introduction to pharmacoepidemiology 1 measuring...

Introduction to pharmacoepidemiology 1 Measuring occurrence and associationThe cohort study

Morten AndersenCentre for PharmacoepidemiologyKarolinska Institutet

May 11, 2011Morten Andersen 2


Clinical pharmacology Epidemiology

Pharmaco-epidemiology

Pharmacoepidemiology

The study of the use of and the effects of drugsin large numbers of people

Brian L Strom, 1994


Pharmacoepidemiological methods

Case reports Case series, clusters Spontaneous reports Ecologic studies Specialised surveillance Case-control studies Self-controlled designs Cohort studies Randomised trials Evidence

Signal or hypothesisgeneration

Risk or effectivenessassessment


Overview

What is a cohort? Closed and open cohorts Measures of incidence Measures of prevalence Introduction to the cohort study Measures of association Time varying exposure


What is a cohort?

A population followed during a period and observed for one or more outcomes

Can be divided into closed and open/dynamic populations


The closed cohort

A group of persons defined at a certain point in time Each person is followed until an event occurs or to the end of

the observation period No entry of new individuals

Note: Cohorts can be identified both prospectively and retrospectively (historically)

Example: The Thule workers who participated in the clean-up after the B-52 crash in 1968


The closed cohort

Time

Person

1234


The closed cohort

Time

Person

1234

Outcomeevent

End of observation,censoring


The closed cohortProblems

Persons censored(observation ends because of incomplete follow-up, migration, death – if not an outcome)

Decreasing population size

Ageing of cohort


The open/dynamic cohort

A population changing over time

Persons can enter or exit during the observation period

Example: Drug users during a certain observation period


The open/dynamic cohort

Time

Person

1234


Person – time – event

Persons under observation (population at risk) Time during observation (risk time)

how long observation period(number of events)

which time axis(calendar time, time from start of study, age)

end of observation – censoring(death, migration, other outcomes)

Event according to defined criteria


Measures of occurrence, which one to choose?

Prevalence rate


Cumulative incidence proportion

Cumulative incidence proportion at time t =

number of new cases until time tnumber of persons at the start of the period

Dimensionless, range 0-1

Example: The 30-day mortality for persons admitted to hospital with MI is 20%


French-Russian war 1811-1812



Example: The cumulative incidence of dying in the French-Russian war after one year was

(422,000-10,000) / 422,000 = 0.98 = 98%

What do we usually call the cumulative incidence proportion?


French-Russian war 1811-1812



Time, years0 1 2 3


at 3 years =



0 1 2 3


at 3 years =

3/8 = 37.5%

Time, years



0 1 2 3


at 3 years =

3/8 = 37.5%

Lost to follow-up

Time, years


Incidence rate

Incidence rate =

number of new eventstime during which events are observed

Dimension time-1, e.g. per year, range 0-∞

Example: The incidence of upper GI bleeding in DK is 50 per 100,000 person years


One person year is

One person followed for one year Two persons each followed for 6 months Three persons each followed for 4 months 100 persons each followed for 3.65 days 10 persons followed for 1 month and 60 persons followed for 1

day …


Incidence rate

0 1 2 3Time, years


Incidence rate

0 1 2 3

Events Time

1 1.5

1 2.5

0 2

0 3

1 2

0 2

0 3

0 2

IR = 1 per 6 person yearsTime, years


Prevalence proportion

The proportion of a population who have the disease at a certain point in time (point prevalence, cross-sectional)


Example: The prevalence of beta-blocker use among persons with a previous MI is 30%


Prevalence proportion – Illustration

0 1 2 3

Use ofbeta-blocker

MI

Prevalence proportion at

1 year =

Time, years


Prevalence proportion – Illustration

0 1 2 3

Use ofbeta-blocker

Prevalence proportion at

1 year =

2/8 = 25%

Time, years

MI


Period prevalence

The proportion of a population who are affected by the disease during the observation period (e.g. 1-year prevalence)


Example: 4% of the population used insulin during 1993 Note: Mixes up prevalent and incident users, the result strongly

depends on length of the period


Period prevalence – Illustration

0 1 2 3

Use ofbeta-blocker

Period prevalence during the 1st year =

Time, years

MI


Period prevalence – Illustration

0 1 2 3

Use ofbeta-blocker

Period prevalence during the 1st year =

4/8 = 50%

Time, years

MI


Exercise 1Measures of occurrence Which epidemiological measures are/can be used? During the influenza epidemic in 1997, 25% of the pupils in one

Copenhagen school were absent on February 1st 15% of pregraduate medical students left the study during the

1st year During 2003, 300 new cases of breast cancer were diagnosed

among 100,000 women aged 50-54 years in DK


The cohort study

”the delineation of a group of persons who are distinguished in some specific way from the majority of the population and observation of them for long enough to allow any unusual morbidityor mortality to be recognised”

Richard Doll 1964


Cohort studies

Individuals are included based on exposure status, e.g. +/- drug use

The occurrence of outcome events among the exposed individuals is compared to the occurrence of events among the unexposed individuals

Overall aim to assess the association between exposure and outcome


The cohort study

Time, years0 1 2 3

Exposed

Unexposed


Risk ratio

Risk among exposed = 2/4 = 0.5Risk among unexposed = 1/4 = 0.25Risk ratio (“relative risk”) = 2.0Risk difference = 0.25

DiseaseExposure + - Total+ 2 2 4- 1 3 4Total 3 5 8


The cohort study

Exposed

Unexposed

0 1 2 3Time, years


Incidence rate ratio

Rate among exposed = 0.2 per yearRate among unexposed = 0.1 per yearRate ratio = 2.0Rate difference = 0.1 per year

DiseaseExposure + - Person time+ 2 2 10- 1 3 10Total 3 5 20


Time varying exposure

Time, years0 1 2 3

Use ofNSAID

Person timeExposed Unexposed

IRR = =

Non-use


Time varying exposure

0 1 2 3

Use ofNSAID

Person timeExposed Unexposed

0.5 1

0 2

1.5 1.5

0.5 1

0 1.5

1.5 1

0 1.5

0 2.5

IRR = = 3.02/42/12

Non-use

Time, years


Exercise 2Measures of associationThe incidence of hospital admission for ulcer per 1,000 person yearsadjusted for baseline demographic characteristics, co-morbidity, previousulcer and medication use

Based on Ray et al. Gastroenterology 2007;133:790-8

Person years Incidence rate

NSAIDs without ulcer prophylaxis 57,032 5.65

NSAID + proton pump inhibitor 6,227 2.57

Coxib without ulcer prophylaxis 13,962 3.38


Exercise 2Measures of association Which study design is used? Which measures of frequency and association are relevant? How much is the risk of an ulcer increased using traditional

NSAIDs compared to coxibs, both without ulcer prophylaxis? How many hospital admissions due to ulcer can be avoided

By changing from NSAID to coxib? By supplemeting NSAID with a proton pump inhibitor?


NSAIDs, coxibs and ulcer(Ray et al. Gastroenterology 2007;133:790-8)


Further considerations related to exposureand outcome Point or continuous exposure Acute or chronic effects Dose-related effect Cumulative effect Latency period Biological evidence (or plausibility) for

exposure-outcome relation Outcome includes first event only or multiple events Time required for defining ”new” incident event


Experimental versus observational research

Experimental research (randomised clinical trial) Randomisation ensures comparability of patients Study the effect of exposure isolated from other causal factors Procedures to ensure complete and valid data collection

Observational research (epidemiological study) Patient groups are fundamentally incomparable Multiple causal factors are acting simultaneously Often incomplete and imperfect data collected for other purposes


False adverse effects


Confounding – mixing of effects

Patient factors become confounders if they are associated with the exposure and also independent predictors of the outcome

Confounder

OutcomeExposure


Confounder kontrol

DESIGN Randomisation Cross-over Restriction Matching

ANALYSIS Stratification Standardisation Multivariable analysis Propensity scores Instrumental variables


Ensuring that differently treated patients arecomparableCausal experiment (rewind time, perfectly comparable)

Patient 1: Drug Change in outcome

Patient 1: Placebo No change

Clinical trial (randomize, on average comparable)


Patient 2: Placebo No change


Ensuring that differently treated patients arecomparableObservational study


Patient 2: No drug No change

How do we select patient 2 to be comparable with patient 1?• A patient that looks exactly like patient 1 with regard to

predictors of outcome (confounders, disease risk score)• A patient that looks exactly like patient 1 with regard to

choice of treatment (propensity score)


Two ways to address confounding in the analysis

Confounder

OutcomeTreatment

introduction to pharmacoepidemiology 1 measuring...

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