Behavioral Sciences

Question 1

Cross Sectional Study ( Observational)

Answer 1

Design- Collects data from a group of people to assess frequency of disease (and related risk factors) at a particular point in time. It asks (What is happening

Measures: disease prevalence and can show risk factor association with disease, but does not estabish causality

Question 2

Case-control study (observational AND retrospective)

Answer 2

Design- comares a group of people with disease to a group without disease. Looks for prior exposure or risk factor “ Asks What happened?”

Measures: Odds Ratio (OR) ex. “patients with cOPd had a higher odds of a history of smoking than those without COPD had”

Question 3

Cohort Study( Observational and prospective or retropsective)

Answer 3

Design: compares a group with a given exposure r risk factor to a group without such exposure. Looks to see if exposure increases liklihood of a disease. Can be prospective and asks “who will develop the disease or retrospective and ask Who developed this disease (exposed vs. nonexposed

Measures: Relative Risk (RR) “Smokers had a higher risk of developing COPD than nonsmokers”

Question 4

Twin concordance study

Answer 4

Design- compares the frequency with which both monozygotic twins or dizygotic twins develop the same disease

Measures- heritablility and influence of environmental factors.

Question 5

Adoption study

Answer 5

compares siblings raised by biological vs. adoptive parents

Measures heritability and influence of environmental factors.

Question 6

Clinical trial

Answer 6

Experimental study involving humans. Compares therapeutic benefits of 2 or more treatments, or of treatment and placebo. Study quality improves when study is randomized and controlled, and double blinded. triple blind refers to the additional blinding of the researchers analyzing the data

Question 7

Drug trials phase 1

Answer 7

small number of healthy volunteers to find out “is it safe?” assesses safety and toxicity, and pharmacokinetics.

Question 8

Drug trials phase 2

Answer 8

Small number of patients with disease of interest to find out “does it work?” Asseses treatment efficacy, optimal dosing and adverse effects.

Question 9

Drug trials phase 3

Answer 9

large number of patients randomly assigned to the treatment under investigation or to the best available treatment or the placebo. Used to measure “ is it as good or better “ it compares treatment to the current standard of care.

Question 10

Drug Trials phase 4

Answer 10

post marketing surveillance trial of patients after approval. measures “ can it stay detects rare or long term adverse effects. can result in a drug being withdrawn from market.

Question 11

Sensitivity

Answer 11

proportion of all people with disease who test positive, or probability that a test detects disease when disease is present.

Value approaching 100% is disirable for RULING OUT a disease and indicates a low false negative rate. high sensitivity test used for screening in diseases with low prevalence.

TP/TP+FN

1-FN rate

SN-N-OUT - highly SeNsitive test when Negative, rules OUT disease
100% sensitivity means no false negatives. only true negatives.

Question 12

Specificity

Answer 12

proportion of all people without disease who test negative, or the probability that a test indicates non disease when disease is absent.

Value approaching 100% is desirable for ruling in disease and indicate a low false positive rate. High specificity test used for confirmation after a positive screening test.

TN/ TN+FP

1- false positive rate

SP-P- IN= highly SPecific test, when Positive rules IN disease

If 100% it means no false positives, all positives are true positives.

Question 13

Positive Predictive Value PPV

Answer 13

Proportion of positive test results what are true positive. Probability that a person actually has the disease given a positive test result.

TP/ TP+FP

varies with prevalence or pretest probability

high pretest probability = high PPV

Question 14

Negative predictive value

Answer 14

Proportion of negative test results that are true negative

Probability that person acutally is disease free given a negative test.

TN/FN+TN

NPV varies inversely with prevalence or pretest probabiilty: high pretest probability = Low NPV

Question 15

100% sensitivity cutoff value

Answer 15

there is no disease and all negatives are true negatives.

Question 16

practical compromomise between specificity and sensitivity

Answer 16

the point where sensitivity and specificity are optimal together.

Question 17

100% specificity cutoff value

Answer 17

there is only disease, all positives are true positives.

Question 18

Incidence verses prevelance

Answer 18

incidence= # of new cases in a specified time period / population at risk during the same timre period.

incidence looks at # of new cases or incidents.

Prevalence= # of existing cases / population at risk. = incidence rate x average disease

Prevalence looks at all current cases

prevalence is greater than incidence for chronic diseases.

Incidence and prevalence for common cold are very similar since disease duration is short.

Question 19

Odds Ratio(OR)

Answer 19

Typically used in case control studies. Odds that the group with the disease (cases) was exposed to a risk factor (a/c) divided by the odds that the group without the disease (Controls) was exposed (B/d)

A/C / B/d = ad/bc

Question 20

relative risk (RR)

Answer 20

typically used in COHORT studies. Risk of developing disease in the exposed group divided by risk in the unexposed group if prevalence is low, RR=OR

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

Question 21

square for quantifying risk

Answer 21

a is positive for disease and risk,
b is positive for risk and negative for disease
c is negative for risk but positive for disease
d is negative for disease and risk.

Question 22

Precision

Answer 22

the consistency and reproducibility of a test (reliability) the absence of random variation in a test. Random error- reduces precision in a test, increase precision decreases standard deviation

Question 23

Accuracy

Answer 23

the trueness of test measurements ( validity) absence of systematic error or bias in a test. Systematic erro reduces accuracy in a test.

Question 24

Selection Bias (perfroming study)

Answer 24

Def- Nonrandom assignment to participate in a study group. Most commonly a sampling bias:

Examples

• -Berkson bias- a study looking only at inpateints
• • loss to follow up- studying a disease with early mortality
• -healthy worker and volunteer biases- study populations are healthier than the general populations.

how to reduce the bias- randomization, ensure the choice of the right comparison/ reference group.

Question 25

Recall bias (perfroming study)

Answer 25

awareness of disorder alters recall by subjects common in retrospective studies

example. patients with disease recall exposure after learning of similar cases.

reduce- decrease time from exposure to follow-up

Question 26

Measurement bias (perfroming study)

Answer 26

information is gathered in a way that distorts it.

example: howthorne effect- groups who know they being studied behave differently than they would otherwise

reduce- use of placebo control groups with blinding to reduce influence of participants and researchers on experimental procedures and interpretation of outcomes.

Question 27

Procedure bias (perfroming study)

Answer 27

Subjects in different groups are not treated the same

example: patients in treatment group spend more time in highly specialized hospital units

reduce- use of placebo control groups with blinding to reduce influence of participants and researchers on experimental procedures and interpretation of outcomes.

Question 28

Observer expectancy bias (perfroming study)

Answer 28

Researchers belief in the efficacy of a treatment changes the outcome of that treatment( Pygmalion effect, self fulfilling prophecy)

example,: if the observer expects teh treatment group to show signs of recovery, then he is more likely to document positive outcomes.

Question 29

Confounding bias (interpreting results)

Answer 29

where a factor is related to both the exposure and outcome but not on the causal pathway which leads to the factor distorting or confusing the effect of exposure on outcome.

example: pulmonary disease is more common in coal workers than general population however, people who work in coal mines also smoke more frequently than the general population
reduce: multiple repeated studies, crossover studies, (subject act as their own controls ) matching (patients with similar characteristics in both treatment and control groups.

Question 30

Lead Time Bias ( interpreting the results)

Answer 30

Early detection is confused

Question 31

Standard deviation

Answer 31

how much variability exists from the mean in a set of vaules

sigma= SD, n= sample size

Question 32

Standard error of the mean

Answer 32

an estimation of how much variability exists between the sample mean and the true population mean.
sigma= SD, n= sample size

SEM= sigma/ sqrt(n)
SEM decreases as n increases.

Question 33

Bimodal distribution

Answer 33

suggest two different populations ( metabolic polymorphisms such as fast vs slow acetylators, suicide rate by age)

Question 34

positive skew distribution

Answer 34

typically mean is greater than them median which is greater than the mode. asymmetry with a longer tail on the right

Question 35

Negative skew

Answer 35

typically mean is less than the median which is less than the mode, asymmetry with longer tail on left.

Question 36

Null Hypothesis

Answer 36

Hypothesis of no difference ( there is no association between the disease and the risk factor in the population

Question 37

Alternative hypothesis

Answer 37

hypthesis of soem difference ( there is asome association between the disease and the risk factor in the population

Question 38

Outcomes of a statistical hypothesis testing- correct result

Answer 38

stating that there is an effect or difference when one exists ( null hypothesis is rejected in favor of the alternative hypothesis)
stating that there there is not an effect or difference when noen exists (null hypothesis not rejected.)

Question 39

Type 1 error (alpha)

Answer 39

stating that there is an effect or difference when none exists (null hypothesis incorrectly rejected in favor of alternative hypthesis)

Alpha is the probability of making a type 1 error

p is judged against a present alpha level of signiricance and should be lower than .05 because there is less than a 5% chance that the data will show something that is not relly there.

AKA false positive error,

alpha= you saw the difference that dd not exists

Question 40

Type 2 error (Beta)

Answer 40

stating that there is not an effect or difference when one exists ( null hypothesis is not rejected when it is in fact false.

Beta is the probability of making a type two error. Beta is related to statistcal power ( 1-Beta) which is the probability of rejecting a null hypothesis when it is false

increase power by increaseing Beta = increase sample size = increase expected effect size= precision of measurement.