Basic Epi

Question 1

How do you calculate prevalence?

Answer 1

of existing cases/# of individuals in study population at one point in time

Question 2

How do you calculate prevalence if you only have incidence rate?

Answer 2

multiple by disease duration and you get prevalence (product)

Question 3

What is a major drawback of using prevalence as disease measure? Give an example of this with a health condition/disease.

Answer 3

It only gives one snapshot in time/is cross sectional…

Essentially, this could make treatment look harmful if treatment prolongs living with a condition like HIV. Those who being treated are still alive while those who were not treated perhaps have died… there will be a higher prevalence of treated HIV cases v. untreated.

Question 4

How do you calculate cumulative incidence?

Answer 4

calculation: # of incident cases in a time period (t0 - t1)/ # of individuals at risk *at the beginning of the time period**(at t0)

Question 5

What is a synonym for cumulative incidence?

Answer 5

risk

Question 6

When an outcome is rare, what does prevalence equal?

Answer 6

prevalence odds

Question 7

What is the range of prevalence?

Answer 7

0-1

Question 8

What is the range of cumulative incidence?

Answer 8

0-1

Question 9

What are the conditions under which you would use cumulative incidence as a measure of disease?

Answer 9

Best for observing new cases over a short period of time in a population assumed to be closed and exposure what assumed to be at the same time (e..g, food-borne illness, cruises, etc.)

Question 10

What is always required for interpretation of cumulative incidence>

Answer 10

time-frame

Ex. “over the study period” (i.e., how many new cases observed during a discrete period of time)

Question 11

What are some drawbacks of using cumulative incidence as a measure of disease?

Answer 11

1. proportion not a rate… does not incorporate dimensions of recovery or exposures that change over time –> tends to 1 over time
2. requires that everyone be followed the entire time (cannot have loss to follow up, competing events, etc. otherwise you cannot determine the numerator)

Question 12

How do you calculate odds? What is this effectively comparing?

Answer 12

Odds = p/(1-p)
OR:
Odds = Pr(Y=1)/Pr(Y=0)

*effectively comparing occurrence (numerator) to non-occurrence (denominator)

Question 13

What is a major drawback of odds as a measure of disease?

Answer 13

Difficult to interpret

Question 14

high prevalence = ____ duration OR ___ incidence rate

Answer 14

long duration or high incidence rate

Question 15

how do you calculate incidence rate?

Answer 15

of new cases during a time period/total person-time accumulated during that time period **amongst those at risk**

Question 16

What does incidence rate mean in lay words and a metaphor for it?

Answer 16

measure of the “speed” at which people develop disease (“force of morbidity”)

Question 17

What is a synonym for incidence rate?

Answer 17

Hazard rate

Question 18

What does person-time account for?

Answer 18

The fact that during a time period, people can change exposure groups (e.g., vaccinated v. not) or if they have contracted a disease/condition, they are no longer “at risk”

Question 19

What is the difference between closed and open cohorts? Give an example of each in terms of Harvard students.

Answer 19

closed cohorts are defined by an event (like a threshold walking through) while open cohorts are defined by possessing a set of characteristics

EX. individuals who were Harvard PhD students (closed) admitted in 2020 v. currently enrolled Harvard PhD students (open)

Question 20

What are the main calculations used for survival/Kaplan Meier curves? How do you determine a risk set?

Answer 20

• conditional cumulative incidences at each time point when a case occurred by: # events/risk set
• risk set: # in prior risk set - # events - # censored

Question 21

How do you calculate cumulative survival proportion?

Answer 21

calculate cumulative survival proportion by multiplying ALL conditional survival proportions (1-conditional CI) up until time point of interest

Question 22

What are some reasons for censoring?

Answer 22

administrative: study ends before some people have outcome of interest

competing risk: event that removes someone from having an outcome (EX. someone dies from heart disease before getting cancer - what we’re interested in)

loss to follow up: dropped out

Question 23

What are the three types of censoring?

Answer 23

right: individuals censored at end of time interval in study
interval: individuals censored at midpoint or some point in the interval between visits or study contact points
left: people enter study with condition (e.g., some 10 year old girls already had puberty before the start of our study)

Question 24

Is incidence rate a probability?

Answer 24

NO!!! it’s a rate that incorporates time dimension, not probability…

Question 25

What is a drawback of using incidence rate?

Answer 25

person-time is not an intuitive way to interpret public health impact

Question 26

How can you calculate cumulative incidence if you have incidence rates?

Answer 26

1-e*integral of IR over each time interval

Question 27

What is a synonym for cumulative incidence ratio?

Answer 27

Risk ratio

Question 28

How do you calculate CIR/RR?

Answer 28

cumulative incidence in the treatment group/cumulative incidence in the untreated group

(Pr[Y=1|A=1]) / (Pr[Y=1|A=0])

Question 29

When outcome is rare in a CLOSED cohort, odds estimates _______.

Answer 29

cumulative incidence

Question 30

What is the implication of using CIR (RR) v. risk difference?

Answer 30

• using the ratio tells the degree of risk between unexposed/exposed (EX. those in the exposed group have X% greater risk of death compared to those unexposed)
• using difference gives an interpretation of the public health impact (EX. Smokers had 11 excess cases of lung cancer per 100 individuals compared with non-smokers.)

Question 31

What is exchangeability and why do we need it for causal inference?

Answer 31

exchangeability — assumption that the exposed and unexposed groups would have experienced the same risk of outcome had they received the same treatment level (i.e., assumed to be identical with exception of the treatment variable)

*we need it for causal inference because it is impossible to observe the same population (the exact same individuals) in one time period under exposed and unexposed conditions —> must compare exposed to group to assumed exchangeable unexposed group

Question 32

What are the 4 assumptions needed for causal inference/

Answer 32

1. exchangeability
2. consistency/SUTVA
3. positivity
4. well-defined interventions & outcomes

Question 33

Define consistency with respect to counterfactual theory.

Answer 33

consistency — the assumption that a participant’s counterfactual outcome under their observed exposure is equal to their observed outcome

Question 34

What does SUTVA stand for? What is the assumption?

Answer 34

stable unit treatment value assumption

assumption that there is no interference or heterogeneity in treatment

Question 35

What is interference with regards to SUTVA and what are some examples?

Answer 35

condition where treatment status of a participant may affect potential outcomes of other units

Ex. spillover effects, herd immunity with vaccination as treatment

Question 36

What is the average causal null hypothesis?

Answer 36

That the probability of the outcome would be the same if the population were exposed v. if they were not exposed.

Question 37

What is the difference between the average causal null and sharp causal null? What is an example?

Answer 37

sharp causal null –> counterfactual outcomes under exposed v. unexposed conditions would be the same for ALL participants

average causal null –> *the probability* of the outcome would be the same for the population was exposed v. unexposed but individual counterfactual outcomes under exposed v. unexposed conditions could vary

Ex. if people given BP medication…
sharp causal null–> everyone’s BP having been given the medication = BP having not been given the med

average causal null –> average of everyone’s BP having been given the medication = BP having not been given the medication BUT for some, BP without medication may have been higher while for others, BP without meds would have been lower…

Question 38

What is the difference between association and causation? What is needed to claim association = causation?

Answer 38

association –> difference in risk amongst two disjoint subsets of population

causation –> difference in risk in the same population under 2 exposure values

**need exchangeability

Question 39

Why does ATT (average txt effect among the treated) need weaker assumptions than other causal inference?

Answer 39

Because you are making comparisons about the observed outcome in a treated group compared to their baseline untreated outcome.