Epidemiology: 7) Cohort Analysis - Disease Occurence

Front 1

What/Why is a measure of disease occurence for?

Back 1

Quantify the likelihood of disease development in a single group.
1)Risk
2)Odds
3)Incidence rates

Front 2

4 purpose of cohort analysis

Back 2

1)Measure of the occurence of D
2)Measure the effect of E on the occurrence of D
3)Control for confounding by other differences between E+ and E-
4)Test/Identify effect modification

Front 3

Risk

Back 3

The probability that a person who initially does not have the disease will develop it by the end of fixed or defined period

Front 4

3 major components of risk

Back 4

1)Probability (between 0 and 1)
2)simple: calculate proportion who develop D
3)Can use life-table method

Front 5

Synonyms of risk

Back 5

Cumulative incidence
Cumulative incidence rate
Cumulative incidence proportion

Front 6

Odds

Back 6

Odds = p/(1-p)
p=risk

Front 7

Incidence Rate

Back 7

likelihood of occurerence for binary outcomes that quantifies the instantaneous rate of disease development.

Front 8

3 What NOT to do for variable follow-up

Back 8

1)Ignore and estimate risk as proportion
2)Take shortest period of follow-up
3)Never allow - always do fixed period studies

Front 9

What to with variable follow-up

Back 9

Estimate incidence rate and calculate risk if strict assumptions hold true

Front 10

Synonyms for Incidence

Back 10

1)Incidence rate
2)Incidence density
3)Hazard function
4)Force morbidity/mortality

Front 11

4 characteristics of Incidence rate

Back 11

1)Varies between 0 and inf
2)[events/time]
3)Magnitude of rate depends upon time unit
4)often not constant over time

Front 12

Can use person-time methods with 3 assumptions

Back 12

1)Incidence is constant over time
2)Incidence is homogenous among persons in the population
3)Loss to follow-up or censoring is not associated with D

Front 13

Incidence eqn if assumptions hold

Back 13

I=(# of events)/(total person-time)

Front 14

2 equations for estimating risk based on incidence

Back 14

I must be constant
1)Risk(t) = 1-exp(-I*t)
2)If I*t < 0.1: Risk(t)=I*t

Front 15

For non-constant I

Back 15

Use survival analysis (life tables)