Back 24

Information fathered at an inappropriate time

Front 25

Procedure bias

Back 25

Subjects in different gruops are not treated the same-e.g. more attention is paid to treatment group, stimulating greater complicance

Front 26

Confounding bias

Back 26

Occurs with 2 closely associated factors; the effect of 1 factor distorts or confuses the effect of the other

Front 27

Lead time bias

Back 27

Early detection confused with increased survival.

Front 28

Pygmalion effect

Back 28

Occurs when a researcher’s belief in the efficacy of a treatment changes the outcome of that treatment.

Front 29

Hawthorne effect

Back 29

Occurs when the group being studied changes its behavior to meet the expectations of the researcher.

Front 30

Ways to reduce bias:

Back 30

1. blind studies (double blind better)
2. placebo responses
3. crossover studies (each subject acts as own control)
4. randomization

Front 31

Describe positive skew statistical distribution

Back 31

Asymmetry with tail on right. Mean>median>mode

Front 32

Describe negative skew statistical distribution

Back 32

Asymmetry with tail on left.
Mean<median<mode

Front 33

Null hypothesis

Back 33

Hypothesis of no difference

Front 34

Alternative hypothesis

Back 34

Hypothesis that there is some difference

Front 35

Type I error (alpha)

Back 35

“False positive error”
Stating that there is an effect or difference when non exists (to mistakenly accept the experimental hypothesis and reject the null hypothesis).
p= probability of making a type I error.
P is judged against alpha, a preset level of significance (usually <0.05).

Front 36

Type II error (beta)

Back 36

“False negative error”
Stating that there is not an effect or difference when one exists (to fail to reject the null hypothesis when in fact it is false). Beta is the probability of making a type II error.

Front 37

Power

Back 37

(1-beta)
Probability of rejecting null hypothesis when it is in fact false, or the likelihood of finding a difference if one in fact exists. It depends on:
1. Total number of end points experienced by population.
2. Difference in compliance between treatment groups (differences in the mean values between groups).
3. size of expected effect.
If you increase the sample size you increase the power.

Front 38

Standard deviation vs standard error

Back 38

n=sample size
x=standard deviation
SEM=standard error of the mean.
SEM=x/square root of n
SEM<x, and SEM decreases as n increases.

Front 39

Confidence interval

Back 39

Range of values in which a specified probability of the means of repeated samples would be expected to fall.

Front 40

T test

Back 40

Checks difference b/w means of 2 groups

Front 41

ANOVA

Back 41

Checks difference between means of 3+ groups

Front 42

Chi squared

Back 42

Checks difference between 2 or more percentages or proportions of categorical outcomes (not mean values)

Front 43

Correlation coefficient

Back 43

R is always b/w -1 and 1. The closer the absolute value of r is to 1, the stronger the correlation b/w two variables.

Front 44

Primary, secondary, and tertiary prevention

Back 44

Primary-prevent disease occurrence (ex. Vaccination)
Secondary-early detection of disease (screening)
Tertiary-reduce disability from disease

Front 45

Reportable diseases

Back 45

Hep Hep Hep Hooray, the SSSMMART Chick is Gone!
HepA
HepB
HepC
HIV
Salmonella
Shigella
Syphilis
Measles
Mumps
AIDS
Rubella
Tuberculosis
Chickenpox
Gonorrhea

Front 46

Leading causes of death in US by age:
(1)Infants
(2) 1-14
(3) 15-24
(4) 25-64
(5) 65+

Back 46

(1)Infants: congenital anomalies, SGA/LBW, SIDS, maternal complications of pregnancy, RDS
(2) 1-14: Injuries, cancer, congenital anomalies, homicide, heart disease
(3) 15-24: Injuries, homicide, suicide, cancer, heart disease
(4) 25-64: Cancer, heart disease, injuries, suicide, stroke.
(5) 65+: heart disease, cancer, stroke, COPD, pneumonia, influenza