Definitions

Question 1

Clinical Epidemiology

Answer 1

• the basic science of EBM
• study of distribution and determinants of health related states and events in specified populations, and the application of this study to control of health problems
• using scientific methods to make predictions/improve pt outcomes
• epidemiological concepts change over time (biostatistic concepts do not)

Question 2

The 5 D’s

Answer 2

Death: bad outcome if untimely
Disease: set up symptoms, physical signs, lab abnormalities
Discomfort: symptoms such as pain, nausea, dyspnea, etc
Disability: impaired ability to go about usual activities
Dissatisfaction: emotional reaction to disease and its care

Question 3

Prevalence vs. Incidence

Answer 3

P: current cases of outcome, proportion of total cases to total pop; burden of disease (how widespread the outcome is)
I: new cases, reflects risk of getting the disease; when time is in numerator is incidence rate

Question 4

Point Prevalence vs Period Prevalence

Answer 4

Point: time period is instantaneous
Period: longer time periods (but time not in the denominator)

Question 5

Cumulative Incidence

Answer 5

proportion of group that develops disease over a given period of time

= # new cases/# people at risk of developing disease over defined time

Question 6

Incidence Rate and Incidence Density

Answer 6

IR: rate at which new disease has occurred in the population at risk per some unit time
ID: refers to IR in dynamic, changing pop in which ppl are under study and at risk for varying periods of time; number of cases over person-years

IR (ID) = # new cases/total time experienced by the pop at risk

Question 7

Systematic vs. Random Error

Answer 7

systematic: bias, compounding, within the study design, sometimes unavoidable; can differential (misclassification unevenly) or non-differential (bias toward the null)
random: occurs due to variations in people, in their responses; non-differential (ex: misclassification)

Question 8

Epidemic
Outbreak&Pandemic
(epidemic curve)

Answer 8

increase in incidence of a disease in a community or region
O: small, in limited region, P: crosses many international boundaries
(a plot of the distribution of cases over time)

Question 9

Endemic

Answer 9

the constant presence of a disease or infectious agent within a geographic area or pop

Question 10

Absolute Risk, Absolute Risk Difference

Answer 10

AR = Incidence (I)
ARD = Iexposed - Iunexposed; describes someone's increased risk for a particular disease

Question 11

Relative Risk

Answer 11

RR = Iexposed / Iunexposed

• evaluates the strength of an association btwn exposure and disease; relative to all other cases
• aka risk ratio
• value of 1 = no difference, >1 = greater risk,

Question 12

Random vs Probability Samples

Answer 12

R: every person has an equal chance of being sampled
P: every person has a known, though not necessarily equal, chance of being sampled; can weight the sample toward some low frequency groups of interest

Question 13

Relative Risk

Answer 13

ratio of incidence in unexposed group to incidence in exposed group

Question 14

Random Error vs Bias

Answer 14

• random errors likely to cancel each other out as # of measurements increases (i.e. bigger sample size); bias will not
• chance more likely to lead to type ii error; bias more likely to lad to type i

Question 15

Confounder

Answer 15

• must meet three rules:
  1) must be associated with exposure
  2) must be independently associated with outcome
  3) must not be within a causal pathway btwn the exposure and the disease
• distorts the association btwn exposure and outcome; Type I Error if distorts toward strengthening association; Type II Error if distorts toward weakening association

Question 16

Randomization

Answer 16

attempt to evenly distribute potential confounders; does not guarantee control of confounders

Question 17

Restriction

Answer 17

ex of smoking as confounder btwn alcohol consumption and lung cancer

• prevents confounding but reduces study size –> could decrease statistical significance
• cannot evaluate effect of excluded variable after restriction

Question 18

Stratification

Answer 18

• data broken down (stratified) by the potential confounder –> removes the effect of the confounder
• if confounding present: risk ratios in strata will be lower than in the unstratified data
• if risk completely due to confounding: would be no diff in risk within the strata

Question 19

Matching

Answer 19

• for each subject in exposed group, one or more subject (with or without confounder) is chosen for unexposed group
• eliminates effect of confounder at individual level
• has to be coupled with matched analysis
• practical limitation on number of confounders to be matched on
• once matched, the effect of the variable on outcomes cannot be evaluated

Question 20

Effect Modification

Answer 20

• effect of confounding factor (ex of birth order, maternal age, and DS in 3D graph)
  = “interaction”
• if stratum-specific risk ratios are DIFFERENT, its effect modification

-effect modifiers are variables that change the effect of exposure on risk of disease

Question 21

Multivariable Adjustment

Answer 21

• allows us to look at multiple confounders simultaneously

- use regression analysis; if results are close to the null, know confounders are important

Question 22

Selection Bias

Answer 22

• often in cohort studies
• selective differences btwn comparison groups that impacts the relationship btwn exposure and outcome
• often results from comparison groups NOT coming from same study base and NOT being representative of their pops

ex: “healthy worker effect”

Question 23

Self-Selection and Withdrawal Bias

Answer 23

SS: volunteers (ex: asbestos retrospective cohort study)
WB: loss to follow up, differential attrition leads to selection bias (“survivorship bias”)

both in cohort studies

Question 24

Information Bias

Answer 24

• investigators who know exposure status (ex: radiologist looking at pt who’s a smoker)
• subjects who know exposure status (may be more likely to report potential symptoms)
• remedy: BLINDING

Question 25

Sensitivity vs. Specificity

Answer 25

Sensitivity: pos when it should be pos = TP/ (TP+FN)

Question 26

Alpha vs. Beta

Answer 26

A: our willingness to be wrong - our willingness to reject the null when we shouldn’t, to make a Type I Error
- usual convention is p =.05 (so we’re 95% certain; willing to be wrong 1 in 20 times)

B: our willingness to tolerate failure - to make a Type II Error

• usual convention = .1 or .2 (so we’re more willing to make a Type II Error than Type I), but sometimes not specified
• if we want 80% power to reject null, our beta is 20%

Question 27

Power

Answer 27

• ability to detect or verify a difference that is real, to avoid a Type II Error
  P = 1 - beta
• if you reject null, then by definition, you cannot lack power (even if small sample size)
• is the sensitivity of our study

Question 28

T-Test

Answer 28

• compares the diff btwn the two means
• divided by variability in the two samples
  T = (meanA-meanB)/(varA+varB)^.5
  df = (nA-1) + (nB-1)
• if we calculate t at less than the critical value (based on alpha and df), then we fail to reject the null
• in a graph: the wider the curves, the less likely they’ll be stat sig

Question 29

Gaussian Distribution

Answer 29

“normal curve”
bell shaped, symmetrical about the mean
mean = median = mode
2/3 of observations fall within 1 SD of mean, about 95% within 2 SDs

Question 30

What are the three criteria for determining whether an observation is abnormal?

Answer 30

1. is it unusual?
2. is it associated with disease?
3. does labeling and treating do more good than harm?